FEDERAL UNIVERSITY OF MINAS GERAIS Aerospace Engineering ... · manufacturer EMBRAER S.A., and the ATR-72 600 turboprop aircraft, designed by the ATR Company. Both airplanes are used

FEDERAL UNIVERSITY OF MINAS GERAISMECHANICAL ENGINEERING DEPARTMENTAerospace Engineering Undergraduation Program

GEOVANA NEVES FELIX SILVA

OPTIMIZATION AND COMPARISON OF TURBOPROP

AND TURBOFAN AIRCRAFT

UNDERGRADUATION SENIOR THESIS

BELO HORIZONTE2018

GEOVANA NEVES FELIX SILVA

OPTIMIZATION AND COMPARISON OF TURBOPROP

AND TURBOFAN AIRCRAFT

Undergraduation Senior Thesis presented to the AerospaceEngineering Undergraduation Program of the FederalUniversity of Minas Gerais, as a partial requirement forobtaining the title of Bachelor in Aerospace Engineering.

Advisor Ricardo Luiz Utsch de Freitas PintoDEMEC - UFMG

Co-Advisor Tarik Hadura OrraEMBRAER S.A.

BELO HORIZONTE2018

I dedicate this work to my parents and sisters.

ACKNOWLEDGMENT

First, I would like to thank my parents, Baldonato and Jaquelina, and sisters,

Fernanda, Luana and Daniela, that always supported and encouraged me.

I am also grateful for my best friend Humberto Lemos, who helped to revise my

text since the first written page. Moreover, I would like to thank my friend Fernanda

Fenelon, who also revised my text and now is looking forward to continue the studies I

started in my undergraduation senior thesis. And I am grateful for Chiara Titton, she

supported me with her friendship even though I was kilometers away from her.

I would like to acknowledge the excellent support I received from my co-advisor

from Embraer S.A., Tarik Orra. He taught me during months and I am grateful for his

patience and interest in my work. Also, I would like to thank the engineers from Embraer

S.A., Ana Cuco and Alexandre Antunes, who revised my work and provided essential

feedback. In fact, I received an important support at Embraer S.A. that was crucial for

the success of this academic work, and for that I am thankful.

I also would like to thank my advisor from UFMG, Ricardo Luiz Utsch de Freitas

Pinto, who guided me during the year. The UFMG provided all the support necessary for

my education as aerospace engineer and I am grateful for all professors who were part of my

academic experience. In special, I would like to thank the Centro de Estudo Aeronauticos

(CEA) at UFMG since all the people there, professors and students, contributed for my

learning during the undergraduation program.

I also want to acknowledge the SAE Aerodesign Brazil Competition which was a

fundamental part of my academic life and formation as aerospace engineer. The interest

for aircraft design and optimization was encouraged during my years as part of the UFMG

team, Uai, So! Fly!!!, in the Aerodesign Competition and I am grateful for all the learning

experience provided by the technical committee.

Finally, I would like to thank the Fundacao Universitaria Mendes Pimentel

(FUMP) for the financial assistance provided since my first year at UFMG. The scholarship

I received allowed me to focus on my studies and I admire the projects done at FUMP in

order to fund students.

A problem well-stated is a problem half-solved.(Albert Einstein)

ABSTRACT

NEVES, Geovana. Optimization and Comparison of Turboprop and Turbofan Aircraft.2018. 98 pages. Undergraduation Senior Thesis – Aerospace Engineering UndergraduationProgram, Federal University of Minas Gerais. Belo Horizonte, 2018.

According to the International Air Transport Association (IATA), it is expected a regionalgrowth in the aviation market for the next years. In North America, the increase in numberof passengers will be 452 million by 2036 compared to 2016 considering only the regionalaviation, representing 37% of the total number of passengers expected. Several aircraft weredesigned to accomplish a typical mission in this specific market and the current airplanesare mainly turbofan and turboprop powered. Given this scenario, a discussion has emergedfor the next generation of aircraft and the question to be answered is: “Turboprop orTurbofan?”. The present work aims at comparing optimized geometries for a turboprop anda turbofan in order to evaluate what configuration best suits the regional aviation marketconsidering as criteria fuel consumption, specific range and block time. The motivation ofthis study is to understand the differences between the aircraft that lead one to stand outover the other given a technical criterion. The discussion and conclusion of the analysisare based on the optimal results. Although the number of passengers considered is thesame, the flight qualities differs considerably and the critical performance requirements foreach aircraft may also be different. The open-source software SUAVE is used for modelingand optimization. The reference airplanes are the E170 Jet, designed by the brazilianmanufacturer EMBRAER S.A., and the ATR-72 600 turboprop aircraft, designed by theATR Company. Both airplanes are used to calibrate the aircraft model as well as a baselinegeometry.

Keywords: aircraft design, regional aviation, optimization, turboprop, turbofan

RESUMO

NEVES, Geovana. Otimizacao e Avaliacao Comparativa de Aeronaves Turboelice e Turbo-fan. 2018. 98 f. Trabalho de Conclusao de Curso – Graduacao em Engenharia Aeroespacial,Universidade Federal de Minas Gerais. Belo Horizonte, 2018.

De acordo com a Associacao Internacional de Transporte (IATA), e esperado um cresci-mento na aviacao comercial nos proximos anos. Na America do Norte, o aumento nonumero de passageiros sera de 452 milhoes no ano de 2036 em relacao a 2016 considerandoapenas a aviacao regional, o que representa 37% do total de passageiros previsto. Diversasaeronaves foram projetadas para cumprir com a missao tıpica nesse mercado especıficoe as atuais aeronaves sao principalmente turboelice e turbofan. Dado este cenario, umadiscussao surgiu a respeito da nova geracao de aeronaves e a principal duvida a ser respon-dida e: “Turboelice ou Turbofan?”. Este trabalho academico tem como objetivo compararaeronaves otimizadas turboelice e turbofan para 70 passageiros em termos de consumode combustıvel, alcance especıfico e tempo de bloco. A motivacao desta investigacao eentender as diferencas entre as aeronave que levam uma a se sobressair em relacao a outradado um criterio tecnico. A discussao e resultados da analise sao baseados nos resultadosotimizados para cada aeronave. Apesar de o numero de passageiros ser o mesmo, as carac-terısticas de voo de cada aeronave diferem consideravelmente e os requisitos dimensionantesrelacionados a desempenho tambem podem ser distintos. O software de codigo abertoSUAVE foi utilizado para modelagem e otimizacao. Os avioes de referencia sao o jato E170da fabricante brasileira EMBRAER S.A. e o turboelice ATR 72-600 da fabricante ATR.Ambas as aeronaves sao utilizadas para calibracao do software bem como geometrias dereferencia.

Palavras-chave: projeto de aeronaves. aviacao regional, otimizacao, turboelice, turbofan

LIST OF FIGURES

Figure 1 – Typical Mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Figure 2 – Important segments of the Take-off run. . . . . . . . . . . . . . . . . . 10

Figure 3 – Important airspeed during the Take-off run. . . . . . . . . . . . . . . . 11

Figure 4 – General Free Body Diagram for ground roll. . . . . . . . . . . . . . . . 11

Figure 5 – Second Segment Climb . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 6 – Free Body Diagram considering Climb Segment. . . . . . . . . . . . . . 14

Figure 7 – Free Body Diagram considering Cruise Segment. . . . . . . . . . . . . . 16

Figure 8 – Payload Range Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Figure 9 – Free Body Diagram - Descent Segment. . . . . . . . . . . . . . . . . . . 19

Figure 10 – Import segments of the landing run. . . . . . . . . . . . . . . . . . . . 20

Figure 11 – Turbofan components. . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 12 – Turboprop components. . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 13 – Efficiency Comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 14 – Velocity and Altitude Comparison . . . . . . . . . . . . . . . . . . . . . 30

Figure 15 – Turbofan Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 16 – Example of public data containing weight information, E170 jet . . . . 35

Figure 17 – Example of the impact in the Payload Range Diagram due to errors in CD 37

Figure 18 – Example of Pareto Plot - BOW vs Block fuel . . . . . . . . . . . . . . 43

Figure 19 – E170 AR Payload Range Diagram, FL350, ISA +0°C . . . . . . . . . . 47

Figure 20 – E170 TOFL, ISA +0°C . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Figure 21 – E170 TOFL, ISA +15°C . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Figure 22 – E170 LFL, ISA +0°C . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 23 – Design Mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Figure 24 – DoE results for turbofan aircraft . . . . . . . . . . . . . . . . . . . . . 55

Figure 25 – Carpet plot: Aspect ratio vs. Wing Area . . . . . . . . . . . . . . . . . 56

Figure 26 – Carpet plot: Aspect ratio vs. Sweep Angle . . . . . . . . . . . . . . . . 57

Figure 27 – Carpet plot: Thickness to chord ratio vs. Sweep Angle . . . . . . . . . 57

Figure 28 – Pareto Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Figure 29 – Optimized geometries for β equal 1, 0.5 and 0. . . . . . . . . . . . . . . 60

Figure 30 – ATR 72-600 Payload Range Diagram, FL210, ISA +0°C . . . . . . . . 64

Figure 31 – ATR 72-600 (SUAVE) vs ATR 72-500 - TOFL, ISA +0°C . . . . . . . 65

Figure 32 – Design Mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Figure 33 – DoE results for turboprop aircraft . . . . . . . . . . . . . . . . . . . . . 70

Figure 34 – Carpet plot: Aspect Ratio vs. Wing Area . . . . . . . . . . . . . . . . . 71

Figure 35 – Carpet plot: Aspect Ratio vs. Thickness to Chord Ratio . . . . . . . . 71

Figure 36 – Carpet plot: Thickness to Chord Ratio vs. Wing Area . . . . . . . . . . 72

Figure 37 – Pareto Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 38 – Optimized geometries for β equal 1, 0.5 and 0. . . . . . . . . . . . . . . 75

Figure 39 – Payload Range Diagram Comparison . . . . . . . . . . . . . . . . . . . 82

Figure 40 – Block Fuel Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Figure 41 – Embraer E170 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Figure 42 – Blueprint of Embraer E170 . . . . . . . . . . . . . . . . . . . . . . . . . 93

Figure 43 – ATR 72-600 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Figure 44 – Blueprint of ATR 72 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

LIST OF TABLES

Table 1 – Major Competitors in the US Market . . . . . . . . . . . . . . . . . . . 1

Table 2 – Typical Mission Segments . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Table 3 – Coefficient of Equation 2.23 . . . . . . . . . . . . . . . . . . . . . . . 13

Table 4 – Cruise Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Table 5 – Coefficient of Equation 2.52 . . . . . . . . . . . . . . . . . . . . . . . 21

Table 6 – Methodology structure based on a MDO process . . . . . . . . . . . . . 33

Table 7 – Aerodynamic parameters and the most impacted performance results . . 36

Table 8 – Constraints considered for the aircraft optimization . . . . . . . . . . . . 40

Table 9 – Matrix of Experiments - Parameter variation . . . . . . . . . . . . . . . 41

Table 10 – E170 - Empty Weight Breakdown . . . . . . . . . . . . . . . . . . . . . 46

Table 11 – E170 AR Payload Range Diagram, FL350, ISA +0°C . . . . . . . . . . . 47

Table 12 – E170 - WAT estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Table 13 – E170 - TOFL estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Table 14 – E170 - LFL estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Table 15 – E170 - CLmax values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Table 16 – E170 - Aerodynamic estimations for drag coefficients . . . . . . . . . . 51

Table 17 – Design Mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Table 18 – DoE results for turbofan aircraft . . . . . . . . . . . . . . . . . . . . . . 54

Table 19 – Turbofan design variable bounds . . . . . . . . . . . . . . . . . . . . . . 59

Table 20 – Optimized geometries for β equal 1, 0.5 and 0. . . . . . . . . . . . . . . 61

Table 21 – ATR 72-600 - Empty Weight Breakdown . . . . . . . . . . . . . . . . . 63

Table 22 – ATR 72-600 Payload Range Diagram, FL210, ISA +0°C . . . . . . . . . 64

Table 23 – ATR 72-600 - TOFL estimation . . . . . . . . . . . . . . . . . . . . . . 65

Table 24 – ATR 72-600 - Second Segment Climb Gradient γ estimation . . . . . . . 66

Table 25 – ATR 72-600 - LFL estimation . . . . . . . . . . . . . . . . . . . . . . . 66

Table 26 – ATR 72-600 - CLmax values . . . . . . . . . . . . . . . . . . . . . . . . . 66

Table 27 – ATR 72-600 - Aerodynamic estimations for drag coefficients . . . . . . 66

Table 28 – Design Mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Table 29 – DoE results for turboprop aircraft . . . . . . . . . . . . . . . . . . . . . 69

Table 30 – Turboprop design variable bounds . . . . . . . . . . . . . . . . . . . . . 73

Table 31 – Optimized geometries for β equal 1, 0.5 and 0. . . . . . . . . . . . . . . 76

Table 32 – Mission parameters comparison . . . . . . . . . . . . . . . . . . . . . . . 79

Table 33 – Time to climb to cruise altitude, MTOW - Comparison . . . . . . . . . 80

Table 34 – BOW comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Table 35 – MTOW comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Table 36 – TSFC comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Table 37 – True airspeed and TSFC ratio comparison . . . . . . . . . . . . . . . . . 84

Table 38 – L/D comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Table 39 – Specific range comparison - E170 versus ATR 72-600 . . . . . . . . . . . 85

Table 40 – Block fuel for turbofan airplanes . . . . . . . . . . . . . . . . . . . . . . 86

Table 41 – Block fuel for turboprop airplanes . . . . . . . . . . . . . . . . . . . . . 86

Table 42 – Block fuel comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Table 43 – Block time comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Table 44 – E170 AR - Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Table 45 – E170 AR - Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Table 46 – ATR 72-600 - Weights (Basic Version) . . . . . . . . . . . . . . . . . . . 94

Table 47 – ATR 72-600 - Performance . . . . . . . . . . . . . . . . . . . . . . . . . 94

LIST OF ABBREVIATIONS AND ACRONYMS

ATR Avions de Transport Regional

BOW Basic Operating Weight

CAD Computer-Aided Design

CFD Computational Fluid Dynamics

DoE Design of Experiments

FL Flight Level

HH Hot and High

IATA International Air Transport Association

ISA International Standard Atmosphere

LFL Landing Field Length

MDO Multidisciplinary Design Optimization

MZFW Maximum Zero Fuel Weight

MTOW Maximum Take-off Weight

PAX Passengers

ROC Rate of Climb

ROD Rate of Descent

SL Sea Level

SLSQP Sequential Least Squares Programming

SSCG Second Segment Climb Gradient

TO Take-off

TOFL Take-off Field Length

TOW Take-off Weight

TSFC Thrust Specific Fuel Consumption

US United States of America

WAT Weight Altitude and Temperature

LIST OF SYMBOLS

AR Wing Aspect Ratio

a Speed of Sound

b Wing span

CD Drag coefficient

CDPParasite drag coefficient

CD0 Drag coefficient term independent of the lift coefficient

CD LiftInduced

Induced drag coefficient

CDCompres-sibility

Compressibility drag coefficient

Cf Skin friction coefficient

Cfac Wing calibration form factor set to 1.1 as default

Cfus Factor normally set to 2.3 used for fuselage form factor computation

CL Lift Coefficient

CLmax Maximum Lift Coefficient

ct Thrust specific fuel consumption in SI units

cBHP Specific fuel consumption in SI units

D Drag

dmaxrefIncrement in CLmax for landing flap position.

einviscid Span-efficiency factor

g Acceleration due to gravity

hf Fuselage Height

K Scaling Factor used in the induced drag computation determined from

flight test data

kf Form Factor

kfuselage Form Factor for fuselage

kwing Form Factor for wing

Kc Flap chord correction factor

Km Flap motion correction factor

Ksw Sweep correction factor

L Lift

L/D Aerodynamic Efficiency

L/D secondsegment

Aerodynamic Efficiency at the second segment in the take-off path

lf,e Effective fuselage length - fuselage length minus the wing chord root

divided by two.

M Mach Number

m engine constant which depends on the engine design and it is usually

near 1

Nult Ultimate design load factor for the aircraft

Nseat Number of Seats

P Power

Pavailable Power Available

Prequired Power Required

sa Slat Angle

Sref Wing Reference Area

S, Sw Wing Platform Area

SHT Horizontal tail area

SV T Vertical Tail area

SFC Specific Fuel Consumption

T Thrust

TSLS Sea-level static thrust

t/c Thickness-to-chord ratio of the wing

(t/c)avg Average wing thickness to chord ratio

V Airspeed

VS Stall speed

VMC Minimum Control Speed with the critical engine inoperative

V1 Decision Speed on the take-off run

VR Rotation speed on take-off condition

VMU Minimum unstick speed

VLOF Lift-off speed

V2 Take-off safety speed

VEF Speed at which the engine failure occurs

Vapp Approach speed

W Weight

Wwing Wing structural weight

WHT Horizontal Tail structural weight

WvT Vertical Tail and Elevator structural weight

Ww,p Weight of the wing-mounted engines, nacelles and pylons

Wf Fuselage structural weight

Wfurn Furnishings Weight

WLG Landing Gear Weight

Wpropulsion Propulsion system Weight

Wp,dry Dry weight of the engine

Λ Wing Sweep Angle

Λc/4 Wing sweep angle at 1/4 chord line

ΛHTc/4Horizontal Tail sweep angle at 1/4 chord line

ΛV Tc/4Vertical Tail sweep angle at 1/4 chord line

λ Wing taper ratio

δumax Used in the fuselage parasite drag calculation - maximum velocity

increase on an ellipsoid of revolution

µ Ground friction coefficient

γ Climb gradient

ωfuel Fuel Weight Flow

ηP Propeller Efficiency

∆Pf Maximum differential pressure of the fuselage

ρ - Air density for a condition of interest

ρ0 - Air density for sea level condition

CONTENTS

1 – Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 – Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Aircraft Design Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Conceptual Design Analyses . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Aerodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1.1 Lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1.2 Drag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.2 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.2.1 Take-off . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.2.2 Climb . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.2.3 Cruise . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2.2.4 Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.2.5 Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.2.2.6 Landing . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.2.7 Fuel Consumption . . . . . . . . . . . . . . . . . . . . . . 21

2.2.3 Weight Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2.4 Propulsion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.4.1 The Tradeoff between Thrust and Efficiency . . . . . . . 24

2.2.4.2 Turbofan Engine . . . . . . . . . . . . . . . . . . . . . . 25

2.2.4.2.1 Thrust . . . . . . . . . . . . . . . . . . . . . . . 26

2.2.4.2.2 Thrust Specific Fuel Consumption . . . . . . . 26

2.2.4.3 Turboprop Engine . . . . . . . . . . . . . . . . . . . . . 27

2.2.4.3.1 Power . . . . . . . . . . . . . . . . . . . . . . . 28

2.2.4.3.2 Specific Fuel Consumption . . . . . . . . . . . . 28

2.2.4.4 Turbofan and Turboprop Comparison . . . . . . . . . . 29

2.2.4.5 SUAVE Model: Energy Network Method . . . . . . . . . 30

2.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.3.1 Multidisciplinary Optimization in the Aircraft Design - MDO . . 32

3 – Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.1 Aircraft Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.1.1 Weight Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.1.2 Aerodynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.1.2.1 Drag Model Evaluation . . . . . . . . . . . . . . . . . . 36

3.1.2.2 CLmax Model Evaluation . . . . . . . . . . . . . . . . . . 38

3.1.2.3 L/D Model Evaluation . . . . . . . . . . . . . . . . . . . 38

3.1.3 Engine Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2.1 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.2.2 Design Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.2.3 Pareto Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4 – Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1 Turbofan Aircraft Results . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1.1 Aircraft Model and Calibration . . . . . . . . . . . . . . . . . . . 46

4.1.2 DoE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.1.3 Pareto Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.1.4 Optimized Geometries . . . . . . . . . . . . . . . . . . . . . . . . 60

4.2 Turboprop Aircraft Results . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2.1 Aircraft Model and Calibration . . . . . . . . . . . . . . . . . . . 63

4.2.2 DoE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.2.3 Pareto Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.2.4 Optimized Geometries . . . . . . . . . . . . . . . . . . . . . . . . 75

4.3 Turbofan and Turboprop Comparison . . . . . . . . . . . . . . . . . . . . 78

4.3.1 Mission Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.3.2 Payload Range Diagram . . . . . . . . . . . . . . . . . . . . . . . . 81

4.3.3 Specific Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.3.4 Block Fuel and Block time . . . . . . . . . . . . . . . . . . . . . . 85

5 – Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6 – Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

A–Reference Airplanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

A.1 Turbofan Aircraft: E170 AR . . . . . . . . . . . . . . . . . . . . . . . . . 92

A.2 Turboprop Aircraft: ATR-72 600 . . . . . . . . . . . . . . . . . . . . . . 94

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

1

1 Introduction

According to IATA [1], the increase in number of passengers in North America

will be 452 million by 2036 compared to 2016 considering only the regional aviation,

representing 37% of the total number expected. In fact, the regional aviation has an

important role in an airline’s business planning since it offers a balance between capacity

and demand in order to increase the possible profit. This is linked to the expected growth

in the regional aviation market, which has further increased the competition among airlines

interested in capturing more potential passengers. In this context, aircraft manufactures

have the opportunity to offer a product capable of satisfying the segment needs, such as

reduced costs and aircraft availability. Several airplanes were designed to accomplish a

typical mission in this specific market and the current aircraft are mainly turbofan and

turboprop powered. Table 1 presents some of the major competitors in the US regional

aviation regarding aircraft manufactures.

