Optimization of the Propulsion System for a

“Silent Aircraft”

David Benveniste

Vai-Man LeiAlexis Manneville

Presentation Outline

• Project presentation • Simulation model • Gradient-based optimization

• Heuristic search technique • Multi-objective optimization

• Conclusion & Future work

The “Silent Aircraft” project

• Objective – Reduce aircraft noise below background noise in a well populated area

• Motivation – Improve quality of life near airports – Reduce costs associated with noise (airport taxes, soundproofing …) – Enable growth of air transportation

• Approach – Design an aircraft with noise as a prime objective applying

revolutionary concepts and using advanced technologies for noise reduction

This project• Focus on propulsion system design (principal noise source at takeoff)

• Multidisciplinary system design problem – Acoustics – Engine design and performance analysis – Takeoff flight dynamics (trajectory) – Cost, Range, Weight

• Revolutionary concepts – Ultra-High Bypass ratio – Multiple fans engines – “Distributed exhaust” (many small engines)

• Objectives – Assess these concepts using MSDO – Understand tradeoffs between noise and performance

Simulation model• Design Variables

– Bypass ratio (BPR) : D

– Fan pressure ratio (FPR) : Sf

– Total Thrust : Ftot [N] – Number of engines : Neng

– Number of fans per engine : Nfan

• Potential objectives / Constraints– Takeoff Noise level : LTO [EPNdB] – Relative Range variation : ' rR [%] – Relative Cost variation : ' rC [%] – Relative Weight variation : ' rW [%]

Simulation model

D Sf Ftot NengNfan

Cycle Analysis

Off-design Performance

Takeoff Flight Dynamics

Range

Cost

Noise

Weight LTO ' rR ' rC ' rW

Optimizer

NASA ANOPP

Simulation model

• Benchmarking – Need details of the aircraft configuration

• Aerodynamic coefficients • Engine cycle settings • Noise Certification conditions

� Assume relative variations are well predicted – Baseline configuration: BWB

• ref: Configuration Control Document-2 (01/26/1996)

F

D� �����Sf �����

totN ��N

eng

fan ��

� 801 kN

LTO �104.8 EPNdB ' rR �� ' rC �� ' rW ��

Gradient Based Method

g

•iSIGHT SQP algorithm

Minimize LTO(x) s.t.

1 (x) = – ' R – 0.5 ˺ 0r

g2 (x) = ' W – 0.2 ˺ 0r

g��(x) = ' C – 0.2 ˺ 0r

•Design Variables bounds:•15 < D�< 50 •1.1 < Sf < 2 •600 kN < Ftot < 1,000 kN

•Constant Integer Design Variables •N ��eng•Nfan ��

BPR FPR Total Thrust (N)

TO Noise (EPNdB)

relative 'Range

relative 'Cost

relative 'Weight

BWB baseline 19.5 1.37 800967 104.8 ~0% ~0% ~0%

“Optimized” design 37.1 1.2 998760 100.3 +2% +13% +20%

Sensitivity Analysis

wJ x *

* calculated with forward differencing with 'x* = 0.002x** wx J

-0.348 D ( -0.050

Sf 1.045

Normalized sensitivity Design varibles F (Total thrust) (optimized) Bypass ratio)

(Fan pressure ratio)

Low influence of bypass ratio is unexpected

Scaling

FPR~O(1)

BPR~O(10)

Total Thrust~O(106)

16% reduction in no.Rough scale of 10-6 for of iterationTotal Thrust

Scaling with the Hessian matrix

§¨

~ 5 F ·§¨ ¨

· 10�F 9% further reduction¸ ¨ ~ 7

¸ ¹¸D10�©¹

¸D© in no. of iteration

Heuristic Search Technique

GA findings

•By far more efficient than gradient based optimization

•Very expensive: simulations limited to a few thousands runs (several hours)

•Very likely to give a global optimum (hit most of the constraints)

GA resultsOptimal solution

Subpopulation size Number of islands Number of generations Mutation rate (%) Total Thrust

Fan pressure ratio Number of engines Fans per engines ' rR

' rC ' rW Total Noise (dB) Number of simulations

15 3 100 5 1,023,189 N 59.94 1.128 1 8 -15.9 +19.9 +14.4 66.505 4500 5:33

Bypass Ratio

Computation time (h:mn)

120

100

80

60

40

20

0 total noise (dB)

BWB baseline 1% MR, 100 runs 1.05% MR, 600 runs 2% MR, 1200 runs 5% MR, 4500 runs

Active constraint

Objective

Multi objective optimization

• Second objective: range, competing with noise from the GA results.

• Pareto front: to get the Pareto front we used the GA with different weighs and scaling factors

• Too computationally expensive to run a real full factorial experiment.

Pareto Front

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

60 70 80 90 100 110

Noise (EPNdB)

Rel

ativ

e ra

nge

varia

tion

(%)

1-l=11-l=0.99991-l=0.9991-l=0.991-l=0.81-l=0.51-l=0.21-l=0.01best

From all our runs we extracted the non dominated (feasible) solutions to obtain our best possible estimate of the Pareto front (in black)

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

60 70 80 90 100 110

Noise (EPNdB)R

elat

ive

rang

e va

riatio

n (%

) best

pareto

4000randomruns

Conclusion

•Gradient based method trapped easily at local minimum

•GA gives a credible global optimum

•Objectives of minimizing noise and maximizing range are opposingwith noise dominating

•Ultra-High Bypass ratio and Multiple fans engines can achieve significant noise reduction (~35dB)

Future work

•Data to validate modules

•Improve weight and cost modules

•Multi-objective optimization with cost, more realistic constraints

•Include other noise sources (airframe,…)

•Study effect of other design variables (Tt4, …)