This work was funded by the US Federal Aviation Administration (FAA) Office of Environment and Energy as a part of ASCENT Project 12 under FAA Award Number: 13-C-AJFE-SU-003. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA or other ASCENT Sponsors.

Project 12

Aircraft Design and Performance Connectivity with AEDT

Lead investigator: J. J. Alonso, Stanford UniversityProject manager: A. Jardines, FAA

April, 2016

Methods – Advanced Aircraft

A strut-braced aircraft was optimized by linking SUAVE with several other tools, such as NASTRAN and SU24.

Conclusions and Next Steps

We have create a link between AEDT and SUAVE and a variety of aircraft that may be used with that link. This demonstrates that future aircraft modeled in SUAVE can be represented in AEDT. Future work may include incorporating noise models or improving fits by using BADA 4 methods.

Motivation and Objectives

AEDT relies on models of aircraft performance supplied to it by external aircraft modeling capabilities for the quantification of fuel burn, emissions, and noise at the source (the aircraft being considered), as well as the trajectories to be flown by said aircraft. This project consisted of three tasks. The first was to create an interface between AEDT and SUAVE1, which is an aircraft design tool under development at Stanford with external partners. The next was to create a database of AEDT aircraft that is representative of aircraft flying today (in all five aircraft classes and with specific models as needed) within SUAVE. Finally we were to demonstrate the capability of SUAVE to generate an advanced aircraft to show the potential value of creating the above interface.

Methods – SUAVE-AEDT Interface

The data needed for AEDT files (ASIFs), primarily performance coefficients, is generated by running SUAVE aircraft through missions as prescribed by BADA and ANP documentation2,3. Other BADA and ANP data is directly input as needed. BADA 3 is used here.

References

1T. Lukaczyk, A. Wendorff, E. Botero, T. MacDonald, T. Momose, A. Variyar, J. M. Vegh, M. Colonno, T. Economon, J. J. Alonso, T. Orra, C. Ilario, "SUAVE: An Open-Source Environment for Multi-Fidelity Conceptual Vehicle Design", 16th AIAA Multidisciplinary Analysis and Optimization Conference, Dallas, TX, June 2015.2EUROCONTROL, “Base of Aircraft Data (BADA Aircraft Performance Modelling Report).” March 2009.3Boven, M.W.P Van. “Airport Noise Modelling: Improvement of Airbus Aircraft Representation in the Integrated Noise Model.” Report of Final Thesis Research at Aerospatiale Metra Airbus Acoustics and Environment Department. August 1999.4T. D. Economon, F. Palacios, S. R. Copeland, T. W. Lukaczyk, and J. J. Alonso, "SU2: An open- source suite for multiphysics simulation and design," AIAA Journal, 54(3):828-846, 2015. doi: 10.2514/1.J053813.5Boeing Airplane Characteristics for Airport Planning,” http:// http://www.boeing.com/commercial/airports/plan_manuals.page”,December 2010

ICAO Aircraft Class Aircraft chosen

Regional Jet CRJ900

Single Aisle B737-800

Small Twin Aisle B767-300ER

Large Twin Aisle B777-200ER

Very Large Aircraft B747-400

Methods – Aircraft Library

Standard aircraft libraries were generated in Python using publically available data including the Boeing airport planning guide5. This was done in SUAVE using SUAVE’s standard format.