Table 1 – Major Competitors in the US Market

Manufacturer Aircraft Engine PAX Fleet

Bombardier CRJ200 Turbofan 50 385Embraer ERJ145 Turbofan 48 233Embraer E170 Turbofan 72 179Bombadier Dash 8 Q400 Turboprop 82 53ATR ATR 72 Turboprop 50 26ATR ATR 42 Turboprop 70 26

Source: AEROWEB, 2016 [2]

These aircraft perform missions of short and medium range, which varies according

to the powerplant used. Given this scenario, a discussion has emerged for the next generation

of aircraft and the main question to be answered is: “Turboprop or Turbofan?”

The present work aims at comparing optimized geometries for turboprop and

turbofan airplanes in order to evaluate what configuration best suits the regional aviation

market considering as criteria fuel consumption, specific range and block time. The goal is to

provide the technical information necessary to answer the previous question quantitatively.

The study involves the application of fundamental aircraft design concepts and optimization

techniques in order to provide an impartial and reliable conclusion.

The motivation of this analysis is to understand the differences between the

aircraft that lead one to stand out over the other given a technical criterion. Although

the number of passengers is the same, the flight qualities differs considerably and the

critical performance requirements for each aircraft may also be different. The open-source

Chapter 1. Introduction 2

software SUAVE [3] is adopted to model and optimize the two concepts here addressed to

be studied. The reference airplanes are the E170 jet and the ATR 72-600 turboprop. They

are used to calibrate the aircraft model as well as a baseline geometry. The discussion and

conclusion of the analysis are based in the optimal results. With the analysis rigor defined,

the next steps are to clearly describe the purpose of the present work, its scope and how it

will be accomplished.

Goal

The present work aims at comparing optimized geometries for turbofan and turboprop

aircraft in order to evaluate what configuration best suits the regional aviation

market considering as criteria fuel consumption, specific range and block time. For

specific range, the comparison also extends to the physical parameters involved in

its estimation such as TOW, cruise speed, SFC, and aerodynamic efficiency.

Scope

The modeling and optimization of the aircraft are consistent with the conceptual

design phase introduced in Section 2.1. The models used are mainly based on

semi-empirical relations and only geometric parameters that define the wing shape

are optimized.

Methodology

First, the modeling, calibration and validation are accomplished using the SUAVE

software [4] and public data of the baseline aircraft [5, 6]. Then the most critical

geometric parameters in terms of fuel consumption and BOW are determined in

order to define the design variables. The optimization is performed using a gradient

algorithm for both fuel burn and MTOW as terms of the aggregating objective

function. The best result for each aircraft given a defined criterion is set as the

reference to be compared. The comparison is conducted considering fuel consumption,

specific range and block time.

Chapter 2 establishes the fundamental concepts of aircraft design applied in this

analysis and it also presents the models used in the SUAVE software. The main aeronautical

disciplines are discussed such as aerodynamics, performance, weight and propulsion. An

overview about MDO is also provided in the aircraft design context. Chapter 3 describes

the methodology developed and its steps in order to accomplish an impartial and reliable

comparison. Chapter 4 presents the model validation, optimization and comparison

results. Chapter 5 highlights the important results and provides the conclusion of the

present study. Chapter 6 introduces suggested complementary analysis for future work.

3

2 Literature Review

2.1 Aircraft Design Process

The aircraft design process starts with the specification of the mission requirements.

In the aerospace industry, market studies are continuously being made in order to provide

a forecast about opportunities and airlines demand. In parallel, several researches aim at

developing new technologies to make the design of improved aircraft possible. In general,

the main requirements to be set are related to the number of passengers, maximum

payload capacity, maximum cruise speed, take-off field length for sea level and hot and

high condition as well as short take-off field performance, range, time to climb, target block

fuel, etc. Then the conceptual design phase starts followed by preliminary design, detail

design, manufacturing, flight test and certification considering the case of commercial

aircraft [7]. Many authors [7, 8, 9, 10, 11, 12] suggest the appropriate scope for each step

in the design process which is summarized below:

1. Requirements Phase

List of expectations that the new design must meet. For example, number of passen-

ger, short and conventional take-off field length considering different atmospheric

conditions, landing field length, rate of climb or time to climb to a given altitude,

noise, range considering all the possible take-off runs including maximum range and

hot and high condition, cruise altitude, payload, etc.

The requirements usually are the result of a market study including the analysis

of competitors. The new aircraft must be competitive and in order to evaluate

the design, a historical study should be made and all existing airplanes should be

classified according to a competitiveness criterion.

In the aerospace industry, other requirements are necessary about the financial risk.

For instance, a maximum price for the new airplane can be set as a target as well as

the cost per block hour and manufacturing expenses. All the information usually

are estimated considering the competitors and the aircraft manufacturer experience.

Therefore, a financial risk exists and it should be mitigated during the design process.

2. Conceptual Design

The conceptual design phase is responsible for the initial idea of the new aircraft.

In other words, the aircraft configuration is determined and it is possible to first

estimate its performance using mainly historical and competitors data, semi-empirical

Chapter 2. Literature Review 4

relations and low-fidelity models since the information available about the new

design is superficial. The following characteristics are defined in the conceptual phase

according to Gudmundsson [7]:

2.1. Type of the aircraft (Piston, Turboprop, Turbofan, Turbojet, etc)

2.2. External Geometry

2.3. Mission

2.4. Technology (Avionics, Fly-by-wire, Materials, Engines)

2.5. Aesthetics

2.6. Ergonomics

2.7. Certification basis (LSA, Part 23, Part 25, Military)

2.8. Ease of Manufacturing

2.9. Maintainability

2.10. Initial Cost Estimation

2.11. Evaluation of Marketability

The conclusion of the conceptual design is an initial loft (blueprint) and an initial

performance evaluation.

3. Preliminary Design

The preliminary design phase has as a first goal to answer if the new design is viable

in technological and financial terms. To accomplish this task, more technical analysis

are made in order to expose potential problems and possible solutions as well as

opportunities to increase the aircraft performance. The scope of the analysis is listed

below [7]:

3.1. Detailed Geometry Development

3.2. Layout of major load paths in the main structures

3.3. Weight Estimation

3.4. Details of the Mission

3.5. Performance

3.6. Stability and Control

3.7. Evaluation of special Aerodynamic Features

3.8. Evaluation of Certifiability

3.9. Evaluation of Mission Capability

3.10. Preliminary Production Cost Estimation

The fidelity of the models used can range from semi-empirical relations (lower fidelity)

to CFD analysis (higher fidelity) and wind tunnel data. The expected result for the

preliminary phase is a drawing package and a preliminary design evaluation in order

to determine if the new design is feasible in both technical and financial terms.


4. Detail Design

From the preliminary design loft, the detail design phase is responsible for detailing

the new design in order to build and flight it. A brief list of the necessary analysis is

given below [7]:

4.1. Structural Detail Design

4.2. System Detail Design

4.3. Aerodynamic Detail Analysis

4.4. Performance Detail Analysis

4.5. Stability and Control Detail Analysis

4.6. Maintenance Procedures Planning

4.7. Material and equipment Logistics

4.8. Subcontractor and Vendor Negotiations

As observed above, the main aeronautical disciplines involved in the aircraft design

process are aerodynamics, performance, stability and control, propulsion and weight. In

addition, optimization techniques can be applied to define the geometry. The next sections

present the theoretical formulation used for each analysis available on SUAVE software [4]

considering only conceptual design scope in order to optimize an aircraft in terms of fuel

consumption and MTOW.

2.2 Conceptual Design Analyses

2.2.1 Aerodynamics

The aerodynamic characteristics of an aircraft has a considerable impact on its

performance. The aerodynamic coefficients of the new design at different flight conditions

yield the aerodynamic databank, which is an output from the aerodynamic discipline to

the other technologies. Therefore, it is mandatory to compute the aerodynamics of the new

airplane as it becomes a necessary input for further analyses. First, the flow conditions

considered are introduced below regarding the local Mach Number.

Subsonic, M < 1 everywhere

Transonic, mixed regions where M < 1 and M > 1. If M∞ is near unity, the flow can

become locally supersonic, weak shock waves on both the top and bottom surfaces

of the airfoil are generated behind which the flow becomes subsonic again.

For the inviscid flow prediction, it is assumed that no friction is involved as well as

thermal conduction or diffusion. In addition, compressibility effects are considered for flight

Mach Number greater than 0.3. The methods used for computing the lift and drag are the

ones available on SUAVE [4]. In the following paragraphs, the theoretical formulation for

calculating lift and drag considering subsonic and transonic flow is presented.


2.2.1.1 Lift

The available method for estimating the inviscid lift is given by a vortex lattice

theory [13] and it is considered the zero fidelity method for lift calculation [4]. Semi-

empirical relations are applied to account for fuselage, compressibility and viscous effects

in order to obtain the coefficient of lift CL for the aircraft from the inviscid wing CL.

The vortex lattice method is based on the Weissinger’s lifting-line method [14]

where discrete horseshoe vortices are positioned along the 1/4 chord line of a swept wing.

The flow tangency condition is satisfied at the control points at 3/4 chord line which allows

the determination of the bound vortex strength.

The computation of CL increment due to high lift devices such as flaps and slats

on the CLmax is carried out considering semi-empirical correlations [15, 16] as presented in

equations 2.1, 2.2 and 2.3.

∆CLslat=sa

23cos(Λ)1.4cos(sa)2 (2.1)

sa - Slat Angle

Λ - Wing Sweep Angle

∆CLflap= KcKmKswdmaxref

(2.2)

Kc - Flap chord correction factor

Km - Flap motion correction factor

Ksw - Sweep correction factor

dmaxref- Increment in CLmax for 25% chord flaps at the 50° landing flap angle.

CLmax = CLmax,wing+ ∆CLslat

+ ∆CLflap(2.3)

2.2.1.2 Drag

The total drag is calculated by adding different contributions such as parasite

drag, induced drag, compressibility drag and miscellaneous drag. The list below present

the method used for estimating each drag contribution.


Parasite Drag

The parasite drag is due to skin friction and pressure drag and its estimation is

calculated for wing, horizontal and vertical tail, fuselages, pylon and nacelles [16].

The skin friction coefficient assumes compressible flat plate formulation for all aircraft

components.

CDF= kfCfSref (2.4)

kf - Form Factor

Cf - Skin friction coefficient

Sref - Wing Reference Area

kfuselage = (1 + Cfusδumax)2 (2.5)

kfuselage - Form Factor for fuselage [16]

Cfus - User defined factor normally set to 2.3

δumax - Maximum Velocity increase on an ellipsoid of revolution

kwing = 1 +2Cfac((t/c)cos(Λ)2)√

1−M2cos(Λ)2+C2

faccos(λ)(t/c)2(1 + 5cos(Λ)2)

2(1−M2cos(Λ)2)(2.6)

kwing - Form Factor for wing [16]

Cfac - Wing form factor calibration set to 1.1 as default

t/c - thickness-to-chord ratio of the wing

Lift Induced Drag

The inviscid oswald coefficient is calculated using vortex lattice method [4]. Then,

the induced drag is calculated considering a viscous and an inviscid components as

described below.

e =1

1einviscid

+ π · AR ·K · CDP

(2.7)

CDi=

C2L

πARe(2.8)

einviscid - Span-efficiency factor

AR - Wing Aspect Ratio

K - Scaling Factor determined from flight test data

CDP- Parasite drag coefficient


Compressibility Drag

The compressibility drag is computed by correcting the drag considering the crest

critical Mach number (Mcc) as given by the equations below [16].

t/ccorrected =t/c

cos(Λ)(2.9)

CLcorrected=

CL

cos(Λ)2(2.10)

CDc = 0.0019

(M

Mcc

)14.641

cos(Λ)3 (2.11)

Miscellaneous Drag

The miscellaneous drag is taken into account considering the contributions of control

surface gaps, air conditioning system, fuselage upsweep, etc. The semi-empirical data

is based on references [15, 16].

2.2.2 Performance

The flight of an aircraft can be divided in distinct segments which can be discussed

and formulated separately. Their composition represent the mission designed for the new

aircraft and each segment is presented in Table 2 and Figure 1 considering a regional

airplane. The segments accounted for in the fuel consumption computation using SUAVE

are also indicated in Figure 1.

Table 2 – Typical Mission Segments

0-1 Taxi 4-5 Descent1-2 Take-off 5-6 Loiter2-3 Climb 6-7 Landing3-4 Cruise 7-0 Taxi

Source: Gudmundsson, 2013 [7]

Figure 1 – Typical Mission

Source: The Author


The next paragraphs develop the formulation for take-off (tricycle landing gear),

climb, cruise, descent and landing considering both turboprop and turbofan engine.

2.2.2.1 Take-off

The take-off (TO) performance usually refers to the distance required for an

aircraft to accelerate and lift-off. The aeronautical regulations (e.g. 14 CFR Part 25, items

25.103 through 25.121 [17]) require the analysis of four take-off conditions given a TOW

for commercial aircraft which are described below:

1. Take-off with all engine operating considering an obstacle of 35 ft. The total distance

calculated must be multiplied by a factor of 1.15 and must not exceed the take-off

distance available.

2. Take-off with an engine failure (for multi-engine aircraft) at VEF , one second before

V1, considering an obstacle of 35 ft. The total distance calculated must not exceed

the take-off distance available.

3. Aborted take-off with all engine operating, where the total distance must account

for the acceleration up to the V1 and the deceleration until full stop of the aircraft.

The brakes are considered to be applied two seconds after reaching V1. The total

distance calculated must not exceed the accelerated-stop distance available.

4. Aborted take-off with an engine failure at VEF , where the total distance must account

for the acceleration up to the VEF and the deceleration until full stop of the aircraft.

The brakes are considered to be applied two seconds after reaching V1. The total

distance calculated must not exceed the accelerated-stop distance available.

VEF - Speed at which the engine failure occurs

The take-off analysis for each listed condition is essential in order to determined

the allowable takeoff weight for a given altitude, atmosphere condition and available

distance for take-off. The allowable weight will be the smallest given a fixed distance

considering the four take-off conditions required by aeronautical regulations and it will be

a function of the parameters listed below according to the reference [18]:

1. Pressure altitude

2. Temperature

3. Wind velocity and direction

4. Clearway and stopway

5. Runway slope

6. Runway condition

7. Pavement strength

8. Obstacle heights and distances

9. The use of requirement for an engine-inoperative turn procedure after takeoff


10. Engine bleed configuration

11. Flap setting

12. Speed limit of the tires of the airplane

13. Type of wheel brakes on the airplane

14. Specialized takeoff techniques such as improved climb

In addition to the take-off limitations, the allowable weight can also be restricted

due to climb gradient requirements as discussed in Section 2.2.2.2.

The analysis considering all the factor and restrictions introduced here require

detailed information that does not necessarily are available in the first stages of an aircraft

design. In order to develop an understating of the take-off segment, a simplified physical

model is discussed in this section, however, further simplifications are necessary in order to

compute the take-off field length and allowable weights considering the scope of conceptual

design phase.

The TO run is split in important segments as shown in Figure 2 in order to

simplify the formulation for each one.

Figure 2 – Important segments of the Take-off run.


During ground roll, the aircraft is considered to be in a constant accelerated

movement in which the nose landing gear is in contact with the runaway. Then after the

rotation speed VR is reached, the aircraft starts to rotate and the nose landing gear leaves

the ground. The next TO segment takes into account a transition maneuver which leads

the aircraft to the climb phase. The TO distance is considered until the aircraft overcome

an obstacle required by aeronautical regulations which differs according to the class of the

airplane. The important airspeeds during the TO run are presented in Figure 3.


Figure 3 – Important airspeed during the Take-off run.


Gudmunsson [7] gives the following description for each important airspeed:

VS - Stalling speed or minimum steady flight speed for which the aircraft is still control-

lable.

VMC - Minimum Control Speed with the critical engine inoperative

V1 - Maximum speed during the take-off at which the pilot can either safely stop the

aircraft without leaving the runaway or safely continue to V2 take-off even if a critical

engine fails (between V1 and V2)

VR - Rotation speed. The speed at which the airplane’s nosewheel leaves the ground. It is

high enough to ensure the aircraft can reach V2 at 35 ft (commercial) in the case of

an engine failure on a multiengine aircraft.

VMU - Minimum unstick speed. The airspeed at which the airplane is no longer “sticks” to

the ground. It is a function of the ground attitude of the airplane. The minimum is

achieved when the ground attitude is at CLmax or at the maximum possible due to

geometric restrictions. Defined for commercial aircraft in 14 CFR Part 25.

VLOF - Lift-off speed

V2 - Take-off safety speed. Airspeed in which the airplane must be capable of reaching in

a given altitude above the ground according to aeronautical regulations.

The equation of motion for a ground run is derived considering the general

free-body diagram presented in Figure 4. In this condition, L < W .

Figure 4 – General Free Body Diagram for ground roll.



dV

dt=

g

W(T −D − µ(W − L)) (2.12)

L =1

2ρV 2SCL (2.13)

D =1

2ρV 2SCD (2.14)

P = T · V (2.15)

D - Drag as a function of V

g - acceleration due to gravity

L - Lift as a function of V

T - Thrust

P - Power

W - Weight, assumed constant

µ - ground friction coefficient

The solution of the equation of motion can be computed using numerical integration

method. The difference between turboprop and turbofan aircraft is mainly on how the

thrust is calculated. The distance traveled is determined by integrating the velocity during

the ground roll as expressed by Equation 2.17.

V =

∫a · dt =

∫dV

dt· dt =

∫g

W(T −D − µ(W − L)) · dt (2.16)

S =

∫V · dt (2.17)

For rotation, a typical time of 2 to 5 seconds is expected for large airplanes to

reach climb phase. The Equation 2.16 can also be applied if the drag and lift forces are

updated considering the change in the aircraft attitude.

The distance during transition and climb can be computed considering the climb

angle and that the obstacle is cleared after the transition segment is completed. The

equations below gives the total distance for transition and climb phase.

Climb Angle:

sin θclimb =T −DW

=T

W− 1

L/D(2.18)

Transition distance:

STR = R · sin θclimb ≈ 0.2156 · V 2S ·(T

W− 1

L/D

)(2.19)

Transition height:

hTR = R(1− cos θclimb) (2.20)

Climb distance over an obstacle:

SC · tan θclimb = hobstacle − hTR (2.21)


The total distance of the take-off run is given by summing each segment:

Stotal = SGround−Roll + SRotation + STransition + SClimb−obstacle (2.22)

Although the formulation derived above seems to be simple, the information

required in order to solve the equation of motion can be very challenging considering the

fact that in conceptual design just a few geometric parameters of the new aircraft are

known. The attempt to predict some parameters in advance in the conceptual design phase

as the ones listed in the beginning of the section can introduce uncertainties in the results

that may be not traceable and lead to unreliable estimations. Therefore, the SUAVE

software [4] offers a semi-empirical formulation for estimating take-off field length which is

based on historical data from certified aircraft. The advantage in using the semi-empirical

model lies on the fact that it covers all four take-off conditions estimation. In addition,

the model gives information about the physical trends and trade-offs of the take-off field

length allowing decision making about the new design. For tube-wing aircraft, the results

are satisfactory as shown in the Chapter 4 for the E170 jet and ATR 72-600 aircraft.

The semi-empirical model is based on the most critical parameters for take-off field

length such as wing area, maximum lift coefficient, take-off weight and engine thrust as

discussed in the reference [4]. The equation of the semi-empirical model and its coefficients

are presented below where V2 is typically 1.2 of the stall speed for a given flap setting.

TOFL =2∑

i=0

ki ·[V 22

T/W

]i(2.23)

Table 3 – Coefficient of Equation 2.23

Engine k0 k1 k2

2 857.4 2.476 1.40e-43 667.9 2.343 9.30e-54 486.7 2.282 7.05e-5

Source: Lukaczyk et al., 2015 [4]

2.2.2.2 Climb

The climb performance of an aircraft dictates how quickly the airplane reaches a

desired cruised altitude and thus how its noise footprint is perceived. Also, as introduced

before, the climb gradient in the second segment shown in Figure 5 may impose restrictions

in the allowable weight due to aeronautical regulations demand. The climb gradient in

the second segment can be calculated using Equation 2.32 and must be equal or greater

than 2.4%. Although other requirements related to climb gradient in different conditions

must be met, the minimum climb gradient in the second segment is the most restrictive


for civil transport aircraft according to reference [18]. Therefore, it is essential to evaluate

this parameter in the conceptual design in order to provide more realistic estimations for

the allowable take-off weight.

Figure 5 – Second Segment Climb

Source: The Author

Now, considering first a general climb segment, the performance is primarily

measured in terms of rate of climb and climb gradient. To determine its parameters, the

climb formulation is derived from the free body diagram presented in Figure 6.

Figure 6 – Free Body Diagram considering Climb Segment.


The general planar equations of motion for an airplane is presented below consid-

ering the Figure 6.

L−W cos θ + T sin ε =W

g

dVzdt

(2.24)

D −W sin θ + T cos ε =W

g

dVxdt

(2.25)


The following assumptions are applied in order to consider a steady flight during

climb.

1. Steady motion implies dV/dt = 0

2. The climb angle θ is a non-zero quantity

3. The angle of attack α is small

4. The thrust angle ε is 0 °

Then the Equations 2.24 and 2.25 can be rewritten as

L = W cos θ (2.26)

T −D = W sin θ (2.27)

The climb angle, horizontal and vertical airspeed can be determined using equations

sin θ =T

W− 1

L/D(2.28)

VH = V cos θ (2.29)

For jets:

ROC = VV = V sin θ ≡ TV −DVW

(2.30)

For propellers:

ROC = VV = V sin θ ≡ Pavailable − Prequired

W(2.31)

ROC - Rate of climb

Pavailable - Power Available

Prequired - Power Required

In the SUAVE software [4], the estimation of the climb gradient in the second

segment discussed previously is given by the approximation in Equation 2.32. In this

condition, the drag must account for an increase due to engine failure and therefore

asymmetric flight.

γ =T

W− 1

L/D(2.32)

γ - Climb gradient


The equations 2.24 and 2.25 can also be solved numerically considering different

profiles for the climb phase which are available in the SUAVE software [4]:

1. Constant-Airspeed Constant-Climb-Rate

2. Constant-Mach Constant-Climb-Rate

3. Constant-Mach Constant-Climb-Angle

4. Constant-Throttle Constant-Airspeed

The Constant-Throttle Constant-Airspeed climb profile is the most representative

profile available on SUAVE considering commercial aircraft since it is more convenient for

the pilot to leave the throttle in a set position. However, the airspeed set to be constant

in the software is the true airspeed while in the operation, the speed schedule during

climb is composed by a segment in which the calibrated airspeed is constant and then

the mach is set to be constant since it must not exceed the maximum operating mach

number. Moreover, SUAVE may present convergence problems on the mission solver when

considering the Constant-Throttle Constant-Airspeed climb profile which is unacceptable

in an optimization environment using gradient based algorithms.

2.2.2.3 Cruise

The cruise segment is considered as a straight and level flight which the aircraft is

designed for in order to perform efficiently. In other words, the goal is to have the highest

possible airspeed for a given fuel consumption. The Figure 7 shows the free body diagram

for the cruise phase.

Figure 7 – Free Body Diagram considering Cruise Segment.



The planar equations of motion is derived below considering the Figure 7 and

steady motion.

L = W (2.33)

D = T (2.34)

The solution of the above equations bring to light important characteristics of the

aircraft such as maximum airspeed, stall airspeed in level flight and best range airspeed. In

the context of the present work, the stall airspeed is one of the most important parameters

since it is a function of CLmax as expressed in Equation 2.35.

VS =

√2W

ρSCLmax

(2.35)

2.2.2.4 Range

The range of the aircraft can be determined considering the cruise profiles described

in Table 4.

Table 4 – Cruise Profiles

Profile V ρ CL/CD SUAVE

1 Constant airspeed/altitude Constant Constant Available [4]2 Constant attitude/altitude Constant Constant Not Available3 Constant airspeed/attitude Constant Constant Not Available


The formulation for range can be done considering the following expressions.

dR

dW=Rate of change of distance

Rate of change of weight=

V

−ctT(2.36)

ct ≡ωfuel

T(2.37)

ct =cBHPV

ηP(2.38)

cBHP ≡ωfuel

P(2.39)

ct - Thrust specific fuel consumption

cBHP - Specific fuel consumption

ωfuel - Fuel Weight Flow

ηP - Propeller Efficiency


Then the equation can be rewritten considering Equations 2.33 and 2.34.

dR

dW=V (L/D)

−ctW(2.40)

R =

∫ Wini−Wf

Wini

V

−ctCL

CD

1

WdW (2.41)

In order to solve the equation, the cruise profiles presented in 4 has to be considered.

The simplest formulation is given by profile 3 - constant airspeed/attitude since V and

CL/CD are constant.

R =V

ct

CL

CD

∫ Wini

Wini−Wf

1

WdW =

V

ct

CL

CD

lnWini

Wf

(2.42)

The range can be computed for different TOW and fuel weight which covers the

possible missions performed by the aircraft operators as well as highlights the trade-offs

between range and payload. The results can be displayed in a chart named Payload Range

Diagram as explained in Figure 8.

Figure 8 – Payload Range Diagram



2.2.2.5 Descent

The formulation for descent segment is derived considering the free body diagram

presented in Figure 9.

Figure 9 – Free Body Diagram - Descent Segment.


The planar equations of motion is given below

L−W cos θ + T sin ε =W

g

dVzdt

(2.43)

−D +W sin θ + T cos ε =W

g

dVxdt

(2.44)

1. Steady motion implies dV/dt = 0

2. The climb angle θ is a non-zero quantity

3. The angle of attack α is small

4. The thrust angle ε is 0 °Then the equations of motion can be rewritten considering steady unpowered

descent.

L = W · cos θ (2.45)

D = W · sin θ (2.46)

The angle of descent is derived by the ratio between Equations 2.45 and 2.46.

tan θ =1

L/D(2.47)


The rate of descent is expressed in terms of the airspeed V

V =

√2 cos θ

ρCL

W

S(2.48)

VV =CD

C3/2L

√2

ρ

W

S(2.49)

VV = V · sin θ =D

WV =

V

CL/CD

(2.50)

2.2.2.6 Landing

Similar to the take-off analysis, the landing performance refers to the distance

required for an aircraft to approach and decelerate. The landing run is split in important

segment as shown in Figure 10 in order to simplify the formulation for each one.

Figure 10 – Import segments of the landing run.


The approach phase starts from a steady descent. Then the pilot performs a flare

maneuver in order to raise the nose of the aircraft and touch the runway smoothly. The

landing segment finishes with the deceleration from the touch-down until the complete

stop of the aircraft. The pilot only actuates the brakes after a free roll distance.

The free-body diagram to be considered in the landing analysis is the same as

the one used for take-off run (Figure 4). The difference is the order of each phase as

discussed above. For this reason, the theoretical formulations are the same of those derived

for take-off run (Equations 2.16, 2.17).

The total distance required for landing the aircraft is then given by summing each

phase contribution as expressed in the Equation 2.51.

Stotal = Sapproach + Sflare + Sfree roll + Sbreaks on (2.51)


Analogous to the take-off field length estimation, the SUAVE software [4] also

offers a semi-empirical model to compute the landing field length based in the formulation

proposed by Torenbeek [10]. The semi-empirical relation is given by the Equation 2.52.

LFL =2∑

i=0

ki ·[V 2app

]i(2.52)

Vapp - Approach speed

Table 5 – Coefficient of Equation 2.52

Wheel Trucks k0 k1 k2

2 250 0 0.25334 250 0 0.3030


2.2.2.7 Fuel Consumption

It is important to determined the fuel required for a mission which can be

accomplished considering the Equation 2.37 rewritten below.

ωfuel =dWfuel

dt= ct · T (2.53)

Wfuel =

∫ct · T · dt (2.54)

The Equation 2.54 can be applied for all segments presented in Figure 1 using

numerical integration. The sum of all segments gives the required fuel weight to accomplish

the design mission.

2.2.3 Weight Estimation

The weight estimated during the design process is an important input for the

performance analyses as observed in the theoretical development in section 2.2.2. The

methods available in the literature range from simplified methods which requires only some

geometric parameters to more sophisticated methods that require detailed information

about the aircraft [7]. Considering the scope of conceptual design, the SUAVE software

uses semi-empirical relations for a tube-and-wing aircraft based in data from references

[15, 16].

The methodology developed by Shevell [16] at Douglas Aircraft and Kroo [15]

calculates a portion of the empty weight of the aircraft which takes into account the wing,

tail, fuselage, furnishings and landing gear weight. The equations uses English engineering

units in pounds and feet, unless otherwise specified and they are presented below for each

component listed.


1. Wing

Wwing = 4.22Sw + 1.642 · 10−6 · Nultb3√WMTOWWMZF (1 + 2 · λ)

(t/c)avg cos Λc/42Sw(1 + λ)

(2.55)

Wwing - Wing structural weight

Sw - Wing area

Nult - Ultimate design load factor for the aircraft. Its value must comply with

aeronautical regulations such as CFR 14 Part 25.

b - Wing span

λ - Wing taper ratio

(t/c)avg - Wing thickness to chord ratio

Λc/4 - Wing sweep angle at 1/4 chord line

2. Horizontal Tail + Elevator

WHT = 5.25SHT + 0.8 · 10−6 · Nultb3HTWMTOW cw

√SHT

(t/c)avg cos ΛHTc/4

2lHTS1.5HT

(2.56)

WHT - Horizontal Tail structural weight

SHT - Horizontal Tail area

bHT - Horizontal Tail span

lHT - Distance between wing aerodynamic center and horizontal tail aerodynamic

center

(t/c)avg - Horizontal Tail thickness to chord ratio

ΛHTc/4- Horizontal Tail sweep angle at 1/4 chord line

3. Vertical Tail

WV T = 2.62SV T + 1.5 · 10−5 ·Nultb

3V T

(8.0 + 0.44 · WMTOW

Sw

)(t/c)avg cos ΛV Tc/4

2 (2.57)

WvT - Vertical Tail and Elevator structural weight

SvT - Vertical Tail area

bvT - Vertical Tail span

(t/c)avg - Vertical Tail thickness to chord ratio

ΛV Tc/4- Vertical Tail sweep angle at 1/4 chord line

4. Fuselage

Ip = 1.5 · 10−3∆Pf · wf (2.58)

Ib = 1.91 · 10−4 ·Nlim(WMZF −Ww −Ww,p) ·lf,eh2f

(2.59)


If the Ip > Ib, If = Ip. If not,

If =I2p + I2b2 · Ib

(2.60)

Wf = (1.051 + 1.020 · If )Sf,wetted (2.61)

Wf - Fuselage structural weight

∆Pf - Maximum differential pressure of the fuselage

Ww,p - Weight of the wing-mounted engines, nacelles and pylons

lf,e - Effective fuselage length. Fuselage length minus the wing chord root divided

by two.

hf - Fuselage Height

5. Furnishings

For aircraft with 300 or fewer seats,

Wfurn = (43.7− 0.037 ·Nseat) + 46 ·Nseat (2.62)

For aircraft over 300 seats,

Wfurn = (43.7− 0.037 · 300) + 46 ·Nseat (2.63)

Wfurn - Furnishings Weight

Nseat - Number of Seats

6. Landing Gear

WLG = 0.04 ·WMTOW (2.64)

WLG - Landing Gear Weight

7. Propulsion System including Engine

Wpropulsion = 1.6 ·Wp,dry = 1.6 · (0.4054 · T 0.9255SLS ) (2.65)

Wpropulsion - Propulsion system Weight

Wp,dry - Dry weight of the engine

TSLS - Sea-level static thrust

An iterative process is needed in order to define the components weight and

MTOW as observed in the equations above. The strategy adopted in the present work to

deal with this issue will be discussed in Chapter 3.


2.2.4 Propulsion

This section describes the aspects of flight propulsion that are necessary for aircraft

design. The two main characteristics discussed are thrust (or power) and fuel consumption

since they directly dictates the airplane performance. Besides, the tradeoff between thrust

and efficiency is introduced in order to give a first comparison considering a turbofan and

a turboprop engine. Then each power plant is described in terms of its thrust and fuel

consumption behavior due to changes in aircraft speed and altitude considering empirical

relations [19]. Finally, the method for engine modeling used in the SUAVE software is

presented.

2.2.4.1 The Tradeoff between Thrust and Efficiency

A generic propulsion device [11] generates thrust by interacting with the flow

and this physical phenomena can be explained considering Newton’s second law which

states that the force on an object is equal to the time rate of change of momentum

(mass times velocity) of that object. In this case, the flow is moving thought the generic

propulsion device and its initial momentum per unit time is m · V∞ (m is the mass flow).

The time rate of change of momentum is simply the change of momentum of the air flowing

through the propulsion device. This can be calculated considering the difference between

the momentum of the air flowing in and out. The Equation 2.66 is the thrust equation

considering a generic propulsion device as described above.

T = m · (Vout − Vin) = m · (Vout − V∞) (2.66)

An equation for efficiency can also be derived considering it as a function of

velocity. The definition of efficiency is given by Equation 2.67 and Equation 2.68 is

developed by reference [11] considering a generic propulsion device.

η =useful power available

total power generated(2.67)

η =2

1 + Vout/V∞(2.68)

As observed above, the thrust can be generated considering two mechanisms. The

first one is by having a relatively large mass flow m with a small change in the velocity of

the air. The other one generates thrust by increasing the difference Vout − V∞ for a small

m. In case of a propeller, m is large since the propeller diameter allows a bigger flow mass.

However, the change in the velocity is limited by the airspeed in the propeller tip in order

to avoid wave drag. On the other hand, a turbofan engine takes a relatively small mass

flow and increases the difference Vout − V∞ without the severe limitation in airspeed in

comparison with a propeller. This is the reason why propeller powered aircraft usually fly

at low speeds whereas a turbofan aircraft can fly in transonic conditions.


Now, considering the efficiency Equation 2.68, it is clear that the efficiency of

the power plant increases by decreasing the ratio Vout/V∞. This mean that a propeller is

more efficient than a turbofan since it generates a small change in the velocity of the flow.

However, a turbofan can generate more thrust since it does not have a severe limitation in

airspeed. With this example, the tradeoff between efficiency and thrust is illustrated and

the power plant should be selected according to the mission requirements. If the airplane

must fly in transonic speeds then a turbofan is suitable for the design. On the other hand,

if there is a compromise between speed and fuel consumption then both turbofan and

turboprop engines should be compared.

2.2.4.2 Turbofan Engine

The turbofan is presented in Figure 11 with a longitudinal cut in order to show

each engine component. The turbofan is similar to a pure jet, however the main difference

is given by the fan and its contribution to the total thrust generated. The engine showed

in Figure 11 is composed by a fan, compressor, combustion chamber, turbine and nozzle.

Figure 11 – Turbofan components.

Source: Maerkang, 2009 [20]

As discussed previously, a generic propulsion device generates thrust by interacting

with the flow and the simplified thrust equation [11] illustrates that the thrust generated

is a function of the flight condition which includes speed and altitude (m = ρ∞AV∞, ρ∞

is a function of altitude). In case of a turbofan engine, the bypass ratio, an important

parameter, is defined as the mass flow passing through the fan, externally to the core

divided by the mass flow through the core itself [11] considering the Figure 11. This

parameter is one of those which dictates the engine behavior. For high bypass ratio (above


5), the effects of speed and altitude in the turbofan thrust is closer to a propeller than it

is closer to a pure jet.

2.2.4.2.1 Thrust

The variation of thrust with altitude can be approximated considering the empirical

relation given in Equation 2.69 as suggest by Mattingly [19] in order to discuss the

engine expected behavior.

T

T0=

(ρ

ρ0

)m

(2.69)

T - Thrust for a condition of interest

ρ - Air density for a condition of interest

T0 - Thrust for sea level condition

ρ0 - Air density for sea level condition

m - depends on the engine design and it is usually near 1

Equation 2.69 shows that as the altitude increases and thus the air density

decreases, the thrust generated decreases.

Considering the effect of speed, the variation of thrust is given by the empirical

relation expressed by Equation 2.70 as discussed by reference [11].

T

T0= AM−n

∞ (2.70)

A - Function of altitude, A > 0

n - Function of altitude, n > 0

M∞ - Mach number

The Equation 2.70 includes the effect of altitude combined with the effect of

speed. Although the parameters A and n must be defined for each engine and flight

condition, it is possible to state that the thrust decreases by increasing the Mach number.

2.2.4.2.2 Thrust Specific Fuel Consumption

The thrust specific fuel consumption is defined as [11]

ct = weight of fuel burned per unit thrust per unit time (2.71)

or

ct =weight of fuel for a given time increment

(thrust output)(time increment)(2.72)

The units in SI (International System of Units) gives

[ct] =N

N · s=

1

s(2.73)


However, the thrust specific fuel consumption was also conventionally defined

using

[TSFC] =lb

lb · h=

1

h(2.74)

The variation of ct with Mach Number for a given altitude follows the empirical

relation expressed in Equation 2.75 for M∞ ranging from 0.7 to 0.85 [11].

ct = B · (1 + k ·M∞) (2.75)

B - Empirical constant found by correlating the engine data

k - Empirical constant found by correlating the engine data

2.2.4.3 Turboprop Engine

The turboprop is a propeller driven by a gas-turbine engine as shown in Figure 12.

Its performance is expressed in terms of power, P = T · V∞.

Figure 12 – Turboprop components.

Source: Maerkang, 2009 [20]

The propeller performance is measure in terms of propeller efficiency defined by

Equation 2.76 which is a function of the advance ratio J expressed in Equation 2.77.

ηprop =P

PA

(2.76)

J =V∞N ·D

(2.77)

P - Shaft Power

PA - Available Power

N - Number of rotations per second of the propeller

D - Propeller Diameter


The advance ratio is a similarity parameter for the propeller performance in the

same category as Reynolds Number and Mach Number. This parameter can be used to

estimate the propeller efficiency using empirical data such as the Naca Report TR 640.

The propeller efficiency is a necessary input for the calculation of available power. In

the case of a turboprop engine, the available power is actually given by the sum of two

contributions, the power generated by the shaft and the thrust due to the jet exhaust as

described in Equation 2.78. Usually, the second contribution correspond to 5% of the

total thrust generated.

PA = ηprop · P + Tjet · V∞ (2.78)

2.2.4.3.1 Power

Some engine manufactures present the turboprop performance in terms of equiva-

lent shaft power which includes the effect of jet exhaust and is defined as

Pes =PA

ηprop(2.79)

As a first approximation, the PA can be considered constant with Mach Number

as discussed by reference [11]. The effect of altitude on the power available is given by the

empirical relation in Equation 2.80.

PA

PA0

=

(ρ

ρ0

)n

(2.80)

n - Empirical constant, n = 0.7

2.2.4.3.2 Specific Fuel Consumption

The specific fuel consumption is defined as

c = weight of fuel burned per unit power per unit time (2.81)

or

c =weight of fuel for a given time increment

(power output)(time increment)(2.82)

The units in SI (International System of Units) gives

[c] =N

W · s(2.83)

However, the specific fuel consumption was also conventionally defined using

[SFC] =lb

hp · h(2.84)


The variation of c with velocity and altitude is first considered constant [11].

Although the value of c remains constant by increasing the altitude, the power required

defined as PR = D · V∞ decreases, where D is the aircraft drag. Therefore, c can be

decreased by decreasing the power generated by the engine as the PR decreases. The power

available varies with altitude, however, the cruise altitude can be defined in such a way

that the fuel consumption is minimized.

2.2.4.4 Turbofan and Turboprop Comparison

The turboprop and turbofan engines can be compared in terms of propulsive

efficiency, flight Mach Number and cruise altitude as presented in Figures 13 and 14.

Figure 13 – Efficiency Comparison.

Source: Medium - Images, 2017 [21]


Figure 14 – Velocity and Altitude Comparison

Source: Mattingly, 1996 [19]

As observed above, the turboprop has a more restrict operation than a turbofan

engine. However, the turboprop has a higher propulsive efficiency. The comparison between

engines can only be realistically made considering two real engines and two similar aircraft

in order to evaluate fuel consumption.

2.2.4.5 SUAVE Model: Energy Network Method

The SUAVE software uses an energy network method [4] in order to model different

propulsive systems. In the case of a gas-turbine, the energy network framework is composed

by individually modeled components such as fan, compressor, turbine, combustor, etc, in

order to compute thrust and fuel consumption rate. In the software, the one dimensional

flow equations are solved across each component according to references [22, 23]. The

Figure 15 represents the turbofan network used to compute the engine thrust and fuel

consumption.

Figure 15 – Turbofan Network.


The engine used in the optimization will be modeled for E170 by composing an

energy network framework for gas-turbine. For ATR 72 600, external data for a general

turboprop engine provided by EMBRAER S.A. will be used.


2.3 Optimization

The use of optimization in engineering design is a common and efficient approach

specially in complex problems that involves non-intuitive relations. A mathematical model

is used in order to represent a real problem which must preserve the main characteristics

necessary to simulate it. Simplification and restrictions are applied and the goal is usually

to minimize or maximize a certain parameter which is represented as an objective function.

In addition, the optimization can provide useful information about the design space that

is valuable for the development of new solutions or for the understanding of the design

limitations and tradeoffs. In order to briefly discuss about the optimization techniques, the

general optimization problem is presented below considering it as a minimization problem

and single-objective optimization.

minimizex

f0(x)

subject to gk(x) ≤ ck, k = 1, . . . , n.

hi(x) = bi, i = 1, . . . ,m.

(2.85)

The basic elements of an optimization problem consist of the following list.

x - Vector of design variables which represents the parameters that affect the objective

function value. The design variables are the unknown quantities to be defined by the

optimization solution. In addition, bounds can be set on these variables since they

may represent physical measurements. In other words, inequalities constraints can

be used to restrict the design variables possible values.

f0(x) - Objective Function: it is a mathematical representation of the parameter being

minimized which is function of the design variables.

hi(x), gk(x) - Constraint Functions is a set of restrictions that the optimization solution

must satisfy in order to be feasible. They are expressed as equality or inequality

constraints respectively which may represent limitations on the design variables as

well as criteria for other parameters or physical relations.

A variety of optimization algorithms is available and they are usually classified in

groups. The main division between algorithms is related to the method used for computing

the iterations until the solution is reached which divides them in gradient-based and

gradient-free optimization algorithms [24]. The first category uses gradient information to

decide where to move in the design space . The objective function can be nonlinear and

must have continuous first derivatives and, in some cases, continuous second derivatives.

Gradient-free algorithms use sampling and/or heuristics to decide where to move in the

design space.


2.3.1 Multidisciplinary Optimization in the Aircraft Design - MDO

A methodology for designing complex engineering systems is essential to exploit

the synergism and mutually interacting phenomenas in order to achieve the optimal solu-

tion. The previous discussion highlights the optimization general definition and algorithms.

This section expands the discussion in terms of the disciplines involved in an optimization

problem by presenting Multidisciplinary Design Optimization (MDO) methodology appli-

cable to aircraft design. In fact, an airplane optimization contains more than one discipline

as illustrated by the Breguet equation for computing the aircraft range.

R =V

ct

CL

CD

· ln Winitial

Wfinal

(2.86)

Where the airspeed V is given by market studies as a performance requirement,

the ratio CL

CDis calculated by the aerodynamics discipline, the parameter ct is under the

propulsion discipline and the ratio between the initial and final weight involves structure

discipline, due to empty weight estimation, and propulsion, due to the fuel weight estimation.

Consequently, it is possible to state that aircraft design is inherently multidisciplinary.

In MDO, there are two well-defined components. One of them is the optimization

algorithm, it can be one from the groups presented previously according to the nature of

the optimization problem, and the other component is the simulation model necessary to

evaluate the designs chosen by the optimizer. With this two main components described,

a typical process in MDO can be listed below according to reference [25].

1. Define the overall system requirements.

2. Define the design vector, the objective function and the constraints.

3. System decomposition in modules.

4. Modeling of physics via governing equations in the module level.

5. Model integration into an overall system simulation.

6. Benchmarking of the model (calibration) with respect to a known system from past

experience if available.

7. Design Space Exploration (DoE) to find sensitive values and important design

variables

8. Formal optimization to find the solution that minimizes the objective function

9. Post-optimality analysis to explore sensitivity and tradeoffs: sensitivity analysis,

approximation methods, etc.

The present work aims at applying the MDO methodology listed above in order to

have a rational optimization process. However, the focus is not the MDO itself, some tech-

niques are only used to guarantee the necessary organization due to the multidisciplinary

nature of the problem. The SUAVE software represents the items 3 to 5.

33

3 Methodology

The turboprop and turbofan powered aircraft for approximately 70 PAX compete

in the regional aviation market performing, in general, missions of medium and short range

as presented in Chapter 1. Although they have the number of passengers in common,

the comparison between the two types of airplane has to be carefully conducted since they

present distinct characteristics that may be an advantage or disadvantage according to

the criterion chosen. In addition, comparing designs from different sources may lead to

skewed results since the hypothesis considered behind the performance estimations may

also be different. To avoid skewing of the comparison, the analysis must be carried out

considering both airplanes modeled and optimized with the same assumptions and models.

Aiming at an organized and impartial approach, the methodology structure is

based on the MDO process presented in Section 2.3.1 for aircraft design. Table 6 presents

the correspondence between the methodology phases and the steps in the MDO process

adopted for defining the final geometry for both turbofan and turboprop aircraft.

Table 6 – Methodology structure based on a MDO process

E170 and ATR72-600 data · Define the overall system requirements· Define the objective function and the constraints

SUAVE · System decomposition in modules· Modeling of physics via governing equations in themodule level· Model integration into an overall system simulation

Aircraft model/calibration · Benchmarking of the model with respect to aknown system from past experience, if available.· Multidisciplinary Design Analysis (MDA) in orderto compute performance results for baseline geometry

Optimization Strategy · Design of Experiments (DoE) to find sensitivevalues and important design variables· Formal multidisciplinary optimization (MDO) tofind the solution that minimizes the objective function

Source: The Author

The methodology is divided into three phases and each one of them is described in

detail in the next sections. Section 3.1 presents the modeling and the calibration strategy

of the aircraft model. Section 3.2 discusses how the design variables are defined and how

the optimization is conducted. Finally, Section 3.3 describes the approach applied in the

comparison between the turboprop and the turbofan aircraft.

Chapter 3. Methodology 34

3.1 Aircraft Model

The modeling is preceded by the search of public data related to the aircraft

defined as baseline geometry. As presented in Section 2.2, the aircraft model is based on

semi-empirical relations, therefore it is important to compare the results obtained using

the SUAVE software with public data. Calibration of model’s constants may be necessary

in order to best fit the references. In this work, the sources were the manufacturer’s

information of each aircraft and literature results in order to ensure that the data is

reliable. The specification sheet [5, 6], Airport Planning Manual (APM) [26], Flight Crew

Operating Manual [27] and papers with aircraft information [4] are the best options to be

used as reference.

Even with the sources listed, the data is scarce, thus it is necessary to define

what parameters available will be considered to calibrate or to validate the aircraft model.

Since the goal is to optimize the aircraft in terms of fuel consumption and MTOW, the

aerodynamic, engine and weight models must be validated through performance estimations

that capture the influence of all disciplines while ensuring that the mission simulated is

consistent with public data. The performance estimation suggested to be compared with

official values from the aircraft manufactures are listed below.

1. Payload Range Diagram

1.1. Range for MTOW, Full Payload, Optimum Flight Level, ISA +0°C1.2. Range for MTOW, Full Fuel, Optimum Flight Level, ISA +0°C1.3. Range for TOW with Full Fuel, zero Payload, Optimum Flight Level, ISA +0°C

2. Takeoff Field Length (TOFL)

2.1. TOFL - MTOW, SL, ISA +0°C2.2. TOFL - TOW for 500nm or 300nm range, SL, ISA +0°C2.3. TOFL - Maximum allowable weight for Denver International Airport (DIA),

5333 ft, ISA +23°C or similar condition

3. WAT (Weight for Altitude and Temperature) - limited by second segment climb

gradient

3.1. SL, ISA 0°C3.2. Denver International Airport (DIA), 5333 ft, ISA +23°C or similar condition

4. Landing Field Length (LFL)

4.1. LFL - MLW, SL, ISA +0°C

The error accepted for each estimation is 3.5% since the model must be capable

of representing the performance expected for a turbofan and a turboprop aircraft. More


parameters could be considered, however, the semi-empirical model is limited to some of

the typical conditions that the aircraft will face on the operation. Therefore, the conditions

listed above were chosen and they are part of the mission simulated during the optimization.

The intention is to guarantee that the trade-offs are reasonably represented as well as the

sizing requirements that dictates the performance estimations such as MTOW.

All the models formulation were previously detailed in Section 2.2 and the

next sections describe the strategy applied to validate and calibrate them. Section 3.1.1

describes the control parameters used to approximate the BOW estimation to public

data. Section 3.1.2 discuss what are the best conditions to use to evaluate parameters

such as CLmax, drag estimations and L/D. Finally, Section 3.1.3 presents the suggested

considerations to calibrate the engine model.

3.1.1 Weight Model

The weight model implemented in SUAVE for tube-and-wing aircraft were pre-

sented in Section 2.2.3. The inputs are mainly geometric dimensions and they can be

measured using the blueprint of the reference aircraft. CAD software such as SOLID-

WORKS® offers an useful feature to measure dimensions using 2D drawings. With the

geometric inputs, the empty weight breakdown is done considering the contributions of

the horizontal tail, propulsion, rudder, furnishings, fuselage, landing gear, vertical tail and

wing. Unfortunately, detailed weight data is generally not public, thus the reference value

must be the basic operating weight given in the specification sheet of the baseline aircraft.

Figure 16 shows an example of public information about the E170 jet.

Figure 16 – Example of public data containing weight information, E170 jet

Source: EMBRAER S.A., 2015 [26]

During the optimization, the geometric parameters for wing, horizontal and vertical

tails are continuously changing. In addition, the weight of the landing gear varies during


the optimization since it is proportional to the MTOW. In the case of the engine weight for

each aircraft, it must be exclusively a function of the static thrust at sea level. Therefore,

the contributions of these components will dictate the increase or decrease in the empty

weight of each aircraft being analyzed during the optimization, while the weight of the

other components such as fuselage and furnishings is considered to be fixed based on the

reference aircraft geometry.

The calibration of the weight model can be conducted, if necessary, considering

the fuselage weight as the control parameter. As shown in Section 2.2.3, the fuselage

weight is a function of the maximum differential pressure encountered by the aircraft,

which is a consequence of the service ceiling and pressurization. The maximum differential

pressure must range from 5.5 to 9.4 psi [28] where the absolute ceiling must be taken into

account. The exactly value must be defined in order to approximate the empty weight

estimation to the public data.

3.1.2 Aerodynamic Model

The aerodynamic models implemented in SUAVE for the estimation of drag,

CLmax and L/D considering the second segment climb gradient are based on semi-empirical

relations previously introduced in Section 2.2.1. The inputs are mainly related to the

aircraft geometry and high lift device deflections. The prediction of these aerodynamic

parameters directly affects the performance results in the conditions discussed in the

beginning of Chapter 3. Therefore, the strategy is to assign the evaluation of a given

aerodynamic parameter to a performance estimation. The list below shows the relations

between aerodynamic prediction and the most impacted performance results.

Table 7 – Aerodynamic parameters and the most impacted performance results

Aerodynamic Parameter Performance Analysis

CD Payload Range DiagramCLmax TOFL and LFL estimations

L/Dsecond segment Second Segment ClimbGradient estimation

Source: The Author

3.1.2.1 Drag Model Evaluation

The drag model is evaluated by comparing the calculated payload range diagram

with the public data. In order to isolate the aerodynamic influence, the empty weight is

inputted equal to the one provided by the aircraft manufacturer. The engine also impacts

the payload range diagram and the strategy is to first calibrate the engine model SFC and

design thrust with the engine manufacturer’s data as discussed in Section 3.1.3.


The total drag can be estimated by the sum expressed in Equation 3.1. Each

term of the equation was discussed in Section 2.2.1.

CD = CD0 + CDLift Induced+ CDCompressibility

(3.1)

The payload range diagram provides useful information about the aerodynamic

model since displacement or distorsion in the curves indicate where the errors may be

coming from. In case of a displacement, the error may be caused by errors in the parasite

drag since it does not depend on the value of the lift coefficient. In case of a distortion, the

error may be generated due to limitations of the induced lift drag model or compressibility

drag model.

In order to illustrate the impact of each portion of the drag coefficient, a parametric

analysis was carried out. Figure 17 presents an example of impact on the final payload

range diagram. The data used in this example was based in the E170 jet.

Figure 17 – Example of the impact in the Payload Range Diagram due to errors in CD

Source: The Author

The explanation for the red curve behavior in Figure 17 lies on the fact that if

the CD0 is increased, its increment will impact in a similar amount the three simulated

points. However, if the CDlift inducedis decreased, as in the situation given by the green

curve, the most impacted condition will be when the aircraft is flying at the MTOW since

this portion of the drag is a function of the lift coefficient, consequently it is also a function

of the weight. This makes the curve rotates in relation to the reference one.


3.1.2.2 CLmax Model Evaluation

The CLmax estimation is evaluated using TOFL and LFL results since the distance

calculated for both takeoff and landing is directly affected by the CLmax as presented in

Section 2.2.2. In addition, the public data for TOFL and LFL can be easily found for

different flap settings which allow the verification of the CLmax for more than one flap and

slat deflection.

Considering the V2 equal to 1.2 · VS for TOFL estimation, Equation 2.23 can be

rewritten as

TOFL =2∑

i=0

ki ·[W 2

T· 2.88

ρCLmaxS

]i(3.2)

Now, considering the Vapp equal to 1.23 · VS for LFL estimation, Equation 2.52

can be rewritten as

LFL =2∑

i=0

ki ·[

3.0258 ·WρCLmaxS

]i(3.3)

As illustrated by Equations 3.2 and 3.3, the TOFL and LFL are functions of

the inverse of the CLmax . In addition, the LFL is affected only by the CLmax model. In the

case of TOFL, the estimation is also affected by the engine model, however, the static

thrust for sea level can be compared to the engine manufacturer data in order to isolate

the CLmax influence. A calibration factor can be applied, if necessary, in the ki constants

of the TOFL and LFL models in order to approximate the results to the official values.

3.1.2.3 L/D Model Evaluation

The L/D estimation for the second segment in the takeoff path can be evaluated by

comparing the WAT estimated and the official value for a given altitude and temperature.

As presented in Section 2.2.2, the second segment climb gradient can be calculated using

Equation 2.32.

γ =T

W− 1

L/D(2.32)

The WAT is defined as the takeoff weight at a given altitude and temperature

that is limited by the second segment climb gradient (γ) indicated in the requirement. For

two engine airplanes, γ must be equal or greater than 2.4%. As Equation 2.32 shows, γ

is a function of the thrust and L/D in the second segment. The static thrust for sea level

can be compared to the engine manufacturer data in order to isolate the L/D influence.

Since the γ estimation is very sensitive to variations in L/D, the evaluation of the L/D

must be restricted to the conditions listed in the beginning of Section 3.1 due to the

semi-empirical model limitation and a calibration factor can be applied in this parameter

in order to match the WAT estimation with public data.


3.1.3 Engine Model

As presented in Section 3.1.3, the engine can be modeled by using the network

concept in the SUAVE software or with external data from a different software such as the

GasTurb®. In the first case, the calibration of the engine modifies internal parameters of

its components. In the second case, a calibration factor is applied in the final values of

SFC and thrust or power. The approach to evaluate the different models is similar and

the first step is to gather engine public data. Unfortunately, the engine official values are

the most scarce information necessary to perform the model validation. It is common

to only have access to the design thrust and SFC at a given atmospheric condition and

the static thrust at sea level. The strategy is to evaluate the engine model described in

Section 2.2.4 using the payload range diagram and the TOFL estimations.

The SFC will directly affect the range calculation as illustrated by Equation 3.4

where ct stands for TSFC (Thrust Specific Fuel Consumption). Therefore, the public

payload range must be used in order to evaluate the SFC estimations. In case of reasonable

differences between official data and theoretical results, a calibration factor can be applied

in the overall pressure ratio of the engine model or in the SFC final estimation itself.

R =

∫ Wini

Wini−Wf

1

ct

V

W

CL

CD

dW (3.4)

For the thrust estimation, the public static thrust value must be used to evaluate

the engine model and then the TOFL data must be compared in order to evaluate the

variation of the engine thrust with speed, atmospheric condition and altitude. The reason

why thrust estimation can be evaluated using the TOFL official values is due to the fact

that takeoff distance is a function of the inverse of the thrust as expressed in Equation 3.2

in which public data is available for a range of altitudes and atmospheric conditions. In

case of reasonable differences between official data and theoretical results, a calibration

factor can be applied in the calculated static thrust for takeoff condition.

3.2 Optimization

The general optimization problem is defined by Equation 3.5 as introduced in

Section 2.3, where f0(x) is the objective function, x is the design variables vector and

gk(x) and hi(x) are the constraints.

minimizex

f0(x)

subject to gk(x) ≤ ck, k = 1, . . . , n.

hi(x) = bi, i = 1, . . . ,m.

(3.5)

The strategy to deal with the optimization problem in the scope of this work is

to first define the constraints that must be satisfied by the new design. The geometric


design variables are selected by applying a DoE study. Then the objective function is

evaluated considering fuel burn and MTOW in a pareto analysis. The optimization is

executed considering the engine fixed in order to analyze only geometric trade-offs. The

final optimized geometries are defined considering three different objective functions as

described in Section 3.2.3. The optimization algorithm used is the SLSQP available in

the SciPy package for Python since this optimizer has shown great performance in similar

problems using SUAVE as the modeling software [29]. In addition, this optimizer is open

source and it is a gradient based algorithm capable of handling non-linear constraints.

3.2.1 Constraints

The constraints are based on physical restrictions and in the Top Level Aircraft

Requirements (TLARs) for each aircraft type, and also disciplinary consistency constraints.

Table 8 presents the constraints considered for the optimization problem where TLAR

represents the corresponding performance requirements for the turboprop or the turbofan

aircraft.

Table 8 – Constraints considered for the aircraft optimization

Constraint Bounds

1 Fuel Margin = 02 Minimum Throttle ≥ 03 Maximum Throttle ≤ 14 MZFW Consistency = 06 TOFL for MTOW, SL, ISA ≤ TLAR7 Second Segment Climb Gradient

for MTOW, SL, ISA ≥ TLAR7 LFL for MLW, SL, ISA ≤ TLAR8 Design Range = TLAR9 Time to Climb to Cruise Altitude ≤ TLAR10 Range for Hot and High TO condition ≥ TLAR11 Maximum Fuel Available ≥ TLAR

Source: The Author

The first four constraints are considered in order to guarantee a physical result.

The fuel margin is defined as the landing weight subtracted by the BOW and the maximum

payload and it must be positive in order to ensure that the fuel burned in the design

mission is equal to the available fuel for the harmonic mission. The upper bound in the

throttle prevent the optimizer to select an aircraft that needs more thrust than the engine

is capable of generating and the lower bound is applied to avoid numerical problems.

The MZFW consistency is defined as the MZFW subtracted by the BOW and maximum

payload which must be equal to zero. This strategy is applied since the BOW is a function


of MZFW and an iterative process is needed in order to converge the values of BOW and

MZFW.

The constraints based on the TLARs represent the most critical conditions which

size the aircraft. They were also applied in other similar aircraft optimization problems as

presented by references [29, 30, 31].

3.2.2 Design Variables

The design variables are first selected as the geometric parameters which define

the wing, such as aspect ratio, area, taper ratio, thickness to chord ratio and sweep angle.

In addition, the tails are resized in order to maintain the same tail volume coefficient of

the baseline geometry with the assumption that this strategy will lead to a stable aircraft.

Aiming at evaluating the influence of the selected design variables, a DoE study is

conducted where the fuel burn, MTOW and constraints variations are computed considering

the engine fixed. The definition of DoE is related to a family of numerical methods and

practical guidelines for selecting the trial points in the design space in order to explore

possible tradeoffs. Often, a DoE is executed before setting up the formal optimization

problem since it allows the identification of the key drivers among the potential design

variables [25].

For the present work, the matrix of experiments is defined considering a parametric

study in which only isolated factors are evaluated as shown in Table 9. The goal with

this analysis is to properly define the design variables for each aircraft type.

Table 9 – Matrix of Experiments - Parameter variation

Experiment Wing Aspect Taper t/c SweepNumber Area Ratio Ratio Angle

1 1.05 1.00 1.00 1.00 1.002 1.00 1.05 1.00 1.00 1.003 1.00 1.00 1.05 1.00 1.004 1.00 1.00 1.00 1.05 1.005 1.00 1.00 1.00 1.00 1.056 0.95 1.00 1.00 1.00 1.007 1.00 0.95 1.00 1.00 1.008 1.00 1.00 0.95 1.00 1.009 1.00 1.00 1.00 0.95 1.0010 1.00 1.00 1.00 1.00 0.95

Source: The Author

With the computed value for fuel burn, MTOW and the constraints for each

experiment, the variation of the results with respect to the parameter alteration can be

calculated as shown by Equation 3.6. This information is essential for defining the most


important design variables among the potential ones and allows to reduce the number of

design variables if it is worthwhile. The strategy to deal with the variation sign in the

equation is applied aiming at a more intuitive result interpretation where the sign of the

variation in the performance indicator is maintained.

V ariation =

(∆f(xi)

f(xref )− 1

)/

[abs

(xixref

− 1

)](3.6)

xref Reference values for the design variables

xi Design variables with variations

f(x) Value computed for a performance indicator

In order to compute the correct variation in the performance indicators due to

geometric alteration, some consistencies must be met such as the value of MTOW, MZFW,

BOW and the design range, the distance traveled disregarding the reserve segments. As

presented in Section 3.1.1, the BOW is a function of MTOW and MZFW, which end to

be also functions of the BOW. Therefore, an iterative process is needed in order to find

the values for BOW, MTOW and MZFW. In addition, the distance traveled in the cruise

segment in order to meet the design range is also unknown. The strategy to guarantee

these consistencies is to run an iterative process similar to an optimization procedure for

each element in the matrix of experiments with only constraint functions, which turns to

be a feasilization problem. Applying this approach, the algorithm will try to satisfy the

constraints and the process will finish when it has converged to a feasible solution without

modifying the geometry, only the unknown parameters. In this case, the variables to be

determine for each analyzed aircraft in the matrix of experiment are the MTOW, MZFW

and cruise distance.

3.2.3 Pareto Analysis

For optimization problems with more than one objective in which a conflict is

expected, a pareto strategy can be applied. This approach transforms a multiobjective

problem into a single objective function optimization. In the present work, aggregating

function is used in order to combine the objectives into a single scalar function [32]. Each

objective is multiplied by a weight factor and different values are considered in order to

compute the optimal solutions.

In aircraft design, two main goals is to achieve a low fuel consumption and a light

operating weight since these characteristics have a positive impact on the total costs. In

fact, these two desirable attributes are widely known and are key drivers in the aircraft

sizing as discussed by reference [33]. The challenge in pursuing both characteristics in the

conceptual design phase relies on the fact that they are conflicting objectives. Therefore,


it is expected distinct aircraft designs when considering fuel burn or BOW which can be

compared considering a non-technical criteria such as the costs involved.

As discussed by reference [33], the BOW impacts directly on the production costs

and the fuel consumption is a key parameter for the operational costs. In the present

work just technical criteria are used such as fuel consumption and MTOW. The costs

estimations are purely statistical and outside the scope of this analysis. In addition, the

maximum take-off weight objective can capture tradeoffs between BOW and fuel burn

when it is being optimized, as shown by reference [33]. Therefore, in order to explore the

design space, a pareto analysis is carried out considering as objectives the fuel consumption

and MTOW. The objective function considering aggregating approach is set as,

f0(x) = β ·W ∗fuel burn + (1− β) ·MTOW ∗ (3.7)

Where β is a constant which can range from 0 to 1, W ∗fuel burn is the fuel burn

divided by the reference value and MTOW ∗ is the MTOW divided by the baseline value. An

optimization is executed for each β value desired. Furthermore, for the same β value, more

than one optimization is carried out considering different initial guesses. The data resulting

from the optimization can be used to construct a pareto plot as shown in Figure 18.

Figure 18 – Example of Pareto Plot - BOW vs Block fuel

Source: Bianchi, 2017 [33]

The final optimized geometries used for comparison are the solutions for β equal

to 0, 0.5 and 1. In other words, it is considered the fuel burn and MTOW optimization

cases as well as the case when each of these technical criteria contributes equally to the

objective function.


3.3 Comparison

The comparison between the turbofan and turboprop aircraft considers the refer-

ence airplanes as well as the optimized geometries for β equal to 0, 0.5 and 1, as previously

stated. The results chosen to be compared are the mission parameters, payload range

diagram, specific range, the block fuel and block time. The goal here is beyond just defining

the best aircraft given a criterion but also understanding why a specific configuration is

more competitive in a defined scenario and what are the opportunities for each aircraft

type. The next paragraphs present a discussion on each topic being considered in the

comparison.

The typical mission performed by a turboprop aircraft differs considerably from

the one performed by a turbofan mainly due to the cruise speed and service ceiling which

are consequences of the propulsion type. Therefore, the first comparison must be between

the main mission parameters which here is generally considered as the time to climb to

cruise altitude, service ceiling and cruise speed.

Moreover, the aircraft weights must be also compared since an expressive difference

is observed between these two types of airplanes. In fact, the BOW is a function of the

maximum loads which are related to the aircraft maximum cruise speed. In other words,

the higher the cruise speed, the higher the loads and hence the BOW. Therefore, it is

expected a higher BOW for the turbofan type than the one for turboprop aircraft.

Summarizing, for the mission analysis, the following parameters must be compared

and discussed in order to identify the explanation for the differences observed.

1. Time to climb to cruise altitude

2. Cruise altitude

3. Cruise Speed

4. Payload

5. Fuel Available

6. BOW

7. MTOW

The next step is to analyze the previous parameters using the payload range

diagram and specific range equation. The first approach offers a visual comparison between

the maximum range given a fixed payload. In addition, the impact of each objective

function optimized can also be seen on the payload range diagram, which illustrates the

consequence of the possible design decisions. An example of payload range diagram can

be seen in Figure 17 presented in Section 3.1.2.1, where the impact of the polar drag

variation was discussed.

The instantaneous specific range illustrates the impact of each parameter discussed

before in the aircraft flight performance as given by Equation 3.8 considering the cruise

segment. In addition, the fuel weight flow relates to the TSFC as shown by Equation 3.9

and since the cruise segment is considered 1g trimmed flight, it is possible to write

Equations 3.10 and 3.11.


SR =R

fuel burn=

dR/dt

dmf/dt=V

m(3.8)

m = TSFC · T (3.9)

T = D (3.10)

W = L (3.11)

SR - Specific Range

m - fuel flow

TSFC - Thrust Specific Fuel Consumption

T - Thrust

D - Drag

L - Lift

W - Aircraft Weight

V - Cruise True Speed

Equation 3.8 can be rewrite as Equation 3.12.

SR =V · (L/D)

TSFC ·W=

(V

TSFC

)·(L

D

)·(

1

W

)(3.12)

This equation summarizes the potential of an aircraft in terms of fuel consumption

by linking all its critical parameters. The first term given by the speed and TSFC ratio

relates the engine characteristics with the design speed. The second term is given by the

aerodynamic efficiency and the last one is the inverse of the aircraft weight. By looking at

each term, it is possible to understand the differences calculated in the specific range for

the turbofan and turboprop aircraft.

For the block fuel and block time, the calculation must be done for different ranges

in order to compare the airplanes in distinct scenarios. Indeed, the differences between a

turboprop and turbofan aircraft will vary according to the mission range and hence the

best aircraft for a defined criterion may change with the desired range as well. The values

considered in this analysis for range are 250, 500, 750, 1000, 1250 and 1500 nm in which

the fuel burn in the climb, cruise and descent segment is considered as block fuel. The

block time also takes into account only these segments.

The block fuel directly relates to the operational costs since the fuel represents an

expressive part of the total expenses of an airline [34]. In addition, it is widely known that

the airlines management plan their operation in order to maximize profit which means

to properly size their fleet according to the demand. Therefore, another critic parameter

relating financial planning is the aircraft availability since an airplane that flies faster is

able to do more flights in a day compared to a slower one.

46

4 Results and Discussion

4.1 Turbofan Aircraft Results

4.1.1 Aircraft Model and Calibration

The models described in Section 2.2 and the evaluation procedure presented in

Section 3.1 were used in order to model the E170 aircraft performance. Differences in the

reference values from distinct official sources, such as the documents [26, 5], were noted.

Therefore, it was decided to set as the main reference the specification sheet [5] when the

public information is conflicting for the conditions of interest listed in Section 3.1, and

the document [26] for others conditions in which the public data is scarce since reference

[26] presents detailed information about the E170 jet. The decision was based on the

fact that the specification sheet [5] is the most updated information, however, it presents

limited performance results. All the inputs used to model the E170 jet in SUAVE as well

as the python scripts are indicated in Appendix A.1.

A performance analysis was carried out in order to compute the Payload Range

Diagram, TOFL and LFL. The empty weight breakdown was also computed and the

results are presented in Table 10 as well as the reference value taken from reference [26].

The maximum differential pressure value used was 8.94 psi, which is consistent with the

feasible range discussed in Section 3.1.1.

Table 10 – E170 - Empty Weight Breakdown

Component Weight (kg)

Horizontal tail 646Propulsion 4092

Rudder 116Systems 6573Fuselage 4087

Landing gear 1488Vertical tail 291

Wing 3444

Empty Weight 20737Reference 20736

Difference +1

Source: The Author

The weight model is considered to be satisfactory and validated to be used in the

turbofan aircraft optimization.

Chapter 4. Results and Discussion 47

The results for payload range diagram comparison are presented in Figure 19.

Table 11 presents the differences between the SUAVE calculation and the reference [26]

for E170 AR version. The fuel reserves considered take into account alternate airport and

holding of 30 min.

Figure 19 – E170 AR Payload Range Diagram, FL350, ISA +0°C

Source: The Author

Table 11 – E170 AR Payload Range Diagram, FL350, ISA +0°C

TOW Fuel SUAVE Reference Difference Difference(-) Available (kg) Range (nm) Range (nm) (nm) (%)

MTOW 7060 1381 1340 41 3.1%MTOW 9428 2039 2020 19 0.9%

BOW + Fuel 9428 2320 2362 42 1.8%

Source: The Author

The maximum error calculated was 3.1% which is considered acceptable. The

semi-empirical aerodynamic model for CD prediction was validated, however, modifications

were necessary in the engine model inputs in order to calibrate the SFC values. The control

parameter was the overall pressure ratio of the turbofan which was set to 21:1 in order to

have a SFC of approximately 0.68 at Mach 0.8, FL350 [35]. The design thrust for the same

condition was set to 27550N since this parameter is also an input. The resulting static

thrust at SL was 129 kN, 2.3% bigger than the manufacturer data.


The impact of the engine model limitations can be observed in the take-off field

length estimations. The document [26] presents TOFL charts for ISA +0°C and +15°C,

however, as discussed previously, the TOFL results from source [26] are conflicting with

the value presented by the reference [5] for TOFL at MTOW, SL and ISA +0°C. Therefore,

the strategy applied was to evaluate the curves tendency and WAT with the document

[26]. The reference [5] was used to evaluate the absolute errors only in the conditions of

interest listed in Section 3.1. The comparison between SUAVE results and reference [26]

is presented in Figures 20 and 21 .

The discontinuities in the TOFL curves are due to the second segment climb

gradient requirement. When γ is equal to 2.4%, the TOW is equal to the WAT. For the

aircraft to be able of taking off in a higher TOW, the flap setting must be changed to a

smaller deflection, thus at a lower CLmax . The E170 flap settings for take-off are the flap 4,

2 and 1 [26].

Figure 20 – E170 TOFL, ISA +0°C

Source: The Author


Figure 21 – E170 TOFL, ISA +15°C

Source: The Author

The WAT estimation is given in Table 12 for conditions that represents airports

of interest such as Denver International Airport and Santos Dumont Airport.

Table 12 – E170 - WAT estimation

Condition Flap SUAVE Reference Difference DifferenceSetting WAT (kg) WAT (kg) (kg) (%)

SL, ISA +0°C 4 38600 38600 0 0%6000 ft, ISA +15°C 4 31205 31990 -785 -2.4 %6000 ft, ISA +15°C 2 35850 36000 -150 -0.4 %6000 ft, ISA +15°C 1 37000 37000 0 0%

Source: The Author

The error in the WAT estimation is considered acceptable for this academic

analysis, however, improvements are desirable since 785kg may represent 8 less passengers

in the maximum allowable weight.

As observed, the TOFL curves reasonably captures the tendency presented by the

reference values [26]. The total errors in the TOFL estimation are computed in Table 13


against reference [5] for the conditions of interest. The TOW for 500nm, full PAX, ISA,

SL was calculated using SUAVE considering 1600kg as fuel reserves. The TOFL at Denver

International Airport was extrapolated from the reference values [26] in order to evaluate

the SUAVE estimation at hot and high take-off conditions.

Table 13 – E170 - TOFL estimation

Condition TOW SUAVE Reference Difference Difference(kg) TOFL (m) TOFL (m) (m) (%)

MTOW, SL, ISA +0°C 38600 1626.43 1644 -17.57 -1.1%

TOW for 500nm, full PAX 31406 1123.39 1151 -27.61 -2.4 %SL, ISA +0°CDenver, WAT 36681 3117.05 3094.44 22.61 0.7 %

ISA +23°C, 5433 ft

Source: The Author

Using the same approach as in the TOFL calculation, the LFL was computed

using the document [26] as reference to evaluate the tendencies as presented in Figure 22

for ISA +0°C and flap setting 6. The LFL value for MLW, SL, ISA +0°C was compared

to reference [5] as shown in Table 14.

Figure 22 – E170 LFL, ISA +0°C

Source: The Author


Table 14 – E170 - LFL estimation

Condition TOW SUAVE Reference Difference Difference(kg) LFL (m) LFL (m) (m) (%)

MLW, SL, ISA +0°C 33300 1278.48 1241 37.48 3.02%

Source: The Author

The aerodynamic model for CLmax prediction was validated to be used in the

turbofan optimization since the estimation for TOFL and LFL present errors considered

acceptable for this academic analysis. Although the engine model at SL gives a higher

static thrust, the values for TOFL and range estimations are consistent with public data

and, therefore, the engine model is also validated to be used in the turbofan optimization.

The final values for the parasite drag in which miscellaneous drag are included, the

oswald coefficient and the CDcompressibilityfor cruise condition (Mach = 0.78) considering

the whole aircraft are presented in Table 16. The CLmax estimations for each flap position

is also presented in Table 15. The flap 4 and 5 have the same deflection, however, the first

one is assigned to take-off and the other to landing where the landing gear is considered

extended. The model used is not capable of differentiating the CLmax according to the

landing gear position, hence the values of CLmax for flap 4 and 5 are equal.

Table 15 – E170 - CLmax values

Flap Position CLmax

6 2.7335 2.6244 2.6242 2.1301 1.9890 1.421

Source: The Author

Table 16 – E170 - Aerodynamic estima-tions for drag coefficients

CDp 0.02317CDcompressibility

0.00242e 0.79

Source: The Author


4.1.2 DoE Analysis

A DoE was conducted using the turbofan aircraft model and the E170 aircraft as

the baseline geometry in order to identify the most important geometric design variables in

the aircraft design. In this analysis, the engine is considered fixed. The key characteristics

considered were selected from the performance parameters defined as constraints in

Section 3.2.1. The list below presents the performance indicators chosen to be analyzed

in the DoE study.

Block Fuel

Time to climb to cruise level

Specific Range in the cruise segment

Fuel Burn (block fuel + reserves)

TOFL (MTOW, SL, ISA)

Second Segment Climb Gradient (MTOW, SL, ISA)

The matrix of experiment is the one presented in Section 3.2.2 and the geometric

parameters considered were the area, thickness to chord ratio, taper ratio, sweep angle

and aspect ratio of the main wing. Furthermore, the design mission considers 1340 nm

of range for the block fuel and reserves calculation, which is also known as the harmonic

mission since it represents a mission at MTOW and Full Payload [31]. Table 17 details

each segment modeled using SUAVE for the DoE analysis. For climb and descent segments,

the values presented for airspeed are mean values since both segments are divided into

more subsegments with slightly differences due to atmospheric changes. The profile chosen

for each segment was based on reference [4] and in the tutorials of SUAVE [3] in order

to avoid convergence problem in the mission solver during the analysis and optimization.

The design mission described in Table 17 is presented in Figure 23 in terms of altitude

and distance traveled.


Table 17 – Design Mission

Segment Considerations Fundamental Parameters

Take-off MTOW V2 = 1.2 · VS, SL, ISAMaximum Throttle Flap configuration 4

Climb Constant Throttle, δthrottle = 1.Constant Airspeed CAS = 265kts

Cruise Constant Altitude, h = 35000ftConstant Airspeed TAS = 450kts

Descent Constant Rate, ROD = 2000 ft/minConstant Airspeed CAS = 250kts

Reserve Constant Rate, ROC = 2500 ft/minClimb Constant Airspeed TAS = 268kts

Reserve 140 nm to alternative h = 15000ftCruise airport and 30min loiter Mach = 0.5

Reserve Constant Rate, ROD = 590 ft/minDescent Linear Mach Mach0 = 0.3,Machf = 0.24

Landing MZFW Vref = 1.23 · VS, SL, ISAFlap configuration 6

Source: The Author

Figure 23 – Design Mission

Source: The Author

ROD - Rate of descent

ROC - Rate of Climb

TAS - True Airspeed

CAS - Calibrated Airspeed

h - Altitude


The variations for each element in the matrix of experiments were computed as

described in Section 3.2.2 for the selected performance characteristics and the normalized

results are presented in Table 18 using Equation 3.6. In the table, SSCG stands for

second segment climb gradient.

Table 18 – DoE results for turbofan aircraft

Performance S (m2) AR (-) Taper (%) t/c (%) Sweep Angle (°) MatrixIndicators 72.72 8.6 32.75 -11 -23 Variation

Block Fuel -0.059% 0.152% -0.026% -0.163% 0.141%Time to Climb -0.348% 0.609% -0.174% -0.609% 0.435%Specific Range 0.05% -0.134% 0.032% 0.2% -0.139% -5%Fuel Burn -0.04% 0.163% -0.025% -0.126% 0.097%SSCG -0.553% -4.596% -0.825% 0.556% -1.085%TOFL 0.978% -0.025% 0.144% 0.733% 0.047%

Block Fuel 0.063% -0.16% -0.007% 0.16 % -0.156%Time to Climb 0.348% -0.609% 0.00% 0.522% -0.609%Specific Range -0.023% 0.167% 0.014% -0.167% 0.191% 5%Fuel Burn 0.043% -0.171% -0.007% 0.111% -0.121%SSCG -1.182% 2.323% -0.895% -2.266% -0.631%TOFL -0.569% 0.351% 0.177% -0.337% 0.282%

Source: The Author

As observed, the block fuel, specific range and fuel burn are most affected by the

aspect ratio, thickness to chord ratio and sweep angle. The block fuel, one of the most

important parameters of the present analysis, vary at least ±0.141% if one of these critical

parameters changes ±1%. This result is due to the strong dependency of the drag polar in

relation to these geometric parameters. As expected, the fuel consumption, and therefore

specific range, are mainly impacted by changes in the total drag. The time to climb is

mainly affected by all parameters except by the taper ratio and this behavior is also

explained by the dependency of the drag polar and total lift. The second segment climb

gradient has a small absolute magnitude and hence it is very sensitive to all parameters

since it is a function of the aerodynamic efficiency which varies considerably with both

drag polar and weight in the second segment of the take-off path. While aspect ratio,

thickness to chord ratio and sweep angle directly impact the total drag, the taper ratio

main effect is on the empty weight of the wing which has a minor impact on the total drag

as observed in the previous results. Finally, the TOFL is most affected by wing area and

thickness to chord ratio because these parameters directly impacts on the total lift and

CLmax/BOW respectively.

The previous results can also be interpreted using plots where the focus now is to

identify the most important design variables in the overall variations. Figure 24 presents

the absolute variations for all parameters of interest.


Figure 24 – DoE results for turbofan aircraft

(a) (b)

(c) (d)

(e)

Source: The Author


As observed, the most impacting design variables are the aspect ratio, area,

thickness to chord ratio and sweep angle of the wing. The taper ration presents only

a minor effect on the second segment climb gradient. Therefore, the geometric design

variables chosen to be used in the formal optimization problem will be the aspect ratio,

area, thickness to chord ratio and sweep angle of the main wing.


As discussed in Section 3.2.3, fuel consumption and BOW are widely used

as technical objective function in a formal optimization problem where they represent

conflicting objectives in the aircraft design. For this reason, the MTOW is also an interesting

technical objective function since it is able to integrate fuel consumption and BOW. For a

turbofan airplane, the tradeoffs can be first seen by using a carpet plot which relates two

geometric design variables with fuel burn and BOW.

The figures below present the carper plot considering as parameters the wing area,

aspect ratio, thickness to chord ratio and sweep angle. The data used to construct the

plots were generated without considering the constraints discussed in Section 3.2.1 since

the goal here is to just evaluate the possible tradeoffs in the design space where the conflict

of BOW and fuel burn is highlighted for a turbofan aircraft.

Figure 25 – Carpet plot: Aspect ratio vs. Wing Area

Source: The Author


Figure 26 – Carpet plot: Aspect ratio vs. Sweep Angle

Source: The Author

Figure 27 – Carpet plot: Thickness to chord ratio vs. Sweep Angle

Source: The Author


As expected, better values of fuel consumption are achieved for higher values

of aspect ratio and lower values of wing area. This is explained by the drag polar since

an increase in AR means a decrease in the induced drag while lower wing area values

allow the aircraft to fly at higher values of aerodynamic efficiency during cruise. The

fuel consumption is also decreased for higher values of sweep angle and lower values of

thickness to chord ratio due to a reduction in the compressibility drag. The impact on the

BOW is almost the opposite since higher values of aspect ratio, sweep angle and lower

values of thickness to chord ration tend to increase the empty weight of the wing. With

this information, it is expected wings with higher aspect ratio and sweep angle when the

objective function is fuel burn. On the other hand, higher values of thickness to chord ratio

is expected as solution for MTOW as the objective function. The wing area will play an

important role with the constraints since it directly impacts the TOFL and time to climb.

The expected tradeoffs in the optimization problem can be confirmed using a

pareto strategy as discussed in Section 3.2.3. The formal optimization problem is stated

below for the turbofan aircraft. All the constraint values are based on the E170 performance

estimations.

minimizex

f0(x) = β ·W ∗fuel burn + (1− β) ·MTOW ∗

subject to:

MTOW = BOW + Fuel Burn+Maximum Payload

MZFW = BOW +Maximum Payload

Design Range = 1340 nm

TOFL (MTOW,ISA,SL) ≤ 1520 m

SSCG (MTOW,ISA,SL) ≥ 0.03

Range H&H TO ≥ 1219.8 nm

Time to Climb ≤ 21.5 min

LFL (MLW,ISA,SL) ≤ 1270 m

Maximum Throttle ≤ 1.

Maximum Fuel Available ≥ 9450 kg.

(4.1)

The optimization was carried out considering different values of β and initial

guesses. In the initial trials, the convergence of MTOW, BOW and MZFW were not

implemented using the design variable strategy discussed in Section 3.2.2, in other words,

the values of MTOW and MZFW used to compute the BOW of a given iteration were the

values of the previous one in the optimization process. This scenario may lead to numerical

difficulties since the optimizer is gradient based and the consistency of MTOW, MZFW

and BOW is not guaranteed. Therefore, in order to avoid numerical problems, the MTOW


and MZFW were introduced as design variable which improved the optimizer performance.

The bounds considered for each design variable are presented in Table 19.

Table 19 – Turbofan design variable bounds

Design Variable Lower Bound Upper Bound

Swing 65 m2 80 m2

AR 7.6 9.8t/c 0.09 0.15

Λ1/4c 15° 30°

Source: The Author

The results are presented in Figure 28, where the points that do not belong to

the pareto front are the optimization solutions prior to the design variables modification.

Figure 28 – Pareto Chart

Source: The Author

As expected, the fuel burn and MTOW minimization are conflicting as shown

by the pareto front in Figure 28. Therefore, a tradeoff is confirmed between a high

aerodynamic efficiency configuration versus a solution that also indirectly takes into

account the BOW. The discussion regarding the optimized geometries is given in the next

section.


4.1.4 Optimized Geometries

The geometries chosen to be discussed in more detail are the ones for β equals to

0, 0.5 and 1, as defined previously in Section 3.2.3. Figure 29 presents a comparison

between the optimal solutions and the reference wing.

Figure 29 – Optimized geometries for β equal 1, 0.5 and 0.

Source: The Author

As can be observed, for all cases the optimizer explore solutions with a high

aspect ratio and sweep angle. In fact such characteristics benefit the aerodynamic efficiency

since both induced and compressibility drag are reduced. However, lower values of these

parameters were expected for the MTOW optimization case. The interpretation for this

behavior is that the wing weight model may underestimates its weight. For high aspect

ratio and sweep angle wings, it is expected weight penalties due to aeroelastic restrictions.

The reference [29] also discusses this possible limitation in the SUAVE weight model.

Table 20 presents the detailed results for the optimal solutions in contrast to the E170

jet. The constraint values are also compared.


Table 20 – Optimized geometries for β equal 1, 0.5 and 0.

Design Baseline β = 1 β = 0.5 β = 0.0Variables Aircraft Fuel Burn MTOW

Wing Area [m2] 72.72 75.86 69.94 67.53

Aspect Ratio [-] 8.6 9.8 9.8 9.2

Wing t/c [-] 0.11 0.115 0.131 0.150

Wing Sweep [°] 23 30.0 30.0 30.0

Cruise Distance [nm] 1103.2 1118.3 1118.9 1107.5

MTOW [kg] 37200 37768 37042 36601

MZFW ratio [-] 0.8102 0.8211 0.8163 0.8079

Constraints Bounds

TOFL (MTOW, ISA, SL) ≤ 1520 m 1520 m 1520 m 1517 m

Second Segment Climb ≥ 0.03 0.0432 0.0410 0.0319

Gradient (MTOW, ISA, SL)

Range for H&H TO Condition ≥ 1219.8 nm 1356.1 nm 1355.2 nm 1228.3 nm

(5433 ft, ISA + 23°C)

Time to Climb to FL350 ≤ 21.5 min 19.2 min 19.1 min 20.8 min

Maximum Fuel ≥ 9420 kg 9876.8 kg 9953 kg 11127.5 kg

LFL (MLW, ISA, SL) ≤ 1270 m 1247.8 m 1267.5 m 1270 m

Maximum Throttle ≤ 1 1 1 1

Design Range = 1340 nm 1340 nm 1340 nm 1340 nm

Objective Function Value 1.0044 0.9570 0.9799 0.9839

Fuel Burn (kg) 7091.9 6756.4 6805.9 7032.9

Fuel Burn Variation (%) - -4.73 -4.033 -0.832

MTOW (kg) 37200 37768 37042 36601

MTOW Variation (%) - 1.53 -0.425 -1.610

Source: The Author


For the fuel burn case, the optimal solution has both AR and sweep angle equal

to the upper bound presented in Table 19. As discussed previously, higher AR and sweep

angle lead to higher aerodynamic efficiency in a transonic aerodynamic regime. However,

the drawback in this case is that the BOW is also increased, which causes the MTOW to

increase as well. In order to meet the requirements of take-off performance, the optimizer

increases the wing area proportionally. The thickness to chord ratio is not reasonably

increased as the wing area because a higher thickness to chord increases considerably the

compressibility drag which would penalize the fuel burn consumption. As can be observed

from Table 20, in fact, the most critical constraint was the TOFL for the fuel burn case.

Therefore, it is possible to state that when optimizing fuel burn, it is expected solutions of

higher AR, sweep angle, wing area and MTOW. The maximum reduction in fuel burn

considering the theoretical models used is 4.73% and the increase in MTOW is 1.53%.

The main disadvantage with this approach is that the most efficient airplane in terms of

performance is not the most profitable one as discussed by reference [33]. The optimized

geometry is more complex and this leads to not just an increase in MTOW but also an

increase in the production costs, which may be prohibitive.

For the intermediary case where β is equal to 0.5, the objective function considers

equally the influence of the fuel burn and MTOW. The AR and sweep angle are also

equal to the their upper bound and the TOFL is again the demanding constraint. The

main difference between the fuel burn case and the intermediary one is that the optimizer

explore solutions of higher thickness to chord ratio in order to increase the CLmax instead

of increasing the wing area which causes the BOW to decrease as well as the MTOW. In

fact, a higher thickness to chord negatively impacts the compressibility drag, however, a

lower MTOW is achieved in comparison to the reference aircraft. The fuel burn reduction

was 4.03%, while the MTOW also decreased 0.425%.

Finally, for the MTOW case, the sweep angle and thickness to chord ratio are

equal to the upper bound and the AR is decreased in comparison to the previous optimal

solutions discussed. Moreover, the demanding constraint was the LFL and not the TOFL.

The interpretation for this characteristic is similar to the intermediary case where the

optimizer explore solutions of higher thickness to chord and CLmax instead of increasing

the wing area which causes the MTOW to decrease. The fuel burn reduction is 0.832%

and the MTOW decrease is 1.61%.

Again, the wing weight model may be non-conservative leading to solutions of

higher AR ans sweep angle when compared to the reference aircraft. For the purposes of

this academic work, this condition does not disqualify the analysis, but it is important to

emphasize that the gains presented here may be overestimated. In addition, just technical

objective functions were considered and it is not possible to affirm that the optimal

solutions encountered here are in fact more competitive than the reference aircraft in terms

of profit.


4.2 Turboprop Aircraft Results

4.2.1 Aircraft Model and Calibration

The same strategy applied to the E170 model was conducted for the ATR 72-600

aircraft. In this case, the main reference used in the calibration of the model was the

specification sheet provided by the manufacturer [6]. Furthermore, additional information

available for the ATR 72-500 version was also used to evaluate if the SUAVE model

properly represents the performance tendency of the ATR 72 aircraft [27]. All the inputs

used to model the ATR 72-600 turboprop in SUAVE as well, as the python scripts, are

indicated in Appendix A.2.

The empty weight breakdown was computed and the results are presented in

Table 21. The BOW considered was the one used by the reference [6]. The maximum

differential pressure value used was 6.0 psi, which is consistent with the feasible range

discussed in Section 3.1.1.

Table 21 – ATR 72-600 - Empty Weight Breakdown

Component Weight (kg)

Horizontal tail 314Propulsion 1760

Rudder 95Systems 5602Fuselage 2264

Landing gear 920Vertical tail 236

Wing 2548

Empty Weight 13739Reference 13500

Difference 239

Source: The Author

The computed difference was 1.77% for the empty weight and it was decided to

apply a decrease of 239 kg in the system’s weight in order to equal the SUAVE estimation

for the BOW with public data. The weight model is considered to be satisfactory and

validated to be used in the turboprop aircraft optimization.

The results for payload range diagram comparison are presented in Figure 30.

Table 22 presents the differences between the SUAVE calculation and reference [6] for

ATR 72-600 version. The fuel reserves for each point at the payload range diagram were

determined considering the data available in [27], where the fuel reserves must account for

alternate airport and holding.


Figure 30 – ATR 72-600 Payload Range Diagram, FL210, ISA +0°C

Source: The Author

Table 22 – ATR 72-600 Payload Range Diagram, FL210, ISA +0°C

TOW Fuel SUAVE Reference Difference Difference(-) Available (kg) Range (nm) Range (nm) (nm) (%)

MTOW 2000 494 500 -6 -1.2%MTOW 5000 1660 1655 5 0.3%

BOW + Fuel 5000 1885 1895 -10 -0.5%

Source: The Author

These results were achieved by applying an increase in the parasite drag estimated

by SUAVE for a turboprop. The total increment was 21.4 drag counts in the parasite drag

coefficient in order to approximate the calculation to more realistic values of miscellaneous

drag. The final parasite drag coefficient is 280 drag counts and the oswald coefficient

estimated for the ATR 72-600 is 0.716. The absolute maximum error calculated was 1.2%

which is considered acceptable. The semi-empirical aerodynamic model for CD prediction

was calibrated and the engine model was compared with the data available in [27] which

validated the SFC estimation.

The TOFL model was compared to ATR 72-600 specification sheet in order to

compute the absolute differences. The model was also compared to the ATR 72-500 data

which provided detailed information about TOFL values and thus it could be used to


evaluate the model tendency. The comparison with SUAVE results and reference [27] for

ATR 72-500 is presented in Figure 31 for SL, ISA condition. The absolute differences are

presented in Table 23 considering the ATR 72-600 public data [6].

Figure 31 – ATR 72-600 (SUAVE) vs ATR 72-500 - TOFL, ISA +0°C

Source: The Author

Table 23 – ATR 72-600 - TOFL estimation

Condition TOW SUAVE Reference Difference Difference(kg) TOFL (m) TOFL (m) (m) (%)

MTOW, SL, ISA +0°C 23000 1389.67 1367 22.67 1.7%

TOW for 300nm, full PAX 21200 1192.98 1175 17.98 1.5 %SL, ISA +0°C

TOW for 300nm, full PAX 21200 1438.36 1410 28.36 2.0 %3000 ft, ISA +10°C

Source: The Author

As observed, the TOFL curves reasonably captures the tendency presented by the

reference values [27]. Furthermore, the maximum error for the TOFL was 2.0% which is

considered acceptable. For the WAT estimation, only data related to Denver International

Airport was available. The table below presents the comparison for the second segment

climb gradient estimation considering as TOW the WAT informed by the manufacturer

[6].


Table 24 – ATR 72-600 - Second Segment Climb Gradient γ estimation

Condition Flap SUAVE Reference DifferenceDeflection γ (%) γ (%) (%)

TOW of 21000 kg 15° 2.37 2.40 -1.35333 ft, ISA +23°C

Source: The Author

The aerodynamic model for L/D prediction in the second segment regarding

turboprop aircraft is considered validated due to the results above. The engine model was

not modified since it was compared to the data available in [27].

For LFL, the model was compared and calibrated considering the data presented

in [6]. The results are presented in Table 25.

Table 25 – ATR 72-600 - LFL estimation

Condition TOW SUAVE Reference Difference Difference(kg) LFL (m) LFL (m) (m) (%)

MLW, SL, ISA +0°C 22350 916.55 915 1.55 0.17%

LW with full PAX + reserves 21000 864.96 862 2.96 0.34%SL, ISA +0°C

Source: The Author

The CLmax estimated by SUAVE for both take off and landing flap deflections

(15° and 30°) was increased by a multiplication factor of 1.18 since it was underestimated

by the SUAVE model when compared to data available in [27]. The final results for the

aerodynamic model are presented in Tables 26 and 27. The aerodynamic model for CLmax

prediction was calibrated and validated to be used in the turboprop optimization since

the estimation for TOFL and LFL present errors considered acceptable for this academic

analysis. The engine model is also considered validated as well as the model for drag

prediction.

Table 26 – ATR 72-600 - CLmax values

Flap Deflection CLmax

30° 2.6315° 2.180° 1.63

Source: The Author

Table 27 – ATR 72-600 - Aerodynamic es-timations for drag coefficients

CDp 0.028e 0.716

Source: The Author


4.2.2 DoE Analysis

A DoE was conducted using the turboprop aircraft model and the ATR 72-600

aircraft as the baseline geometry in order to identify the most important geometric design

variables in the aircraft design. In this analysis, the engine is considered fixed. The

key characteristics considered were selected from the performance parameters defined as

constraints in Section 3.2.1. The list below presents the performance indicators chosen to

be analyzed in the DoE study. Here the climb throttle was chosen instead of time to climb

because the climb profile used in the turboprop mission is the ”Constant Rate Constant

Equivalent Airspeed” due to convergence problems in the mission solver, hence, the time to

climb is fixed and the throttle is variant with time.

Block Fuel

Climb Throttle

Available Fuel for Hot & High condition - Denver Airport (Fuel HH)

Fuel Burn (block fuel + reserves)

Second Segment Climb Gradient (MTOW, SL, ISA)

TOFL (MTOW, SL, ISA)

The same considerations for the turbofan aircraft were applied in the turboprop

DoE study. The design mission considers 500 nm of range and Table 28 details each

segment modeled using SUAVE. The rate of climb presented is a mean value since the climb

segment is divided into more subsegments with differences due to atmosphere changes.

The design mission described in Table 28 is presented in Figure 32 in terms of altitude

and distance traveled.


Table 28 – Design Mission

Segment Considerations Fundamental Parameters

Take-off MTOW V2 = 1.143 · VS, SL, ISAMaximum Throttle Flap Deflection 15°

Climb Constant Rate, ROC = 800 ft/minConstant Airspeed EAS = 170kts

Cruise Constant Altitude, h = 21000ftConstant Airspeed TAS = 260kts

Descent Constant Rate, ROD = 2000 ft/minConstant Airspeed CAS = 250kts

Reserve Constant Rate, ROC = 900 ft/minClimb Constant Airspeed TAS = 180kts

Reserve 35 nm to alternative h = 10000ftCruise airport and 45min loiter Mach = 0.38

Reserve Constant Rate, ROD = 800 ft/minDescent Linear Mach Mach0 = 0.3,Machf = 0.2

Landing MZFW Vref = 1.23 · VS, SL, ISAFlap Deflection 30°

Source: The Author

Figure 32 – Design Mission

Source: The Author


The variations for each element in the matrix of experiment were computed as

described in Section 3.2.2 for the selected performance characteristics and the normalized

results are presented in Table 18 using Equation 3.6.

Table 29 – DoE results for turboprop aircraft

Performance S (m2) AR (-) Taper (%) t/c (%) Sweep Angle (°) MatrixIndicators 61 12 53 15 3 Variation

Block Fuel -0.13% 0.16% -0.004% -0.028% 0.0004%Climb Throttle -0.05% 0.23% -0.008% -0.002% 0.0004%Fuel HH -3.50% -6.28% 0.185% -1.361% 0.0063% -5%Fuel Burn -0.16% 0.14% -0.004% -0.029% 0.0004%SSCG -0.63% -1.09% 0.033% -0.235% 0%TOFL 0.81% -0.20% -0.033% 0.224% -0.0003%

Block Fuel 0.16% -0.14% 0.005% 0.037% 0.0001%Climb Throttle -0.09% 0.20% -0.009% -0.011% -0.0005%Fuel HH 3.09% 5.59% -0.182% 1.261% -0.0082% 5%Fuel Burn 0.18% -0.12% 0.005% 0.036% 0.0001%SSCG 0.52% 0.91% -0.036% 0.195% -0.0031%TOFL -0.70% 0.22% 0.040% 0.008% 0.0019%

Source: The Author

As observed, the block fuel, fuel burn and climb throttle are most affected by

the AR and wing area. The block fuel vary at least ±0.13% if one of these critical

parameters changes ±1%. The explanation for this result is similar to the one discussed

in the turbofan DoE. There is a strong dependency of the total drag in relation to these

geometric parameters in a subsonic aerodynamic regime. The SSCG has a small absolute

magnitude and hence it is very sensitive to all parameters since it is a function of the

aerodynamic efficiency which varies considerably with both drag polar and weight in the

second segment of the take-off path. The fuel for hot & high (HH) condition is given by the

TOW, which is a function of the SSCG, minus the payload and BOW. In consequence, the

fuel HH is also a function of SSCG and it is very sensitive to all parameters as well. While

aspect ratio impact the total drag, the taper ratio main effect is on the empty weight of

the wing, which has a minor impact on the total drag as observed in the previous results.

The sweep angle influence is negligible since the aerodynamic regime is subsonic and there

is no compressibility drag. Finally, the TOFL is most affected by area and thickness to

chord ratio because they directly impacts on the total lift, CLmax and BOW.

The previous results can also be interpreted using plots, where the focus now is to

identify the most important design variables in the overall variations. Figure 24 presents

the absolute variations for all parameters of interest. As observed, the most impacting

design variables are the aspect ratio, wing area and thickness to chord ratio and, therefore,

they are chosen to be the design variables.


Figure 33 – DoE results for turboprop aircraft

(a) (b)

(c) (d)

(e)

Source: The Author



The approach used in the turbofan optimization was applied for turboprop opti-

mization and carpet plots were generated in order to evaluate the possible tradeoffs in

the design space, where the conflict of BOW and fuel burn is highlighted for a turboprop

aircraft. Figures 34, 35 and 36 presents the results.

Figure 34 – Carpet plot: Aspect Ratio vs. Wing Area

Source: The Author

Figure 35 – Carpet plot: Aspect Ratio vs. Thickness to Chord Ratio

Source: The Author


Figure 36 – Carpet plot: Thickness to Chord Ratio vs. Wing Area

Source: The Author

Similar to the turbofan, better values of fuel consumption are achieved for higher

values of aspect ratio and lower values of wing area. The fuel consumption is also decreased

for lower values of thickness to chord ratio due to a reduction in the profile drag. The

impact on the BOW is almost the opposite since higher values of aspect ratio and lower

values of thickness to chord ratio tend to increase the empty weight of the wing. With

this information, it is expected wings with higher aspect ratio when the objective function

is fuel burn for the turboprop optimization. On the other hand, lower values of AR are

expected as solution for MTOW as the objective function. The wing area will play an

important role considering the constraints since it directly impacts the TOFL and LFL.


The expected tradeoffs in the optimization problem can be confirmed using a

pareto strategy as discussed in Section 3.2.3. The formal optimization problem is stated

in Equation 4.2 for the turboprop aircraft. All the constraint values are based on the

ATR72-600 performance estimations.

minimizex

f0(x) = β ·W ∗fuel burn + (1− β) ·MTOW ∗

subject to:

MTOW = BOW + Fuel Burn+Maximum Payload

MZFW = BOW +Maximum Payload

Design Range = 500 nm

TOFL (MTOW,ISA,SL) ≤ 1391 m

SSCG (MTOW,ISA,SL) ≥ 0.037

Range H&H TO ≥ 335 nm

Throttle Climb ≤ 1.

LFL (MLW,ISA,SL) ≤ 917 m

Maximum Throttle ≤ 1.

Maximum Fuel Available ≥ 5000 kg.

(4.2)

The optimization was carried out considering different values of β and initial

guesses. In the initial trials, the design variables were the wing area, thickness to chord

ratio and aspect ratio and it was observed that the variations of fuel burn and MTOW

were lower than the ones encountered for the turbofan aircraft. In order to allow more

degrees of freedom in the design, the taper ratio was also included in the optimization as a

design variable. The bounds considered for each design variable are presented in Table 30.

Table 30 – Turboprop design variable bounds

Design Variable Lower Bound Upper Bound

Swing 50 m2 72 m2

AR 10 14t/c 0.11 0.18λ 0.3 0.7

Source: The Author

The results are presented in Figure 37, where the points that do not belong to

the pareto front are the optimization solutions prior to the design variables modification.


Figure 37 – Pareto Chart

Source: The Author

As expected, the fuel burn and MTOW minimization are conflicting, as shown by

the pareto front in Figure 37 for the turboprop aircraft. The main difference between

the turbofan and turboprop optimization was the achievable reduction in both fuel burn

and MTOW since the turboprop based on the ATR 72-600 aircraft has shown to be

closer to the optimal solutions than the E170 jet was considering only technical objective

function. Moreover, a trade-off is also confirmed between a high aerodynamic efficiency

configuration versus a solution that also indirectly takes into account the BOW for the

turboprop airplane. The discussion regarding the optimized geometries is given in the next

section.


4.2.4 Optimized Geometries

The geometries chosen to be discussed in more detail are the ones for β equals to

0, 0.5 and 1, as defined previously in Section 3.2.3. Figure 38 presents a comparison

between the optimal solutions and the reference wing.

Figure 38 – Optimized geometries for β equal 1, 0.5 and 0.

Source: The Author

As can be observed, for the fuel burn case, the optimizer explore geometries with

a high aspect ratio while decreasing the taper ratio in order to decrease the BOW. In fact

such characteristics benefit the aerodynamic efficiency since the induced drag is reduced

due to two reasons. The first one is related to a reduction in the CL which is function

of the MTOW, and the second reason is given by a decrease in the factor k, which is a

function of the AR in the drag polar 2 terms equation. Other impacts are discussed in the

next paragraphs considering the taper ratio influence.

For the intermediary and MTOW cases, the aspect ratio value is lower than the

one encountered for fuel burn case and the taper ratio is further decreased. Table 31

presents the detailed results for the optimal solutions in contrast to the ATR 72-600

aircraft. The constraint values are also compared.


Table 31 – Optimized geometries for β equal 1, 0.5 and 0.

Design Baseline β = 1 β = 0.5 β = 0.0Variables Aircraft Fuel Burn MTOW

Wing Area [m2] 61 62.18 60.56 60.69

Aspect Ratio [-] 12 14.00 12.05 11.81

Taper ratio [-] 0.53 0.385 0.335 0.3

Wing t/c [-] 0.15 0.157 0.153 0.160

MTOW [kg] 23000 23280 22875 22789

MZFW ratio [-] 0.9130 0.9149 0.9124 0.9117

Constraints Bounds

TOFL (MTOW, ISA, SL) ≤ 1391 m 1327.51 m 1306.52 m 1303.13 m

Second Segment Climb ≥ 0.037 0.0464 0.0401 0.0397

Gradient (MTOW, ISA, SL)

Range for H&H TO Condition ≥ 335 nm 732.9 nm 393.3 nm 373.8 nm

(5433 ft, ISA + 23°C)

Maximum Fuel ≥ 5000 kg 5000 kg 5053.7 kg 5524.6 kg

LFL (MLW, ISA, SL) ≤ 917 m 917 m 917 m 917 m

Maximum Throttle ≤ 1. 1 1 1

Design Range = 500 nm 500 nm 500 nm 500 nm

Objective Function Value 1.00453 0.99056 0.99825 0.99081

Fuel Burn (kg) 2009.05 1981.1 2003.9 2013.1

Fuel Burn Variation (%) - -1.39 -0.256 -0.202

MTOW (kg) 23000 23280 22875 22789

MTOW Variation (%) - 1.22 -0.543 -0.917

Source: The Author


For the fuel burn case, the optimal solution has the aspect ratio equal to the

upper bound presented in Table 30. The drawback in this case is that the BOW is also

increased which causes the MTOW to increase as well. On the other hand, the taper

ratio was decreased in comparison to the reference aiming at reducing the BOW while the

thickness to chord ratio had to be increased in order to satisfy the constraint of maximum

fuel available on the wing tanks. The concern here is related to a possible tip stall in which

the sections closer to the tip, where the aileron is usually located, stall first. A lower taper

ratio decreases the chords closer to the tip which causes the local lift coefficient to increase

in this region in comparison to a wing of higher taper ratio. Therefore, the sections closer

to the tip are going to reach the maximum lift coefficient earlier than the others which

causes a tip stall. Moreover, the taper ratio modifies the wing loading for a fixed CL which

may decrease the oswald coefficient for loadings considerably different from the elliptical

one. In consequence, an expressive decrease in the taper ratio may also cause an increase in

the induced drag which is being considered in the aerodynamic model used in the SUAVE

software. For the tip stall problem, there is no implemented constraint that prevents the

optimizer to decrease the taper ratio in other to avoid the tip stall. A strategy to deal

with this problem in future analysis could be the implementation of a constraint in which

the section that reaches the maximum lift coefficient first is geometrically limited. Since

this consideration was not made, the optimized results may be overestimated which is still

acceptable in the scope of this academic analysis.

For the requirement related to the landing performance, the optimizer increases

the wing area proportionally to the increase in MTOW. Therefore, it is possible to state

that when optimizing fuel burn, it is expected solutions of higher AR, wing area and

MTOW. The maximum reduction in fuel burn considering the theoretical models used is

1.39% and the increase in MTOW is 1.22%. The main disadvantage with this approach

is that the most efficient airplane in terms of performance is not the most profitable one

as discussed by reference [33]. The optimized geometry is more complex and this lead to

not just an increase in MTOW but also an increase in the production costs which may be

prohibitive.

For the intermediary case where β is equal 0.5, the AR is lower than value

encountered in the fuel burn case and the LFL is again the demanding constraint. Another

difference between the fuel burn case and the intermediary one is that the optimizer explore

solutions of even lower taper ratio in order to decrease the BOW as well as the MTOW.

The fuel burn reduction was 0.256% while the MTOW also decreased 0.543%.

Finally, for the MTOW case, the thickness to chord ratio is the highest value of all

optimization cases while AR is the lowest. Moreover, the demanding constraint was again

the LFL. The interpretation for this characteristic is that the optimizer explore solutions

of higher t/c and CLmax instead of increasing the wing area which causes the MTOW to

decrease. The fuel burn reduction is 0.202% and the MTOW decrease is 0.917%.


In order to improve the results for all optimization cases, a more complex high

lift device or a bigger flap deflection could be considered to increase the CLmax in the

landing condition. This consideration was not applied in order to maintain the optimized

geometries similar to the reference one where just wing geometric parameters were modified.

Otherwise, the same improvement would have to be taken into account for the turbofan

aircraft as well.

Furthermore, similar to the turbofan optimization, it is important to emphasize

that the results presented here may not represent real achievable gains since some con-

siderations or models are non-conservative. In addition, just technical objective functions

were considered and it is not possible to affirm that the optimal solutions encountered

here are in fact more competitive than the reference aircraft in terms of profit.

4.3 Turbofan and Turboprop Comparison

The E170 jet and the ATR 72-600 compete in the regional aviation market where

both aircraft offer a similar passenger capability while performing in general missions of

medium and short range. This work aims at comparing the optimized geometries of each

airplane in order to define which aircraft type best suits the aviation market given a defined

criterion. As discussed in Chapter 3, the methodology was structured in three steps in

order to avoid skewing of the comparison. The first one was the model calibration using

the public data from the E170 jet and ATR 72-600 aircraft. The modeling environment

used was the SUAVE software in which semi-empirical models were applied in order to

properly represent each aircraft. An optimization was carried out for both E170 and ATR

72-600 as baseline geometries considering different objective functions. The purpose of

this section is to evaluate the optimized geometries following the approach described in

Section 3.3. The comparison first considers the mission parameters, then the payload

load range is also analyzed followed by the specific range, block fuel and block time.

4.3.1 Mission Parameters

As previously introduced, the mission performed by a turboprop and turbofan

differs considerably. Table 32 presents a comparison between the main parameters that

define the mission performance. The values presented in Table 32 are equal for all

geometries since they are performance requirements inputted in the optimization process.

The only two values that change from one aircraft to another is the time to climb and

fuel available. The first one varies for the turbofan airplanes modeled since the mission

implemented considers the climb segment with throttle fixed hence the rate of climb is a

variable for each geometry. For the turboprop aircraft, the mission implemented considers a

climb segment with fixed rate of climb due to convergence problems as previously discussed.

In consequence, the rate of climb is fixed for all geometries considering the design mission


implemented for the turboprop aircraft model.

For the fuel available, the variation is due to geometric changes in the total volume

available on the wing for fuel tanks. Although the maximum fuel constraint enforces a

minimum value for the available fuel, some optimized geometries may present a higher

value. In the following analysis, the maximum fuel available is considered equal to the

baseline geometry in order to isolate the impact due to the empty weight and aerodynamic

efficiency variation.

Table 32 – Mission parameters comparison

Parameter Turbofan Turboprop Difference (abs) Difference (%)

Cruise Altitude (ft) 35000 21000 14000 -40.0Time to Climb* (min) 21.5 32.8 11.3 52.6Cruise Speed (KTAS) 450 260 190 -42.2

Maximum Payload (kg) 9404 7500 1904 -20.2Fuel Available* (kg) 9428 5000 4428 -47.0

Source: The Author

(*) - Time to climb and maximum fuel available vary for each aircraft, the values presented in

the table are based on the E170 and ATR 72-600 airplanes.

As can be observed from Table 32, the cruise altitude of the airplanes are

expressively different. The turbofan cruise altitude is 35000 ft while the turboprop cruise

is at 21000 ft. It is important to emphasize that the turboprop aircraft with the PW127

engine [36, 37] may reach higher altitudes if the TOW is less than the MTOW. Although

some references [27] present cruise altitudes higher than 21000 ft, they perform the

calculation for lower values of TOW. The cruise altitude is a direct consequence of the

propulsion type since a turboprop engine presents a more limited flight envelope than the

one encountered for the turbofan engine as discussed in Section 2.2.4. The main reason

is related to the propeller limitation of mach on the blade tip in order to avoid shock waves

and hence a decrease in the propulsive efficiency. In higher cruise altitudes, the aircraft

mach increases for a fixed true airspeed due to variations on the speed of sound with the

atmosphere. Therefore the blade tip reaches the mach 1 earlier in higher altitudes for a

fixed true airspeed and propeller rotation which limits the turboprop altitude envelope.

Moreover, the power generated by the gas turbine also decreases with altitude which

contributes to the service ceiling definition.

For the cruise speed, the same explanation applies since the turboprop is also

limited in terms of aircraft speed in order to avoid a decrease in the propulsive efficiency.

In addition, the gas turbine for the turboprop engine is not designed to generate the power

required to fly at a higher cruise airspeed. Therefore, the cruise speed is 42.2% higher for

the E170 jet which correspond to a mach 0.78 in the cruise altitude.


The maximum payload is also 20.2% bigger for the turbofan which, in this case,

may be explained by market demands of increased payload capacity. The aircraft can

transport passengers and their luggage as well as cargo which may be part of the airlines

services.

The fuel available in the regional aviation context is mainly a consequence of the

available volume that can be used as fuel tanks in the wing. Since the turbofan wing

volume is bigger, the E170 jet presents a fuel capacity 47% bigger than the one presented

by the ATR 72-600 airplane. The fuel capacity may also be a market requirement since

the airlines may plan to carry more fuel to one airport to another or due to desired routes

of increased distance. In this case, the E170 shows to be a more flexible aircraft since it is

able to carry more fuel or travel longer distances if desired.

Since the time to climb may vary between the turbofan aircraft, Table 33 presents

a detailed information in which the time to climb is given for each optimized geometry in

comparison to the turboprop airplanes.

Table 33 – Time to climb to cruise altitude, MTOW - Comparison

Time to Baseline β = 1 β = 0.5 β = 0.0Climb (min) Aircraft Fuel Burn MTOW

Turbofan, FL350 21.5 19.2 19.1 20.8Turboprop, FL210 32.8 32.8 32.8 32.8Difference (abs) 11.3 13.6 13.7 12.0Difference (%) 52.6 70.8 71.7 57.7

Source: The Author

As can be observed, the optimized geometries for the turbofan airplane present

an increased climb performance when compared to the E170 aircraft. This is mainly due

to an increase in the aerodynamic efficiency since all the optimized geometries present at

least a higher sweep angle than the reference aircraft. In comparison to the turboprop,

the turbofan airplanes overcome its competitor by at least 52.6% in the time to climb

to cruise altitude which is even higher for the turbofan. Therefore, it is possible to state

that the turbofan aircraft is faster than the turboprop considering climb performance and

cruise speed. This increased speed has a price which can be seen in the BOW and MTOW

comparison in Tables 34 and 35 since the speed directly impacts the loads envelope and,

hence, the empty weight as well.


Table 34 – BOW comparison

BOW Baseline β = 1 β = 0.5 β = 0.0(kg) Aircraft Fuel Burn MTOW

Turbofan 20737 21618 20839 20196Turboprop 13500 13802 13371 13272Difference (abs) -7237 -7816 -7468 -6924Difference (%) -34.9 -36.2 -35.8 -34.3

Source: The Author

Table 35 – MTOW comparison

MTOW Baseline β = 1 β = 0.5 β = 0.0(kg) Aircraft Fuel Burn MTOW

Turbofan 37200 37768 37042 36601Turboprop 23000 23280 22875 22789Difference (abs) -14200 -14488 -14167 -13812Difference (%) -38.2 -38.4 -38.2 -37.7

Source: The Author

Both MTOW and BOW present a similar difference between the turboprop and

turbofan aircraft which is consistent with the cruise airspeed of each airplane. As stated

previously, the loads envelope is a function of the aircraft maximum cruise speed which

can be clearly represented by the V-n Diagram required by the aeronautical regulations.

Therefore, an aircraft of higher speed must first pay the price in the BOW since the empty

weight will inherently increase. The aircraft speed also impacts in the engine performance

which will be discussed in Section 4.3.3.

4.3.2 Payload Range Diagram

The payload range diagram offers a visual comparison between the airplanes in

terms of maximum range given a fixed payload. In other words, it is possible to see the

capability of each aircraft type in terms of range as well as what are the impacts of

each optimized geometry on the range estimation considering that all optimized airplanes

present the same maximum fuel available. The fuel reserves were determined considering

reference [27] for the turboprop aircraft. For the turbofan, the reserve mission profile

described in Section 4.1.2 was considered. In addition, the burned fuel during taxi,

take-off and landing was also subtract from the total fuel in order to isolate the exactly

fuel available for climb, cruise and descent.

The payload range diagram considers the design range which takes into account

the distance traveled during climb, cruise and descent. Figure 39 presents the results


for all optimized geometries and for the baseline aircraft. The dotted lines represents the

turboprop airplanes while the others present the results for the turbofan aircraft.

Figure 39 – Payload Range Diagram Comparison

Source: The Author

All aircraft were designed to have the same range for the harmonic mission

considering as performance requirement the values for E170 and ATR 72-600. Therefore,

the points that correspond to MTOW and maximum payload coincide between the airplanes

of same type. As expected, a more expressive variation on the other points can be observed

for the turbofan aircraft since the optimization results showed a maximum gain on the fuel

burn of 4.73% while the reduction for the turboprop was only 1.39%. This means that the

optimized geometries considering a turbofan has a higher aerodynamic efficiency which

allow them to fly further at MTOW and maximum fuel since they burn less fuel per unit

of distance. The same variation on the curves can be observed for the turboprop but it is

less expressive.

Since the maximum fuel available presented in Table 32 is different for each

aircraft, the maximum range is also different. The turbofan is capable of performing a

mission of higher range given a fixed payload when compared to the turboprop aircraft due

to the increased fuel capacity. Moreover, it is widely known that the range is a function

of the aerodynamic efficiency, fuel available, TSFC, TOW and cruise speed. Therefore, it

can be state that the payload range diagram shows the range difference where the fuel

capacity impact is the most highlighted. The impact of the other parameters is discussed

in the next section.


4.3.3 Specific Range

The specific range is considered in this comparison in order to remove the fuel

capacity influence and only to consider the engine, weight and aerodynamic character-

istics impact on the cruise performance. The instantaneous specific range is given by

Equation 4.3 as introduced in Section 3.3.

SR =

(V

TSFC

)·(L

D

)·(

1

W

)(4.3)

The first comparison considers the term given by the ratio between the cruise true

airspeed and the TSFC. For the turboprop aircraft, the TSFC computation was directly

made by taking the thrust at the initial cruise and the fuel weight flow. The design mission

simulated using SUAVE is the same presented in Section 4.2.2. For the turbofan aircraft,

the engine model considers the TSFC as a function of the overall pressure ratio of the

gas turbine, airspeed, altitude and ISA variations. Therefore, the TSFC for the turbofan

airplanes is equal given the same flight condition. The values for each turboprop aircraft

are presented in Table 36.

Table 36 – TSFC comparison

TSFC Baseline β = 1 β = 0.5 β = 0.0(h−1) Aircraft Fuel Burn MTOW

Turbofan 0.7001 0.7001 0.7001 0.7001Turboprop 0.4037 0.4062 0.4041 0.4037Difference (abs) -0.2964 -0.2939 -0.2960 -0.2968Difference (%) -42.3 -42.0 -42.3 -42.4

Source: The Author

The TSFC increases with a decrease in throttle since a throttle less than one at

cruise implies that the turboprop engine deviated from the design point. This does not

mean that the absolute fuel consumption increased since the fuel weight flow is proportional

to the decrease in the thrust required. Therefore, it is observed an increase in the TSFC for

the optimized geometries that presented a reduction in the fuel burn since these results are

due to a decrease in the thrust required in comparison to the reference turboprop aircraft.

Moreover, it can be observed from Table 36 that the TSFC for a turboprop is

at least 42% less than the value computed for a turbofan airplane. This result is one of

the reasons why the common sense believes that the turboprop aircraft consumes less fuel.

In other words, the lower TSFC is usually related to the turboprop aircraft reduced fuel

consumption as the only critical and decisive parameter. However, as previously shown,

the turboprop aircraft is slower than the turbofan. Therefore, in order to evaluate each

propulsion type, the airspeed must be taken into account. The TSFC and true airspeed


ratio can be compared in order to provide such perspective. Table 37 presents the values

of the ratio for the E170 jet and ATR 72-600 aircraft. The TSFC considered for the

turboprop is the one calculated for the ATR 72-600.

Table 37 – True airspeed and TSFC ratio comparison


V [m/s] 231.5 133.76 -97.74 -42.2%

TSFC [h−1] 0.7001 0.4037 -0.2964 -42.3%

TSFC [s−1] 1.945 · 10−4 1.121 · 10−4 8.233 · 10−5 -42.3%V

TSFC[m] 1.1904 · 106 1.1928 · 106 2.4 · 103 0.2%

Source: The Author

Surprisingly, the ratio between the airspeed and TSFC are almost the same for the

turbofan and turboprop aircraft which undermines the common belief that the turboprop

airplane burns less fuel due to the engine exclusively without considering the airframe

integration. Therefore, the explanation for why a turboprop aircraft consume less for a fixed

range must be related to the aerodynamic efficiency or the MTOW. Table 38 presents the

comparison between the aerodynamic efficiency for the turbofan and turboprop aircraft.

Table 38 – L/D comparison

L/D Baseline β = 1 β = 0.5 β = 0.0(-) Aircraft Fuel Burn MTOW

Turbofan 13.76 15.00 14.44 13.58Turboprop 14.66 15.19 14.62 14.51Difference (abs) 0.90 0.19 0.18 0.93Difference (%) 6.54 1.27 1.25 6.85

Source: The Author

It was expected the turbofan L/D to be higher than the value presented by the

turboprop aircraft. Since the missions here are for MTOW, the CL at which the turboprop

flies is higher than it is usually observed in the operation. The cruise altitude also impacts

the CL at cruise and different altitudes are also observed for the ATR72-600 aircraft. These

discrepancies may increase the L/D estimated here for the turboprop airplane. In addition,

since the model calibration considered the final performance estimations, it is possible to

have errors in the engine and aerodynamic model that cancel each other. This does not

compromises the comparison since the match of range captures the main trends, however,

it is important to highlight possible limitations on the models used due to the lack of

detailed and official information.


As expected, all geometries optimized for β equal to 1 and 0.5 presents a higher

aerodynamic efficiency in comparison to the baseline geometry. For β equal to 0, the

decrease in MTOW penalizes the aerodynamic efficiency for both turbofan and turboprop

aircraft. Moreover, the variation in L/D is more expressive for the turbofan airplane which

is consistent with the maximum fuel reduction encountered. For all β values including

the reference aircraft, the turboprop airplane has shown to have a higher aerodynamic

efficiency. However, this difference is small and still not explain the fuel consumption

discrepancy between the turboprop and turbofan airplanes.

Finally, the last term to be compared is the MTOW which was presented in

Table 35. In fact, the discrepancy in the MTOW is expressive as previously discussed. In

order to gather the previous information, Table 39 presents the values for each term in

the specific range equation considering the optimized geometries for β equal to 1.

Table 39 – Specific range comparison - E170 versus ATR 72-600


VTSFC

[m] 1.1904 · 106 1.1928 · 106 2.4 · 103 0.2%

LD

[-] 15.00 15.19 0.19 1.27

1W

[N−1] 2.70 · 10−6 4.38 · 10−6 1.68 · 10−6 38.0

SR [m/N ] 48.21 79.36 31.15 39.3

Source: The Author

As can be observed, the turboprop aircraft flies a higher distance per unit of fuel

weight and the critical parameter in this result is the expressive difference between the

turboprop and turbofan MTOW. The engine impacts the airspeed envelope of the aircraft

which in consequence influences the loads magnitude and also the empty weight. Therefore,

it is possible to state that the engine type choice in an aircraft design indirectly impacts

its fuel burn by impacting first the BOW and MTOW. Of course other factors are of

important influence in the fuel burn calculation, however, the results presented here show

that one of the main reasons why the turboprop burn less fuel than a turbofan aircraft is

due to its low MTOW value.

4.3.4 Block Fuel and Block time

The previous section discussed one of the main reasons why a turboprop aircraft

burns less fuel per unit of distance in contrast to a turbofan airplane for the same passenger

capability. The comparison now focus on the absolute values of block fuel and block time

for different ranges in order to define what is the best aircraft when the criteria is fuel

consumption or block time. Figure 40 and Tables 40, 41 and 41 present the block fuel

for the turboprop and turbofan aircraft as well as the difference calculated in percentage.


The mission profiles considered for each aircraft are the ones presented in Section 4.1.2

and Section 4.2.2. Moreover, it is important to emphasize that the turboprop airplanes

have a smaller payload capacity in comparison to the turbofan aircraft which forces the

turboprop to perform a mission of higher range with less passengers. This is one of the

reasons why the turboprop is not competitive for longer missions since the possible number

of passengers is smaller in contrast to the turbofan airplanes. Although the turboprop

presents this limitation, the block fuel and block time were computed for a maximum

distance of 1500 nm in order to provide an analysis for different scenarios.

Table 40 – Block fuel for turbofan airplanes

Block Baseline β = 1 β = 0.5 β = 0.0Fuel (kg) Aircraft Fuel Burn MTOW

250 nm 1100.1 1063.0 1059.4 1085.7500 nm 2115.1 2013.3 2025.2 2097.7750 nm 3109.9 2948.1 2973.3 3088.71000 nm 4085.4 3867.9 3904.6 4059.81250 nm 5042.6 4773.4 4819.7 5011.71500 nm 5982.3 5665.3 5719.3 5945.5

Source: The Author

Table 41 – Block fuel for turboprop airplanes

Block Baseline β = 1 β = 0.5 β = 0.0Fuel (kg) Aircraft Fuel Burn MTOW

250 nm 661.6 649.4 660.1 663.7500 nm 1252.9 1231.7 1250.0 1256.5750 nm 1836.3 1807.1 1832.1 1841.41000 nm 2412.2 2375.8 2406.7 2418.61250 nm 2980.6 2937.9 2973.8 2988.31500 nm 3541.8 3493.2 3533.6 3550.7

Source: The Author


Table 42 – Block fuel comparison

Difference in Baseline β = 1 β = 0.5 β = 0.0Block Fuel (%) Aircraft Fuel Burn MTOW

250 nm -39.9 -38.9 -37.7 -38.9500 nm -40.8 -38.8 -38.3 -40.1750 nm -41 -38.7 -38.4 -40.41000 nm -41 -38.6 -38.4 -40.41250 nm -40.9 -38.5 -38.3 -40.41500 nm -40.8 -38.3 -38.2 -40.3

Source: The Author

Figure 40 – Block Fuel Comparison

Source: The Author

The differences computed for block fuel are consistent with the difference calculated

in the specific range in Table 39. The turboprop airplanes burn at least 38.2% less than

a turbofan aircraft for the same range. Moreover, it can be observed that the block fuel

difference is lower for the optimized geometries considering β equal to 0 and 0.5 which is

explained by the fact that the turbofan aircraft presented a higher achievable gain in fuel

burn than the optimized turboprop airplanes. These results imply that it is possible to

improve more the turbofan aircraft than the turboprop which may decrease the difference in

fuel consumption and hence increase the competitiveness of the turbofan airplane in terms

of fuel burn. Considering the second criterion, Table 43 presents the values computed for

block time.


Table 43 – Block time comparison

Turbofan (min) Turboprop (min) Difference (abs) Difference (%)

250 nm 41 64 23 57.4%500 nm 74 122 48 64.1%750 nm 108 180 72 67.0%1000 nm 141 237 96 67.9%1250 nm 174 295 121 69.4%1500 nm 208 353 145 70.0%

Source: The Author

As expected, the turbofan aircraft flies faster and therefore its block time is smaller

for a fixed range when compared to a turboprop. The difference is expressive since the

turboprop block time is in average 66% higher than the turbofan block time. The computed

results proves that in fact a turboprop aircraft burns less fuel while flying slower than a

turbofan airplane.

Before defining the best aircraft for a given criterion, it is important to discuss the

context of the regional aviation market. Different demands occur which can be exemplified

by two scenarios. The first one consists of a route that presents a highly intense flux of

passengers such as Belo Horizonte (CNF) to Sao Paulo (GRU). The other is related to

a route of less flux of passengers during the day such as Sao Jose dos Campos (SJK) to

Rio de Janeiro (SDU) in Brazil. The airlines size their fleet in order to properly meet the

demand of each route. This means that they will need a fleet capable of performing more

flights in a day in the first scenario and, in the second scenario, they are able to prioritize

only fuel consumption. In consequence, the block time is a critical parameter for the CNF

to GRU route while for the SJK to SDU route, the main parameter is fuel consumption

only. This interpretation relates the airline profit in each scenario with the technical figures

of merit discussed which is acceptable for this academic analysis. However, the planning

challenge for an airline is more complex and requires appropriate financial models in order

to predict what are the critical parameters for each route in order to maximize profit.

Considering the technical interpretation, it is possible to state that the turbofan

is more competitive in highly intense flux of passengers routes while the turboprop can be

used in the others. Therefore, considering the fuel burn as criterion, the turboprop aircraft

are preferred while the turbofan is the most suitable option when the criterion is block

time.

89

5 Conclusion

This academic work started by first describing the methods used in order to model

an aircraft considering the conceptual design phase scope as presented in Chapter 2. The

main aeronautical disciplines were taken into account aiming at representing the most

important trade-offs faced in aircraft design regarding the regional aviation. The discussion

also included an overview about MDO due to the multidisciplinary nature of the problem.

Most of the models were based on semi-empirical relations available on SUAVE which

inherently requires an appropriate validation using official data from existing aircraft.

Moreover, the goal of the present work was to compare a turboprop and a turbofan

optimized airplanes considering the regional aviation, which also requires model validation

in order to properly represent each aircraft, allowing an impartial analysis. Therefore,

the methodology was structured in order to ensure that the airplane model is reliable. It

consisted of three steps in which the first one was the model calibration, followed by the

optimization and then the comparison between the turbofan and turboprop airplanes.

The baseline geometries were the E170 jet and ATR 72-600 turboprop, which have

a similar passenger capacity. The model validation was conducted considering a maximum

error of 3.5% for performance estimations. The optimization was carried out using the

previous validated models as well as the performance results as constraints in order to

establish the requirements for each aircraft type. Different objective functions were applied

in order to verify the impact on the optimized geometries. It was demonstrated that the

E170 turbofan aircraft has more potential to be improved considering technical objective

functions than the turboprops based on the ATR 72-600. All the results captured the

trends expected and the model accuracy is acceptable in the scope of this analysis.

The comparison between the turboprop and turbofan aircraft lead to the conclusion

that the MTOW discrepancy is the main factor on the fuel burn difference. Furthermore,

the turboprop aircraft is the most preferred when considering as criterion fuel consumption,

while the turbofan airplane is the most competitive in terms of block time. The optimized

geometries presented gains in relation to the reference. Moreover, the block fuel difference

is lower for the optimized airplanes considering the weight factor β in the aggregating

objective function equals to 0 and 0.5, since the turbofan has shown to have more potential

to be improved regarding fuel burn.

In addition to the goals proposed, the development of the present academic work

also allowed the technical improvement of the author which is the primarily objective of

the undergraduation senior thesis. A well-structured knowledge was developed considering

the fundamentals of aircraft design and optimization regarding turboprop and turbofan

aircraft. Therefore, the academic analysis presented here is considered successful on both

technical and educational terms.

90

6 Future Works

The academic analysis developed here has shown opportunities to be improved

or complemented. A weight model verification is suggested for the turbofan considering

wings of high sweep angle and aspect ratio since the model may underestimate the wing

weight in this scenario. For the turboprop aircraft, it is suggested the implementation

of an additional constraint aiming at taking into account the taper ratio impact on the

wing stall propagation. A detailed study on drag prediction regarding turboprop powered

aircraft is also recommended since it can increase the SUAVE model accuracy.

As complementary analysis, it is suggested the optimization of a turbofan con-

sidering a reduced flight envelope similar to a turboprop airplane in order to understand

the impact of airspeed in the MTOW. The turboprop airplane may also be optimized

considering an extended flight envelope similar to a turbofan aircraft aiming at defining

the airspeed ranges in which the turboprop is still more competitive than a turbofan in

terms of fuel consumption. In other words, it is suggested a crossed optimization in which

the turbofan must accomplished a turboprop alike mission and vice versa. It is also sug-

gested the consideration of financial models in order to capture the airlines opportunities.

Moreover, an optimization considering variable engine can also provide more information

about important trade-offs in the aircraft design and it is also recommended for future

work.

91

A Reference Airplanes

This appendix presents the basic information about the reference airplanes. Due

to the amount of files and data, all the models, scripts and their output are available

online on the author’s Github. The official version of the SUAVE software is also available

on the same website. The links are the following:

https://github.com/nevesgeovana/TCC

https://github.com/nevesgeovana/SUAVE

https://github.com/suavecode/SUAVE

For more detailed information about the implementation, the author’s contact is

[email protected].

Appendix A. Reference Airplanes 92

A.1 Turbofan Aircraft: E170 AR

Figure 41 – Embraer E170


Table 44 – E170 AR - Weights

Weights

Maximum Takeoff Weight 37200 kgMaximum Landing Weight 33300 kgBasic Operating Weight 20736 kgMaximum Zero Fuel Weight 30900 kgMaximum Payload 9743 kgMaximum Usable Fuel* 9335 kgMaximum Usable Fuel 11625 l


Table 45 – E170 AR - Performance

Performance

Max Cruise Speed M 0.82Time to climb to FL350** 16 minTakeoff Field Length** (ISA, SL) 1151 mTakeoff Field Length (MTOW, ISA, SL) 1644 mLanding Field Length (MLW, ISA, SL) 1241 mService Ceiling 41000 ftRange*** 2150 nm


(*) Fuel density: 0.803 kg/l (**) TOW for 500 nm, full PAX considering single-class

seating and passengers at 100 kg (***) Full PAX, typical mission reserves


Fig

ure

42

–B

luep

rint

ofE

mbra

erE

170

Sou

rce:

EMBRAER

S.A

.,20

15[26]


A.2 Turboprop Aircraft: ATR-72 600

Figure 43 – ATR 72-600

Source: ATR Aircraft, 2017 [6]

Table 46 – ATR 72-600 - Weights (Basic Version)

Weights

Maximum Takeoff Weight 22800 kgMaximum Landing Weight 22350 kgBasic Operating Weight 13500 kgMaximum Zero Fuel Weight 20800 kgMaximum Payload 7500 kgMaximum Usable Fuel 5000 kg


Table 47 – ATR 72-600 - Performance

Performance

Max Cruise Speed* M 0.45Time to climb to FL250 18 minTakeoff Field Length** (ISA, SL) 1175 mTakeoff Field Length (MTOW, ISA, SL) 1333 mLanding Field Length (MLW, ISA, SL) 914 mService Ceiling 25000 ftRange with max PAX 825 nm


(*) 95% MTOW, ISA, Optimum FL - Estimated considering sound speed at Optimum

FL (**) TOW for 300 nm, full PAX, SL, ISA


Figure 44 – Blueprint of ATR 72


96

Bibliography

1 IATA. 2036 Forecast Reveals Air Passengers Will Nearly Double to 7.8 Bil-lion. 2017. <http://www.iata.org/pressroom/pr/Pages/2017-10-24-01.aspx>. [Online:Accessed 15-April-2018]. Cited on page 1.

2 AEROWEB. Forecast International’s Aerospace Portal: U.S. Commercial Air-craft Fleet 2016. 2016. <http://www.fi-aeroweb.com/US-Commercial-Aircraft-Fleet.html>. [Online: Accessed 15-April-2018]. Cited on page 1.

3 BOTERO, E. M. et al. Suave: An open-source environment for conceptual vehicledesign and optimization. In: 54th AIAA Aerospace Sciences Meeting. [S.l.: s.n.],2016. p. 1275. Cited 2 times on pages 2 and 52.

4 LUKACZYK, T. W. et al. Suave: An open-source environment for multi-fidelity con-ceptual vehicle design. In: 16th AIAA/ISSMO Multidisciplinary Analysis andOptimization Conference. [S.l.: s.n.], 2015. p. 3087. Cited 13 times on pages 2, 5, 6, 7,13, 15, 16, 17, 18, 21, 30, 34, and 52.

5 EMBRAER S.A. E170 AR Specifications. 2017. <https://www.embraercommercialaviation.com/wp-content/uploads/2017/02/Embraer spec 170Web.pdf>. [Online: Accessed 01-May-2018]. Cited 6 times on pages 2, 34, 46, 48, 50,and 92.

6 ATR Aircraft. ATR 72-600 Specifications. 2017. <http://www.atraircraft.com/datas/download center/27/fiche72 27.pdf>. [Online: Accessed 01-May-2018]. Cited 7times on pages 2, 34, 63, 65, 66, 94, and 95.

7 GUDMUNDSSON, S. General Aviation Aircraft Design. [S.l.]: Butterworth-Heinemann, 2013. Cited 11 times on pages 3, 4, 5, 8, 10, 11, 14, 16, 19, 20, and 21.

8 TORENBEEK, E. Advanced aircraft design: Conceptual design, technologyand optimization of subsonic civil airplanes. [S.l.]: John Wiley & Sons, 2013. Citedon page 3.

9 RAYMER, D. Aircraft Design: A Conceptual Approach. [S.l.]: American Instituteof Aeronautics and Astronautics, Inc., 2012. Cited on page 3.

10 TORENBEEK, E. Synthesis of subsonic airplane design: an introductionto the preliminary design of subsonic general aviation and transport aircraft,with emphasis on layout, aerodynamic design, propulsion and performance.[S.l.]: Springer Science & Business Media, 1976. Cited 2 times on pages 3 and 21.

11 ANDERSON, J. D. Aircraft performance and design. [S.l.]: WCB/McGraw-HillBoston, MA, 1999. v. 1. Cited 7 times on pages 3, 24, 25, 26, 27, 28, and 29.

12 ROSKAM, J. Airplane design. [S.l.]: DARcorporation, 1985. Cited on page 3.

13 OWENS, D. Weissinger’s model of the nonlinear lifting-line method for aircraft design.In: 36th AIAA Aerospace Sciences Meeting and Exhibit. [S.l.: s.n.], 1998. p. 597.Cited on page 6.

http://www.iata.org/pressroom/pr/Pages/2017-10-24-01.aspx

http://www.fi-aeroweb.com/US-Commercial-Aircraft-Fleet.html

http://www.fi-aeroweb.com/US-Commercial-Aircraft-Fleet.html

https://www.embraercommercialaviation.com/wp-content/uploads/2017/02/Embraer_spec_170_Web.pdf



http://www.atraircraft.com/datas/download_center/27/fiche72_27.pdf

http://www.atraircraft.com/datas/download_center/27/fiche72_27.pdf

Bibliography 97

14 WEISSINGER, J. The lift distribution of swept-back wings. 1947. Cited on page 6.

15 KROO, I. and Shevell, R., A241A Course Notes: Aircraft Design, Synthesis andAnalysis. <http://adl.stanford.edu/aa241/Home.html>. Acessed: June 16, 2018. Cited 3times on pages 6, 8, and 21.

16 SHEVELL, R. S.; SHEVELL, R. S. Fundamentals of flight. [S.l.]: Prentice HallEnglewood Cliffs, NJ, 1989. v. 2. Cited 4 times on pages 6, 7, 8, and 21.

17 ADMINISTRATION, F. F. A. 14 CFR Part 25 - AIRWORTHINESS STAN-DARDS: TRANSPORT CATEGORY AIRPLANES. [S.l.: s.n.], 2013. Cited onpage 9.

18 AIRPLANES, W. B. B. C. Jet Transport Performance Methods. [S.l.]: BoeingCompany, 2009. Cited 2 times on pages 9 and 14.

19 MATTINGLY, J. D. Elements of gas turbine propulsion. [S.l.]: McGraw-Hill,1996. Cited 3 times on pages 24, 26, and 30.

20 Maerkang. Components of jet engines. 2009. <http://maerkang.org/propeller-engine-schematic.html>. [Online: Accessed 24-June-2018]. Cited 2 times onpages 25 and 27.

21 Medium - Images. Stop Disrespecting the Turboprop. 2017. <https://medium.com/war-is-boring/stop-disrespecting-the-turboprop-c00acd3fff3a>. [Online: Accessed24-June-2018]. Cited on page 29.

22 Cantwell, B. J. AA210A Course Notes. <http://web.stanford.edu/˜cantwell/AA210A Course Material>. [Online: Accessed 24-June-2018]. Cited on page 30.

23 GREITZER, E. et al. N+ 3 aircraft concept designs and trade studies, final reportvolumes 1 and 2. Rep. CR—2010-216794, NASA, Hampton, VA, 2010. Cited onpage 30.

24 VANDERPLAATS, G. N. Numerical optimization techniques for engineeringdesign. [S.l.]: Vanderplaats Research and Development, Incorporated, 2001. Cited onpage 31.

25 Willcox, K. and Weck, O. MIT - Multidisciplinary System DesignOptimization. <https://ocw.mit.edu/courses/institute-for-data-systems-and-society/ids-338j-multidisciplinary-system-design-optimization-spring-2010/>. [Online: Accessed01-April-2018]. Cited 2 times on pages 32 and 41.

26 EMBRAER S.A. Commercial Aviation - E170-E1 APM. 2015. <https://www.embraercommercialaviation.com/wp-content/uploads/2017/02/APM E170.pdf>. [Online:Accessed 01-May-2018]. Cited 9 times on pages 34, 35, 46, 47, 48, 49, 50, 92, and 93.

27 ATR Aircraft. ATR 72-600 - Flight Crew Operating Manual. 1997. Cited 7times on pages 34, 63, 64, 65, 66, 79, and 81.

28 Chris Lomax. Cabin Pressurization. <http://mrcabinpressure.com/aircraft.htm>.[Online: Accessed 01-July-2018]. Cited on page 36.

http://adl.stanford.edu/aa241/Home.html

http://maerkang.org/propeller-engine-schematic.html

http://maerkang.org/propeller-engine-schematic.html

https://medium.com/war-is-boring/stop-disrespecting-the-turboprop-c00acd3fff3a

https://medium.com/war-is-boring/stop-disrespecting-the-turboprop-c00acd3fff3a

http://web.stanford.edu/~cantwell/AA210A_Course_Material

http://web.stanford.edu/~cantwell/AA210A_Course_Material

https://ocw.mit.edu/courses/institute-for-data-systems-and-society/ids-338j-multidisciplinary-system-design-optimization-spring-2010/

https://ocw.mit.edu/courses/institute-for-data-systems-and-society/ids-338j-multidisciplinary-system-design-optimization-spring-2010/

https://www.embraercommercialaviation.com/wp-content/uploads/2017/02/APM_E170.pdf

https://www.embraercommercialaviation.com/wp-content/uploads/2017/02/APM_E170.pdf

http://mrcabinpressure.com/aircraft.htm

Bibliography 98

29 WENDORFF, A.; BOTERO, E.; ALONSO, J. J. Comparing different off-the-shelfoptimizers’ performance in conceptual aircraft design. In: 17th AIAA/ISSMO Multi-disciplinary Analysis and Optimization Conference. [S.l.: s.n.], 2016. p. 3362. Cited3 times on pages 40, 41, and 60.

30 SILVA, C. R. Ilario da; ORRA, T. H.; ALONSO, J. J. Multi-objective aircraft designoptimization for low external noise and fuel burn. In: 58th AIAA/ASCE/AHS/ASCStructures, Structural Dynamics, and Materials Conference. [S.l.: s.n.], 2017.p. 1755. Cited on page 41.

31 VOS, M. H. R. System-level assessment of tail-mounted propellers for regional aircraft.In: 31st Congress of the International Council of the Aeronautical Sciences.[S.l.: s.n.], 2018. Cited 2 times on pages 41 and 52.

32 COELLO, C. A. C. C. A short tutorial on evolutionary multiobjective optimiza-tion. In: SPRINGER. International Conference on Evolutionary Multi-CriterionOptimization. [S.l.], 2001. p. 21–40. Cited on page 42.

33 BIANCHI, D. H. D.; ORRA, T. H.; SILVESTRE, F. J. Evaluation of the impacts ofobjective function definition in aircraft conceptual design. Journal of Aircraft, AmericanInstitute of Aeronautics and Astronautics, v. 55, n. 3, p. 1231–1243, 2017. Cited 4 timeson pages 42, 43, 62, and 77.

34 A4A. Passenger Airline Cost Index (PACI). <http://airlines.org/dataset/a4a-quarterly-passenger-airline-cost-index-u-s-passenger-airlines/>. [Online: Accessed 20-October-2018]. Cited on page 45.

35 GE Aviation. CF34-8C Data Sheet. <https://www.geaviation.com/sites/default/files/datasheet-CF34-8E.pdf>. [Online: Accessed 01-July-2018]. Cited on page 47.

36 A4A. EASA TYPE-CERTIFICATE DATA SHEET, PW 100-150 Series.<https://www.easa.europa.eu/sites/default/files>. [Online: Accessed 10-August-2018].Cited on page 79.

37 A4A. Pratt Whitney Canada, PW 100-150 Series. <https://www.pwc.ca/en/products-and-services/products/regional-aviation-engines/pw100-150>. [Online: Accessed10-August-2018]. Cited on page 79.

http://airlines.org/dataset/a4a-quarterly-passenger-airline-cost-index-u-s-passenger-airlines/

http://airlines.org/dataset/a4a-quarterly-passenger-airline-cost-index-u-s-passenger-airlines/

https://www.geaviation.com/sites/default/files/datasheet-CF34-8E.pdf

https://www.geaviation.com/sites/default/files/datasheet-CF34-8E.pdf

https://www.easa.europa.eu/sites/default/files

https://www.pwc.ca/en/products-and-services/products/regional-aviation-engines/pw100-150

https://www.pwc.ca/en/products-and-services/products/regional-aviation-engines/pw100-150

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FEDERAL UNIVERSITY OF MINAS GERAIS Aerospace Engineering ... · manufacturer EMBRAER S.A., and the ATR-72 600 turboprop aircraft, designed by the ATR Company. Both airplanes are used