Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-17T21:30:15.219Z Has data issue: false hasContentIssue false
  • English
  • Français

Coupled aeropropulsive design optimisation of a boundary-layer ingestion propulsor

Published online by Cambridge University Press:  31 October 2018

Justin S. Gray Open the ORCID record for Justin S. Gray [Opens in a new window]  and
Joaquim R. R. A. Martins
Justin S. Gray*
Affiliation:
PSA BranchNASA Glenn Research CenterCleveland, OH USA Department of Aerospace EngineeringUniversity of Michigan Michigan, USA
Joaquim R. R. A. Martins
Affiliation:
Department of Aerospace EngineeringUniversity of Michigan Michigan, USA
*
*justin.s.gray@nasa.gov
Get access Rights & Permissions [Opens in a new window]

Abstract

Airframe–propulsion integration concepts that use boundary-layer ingestion (BLI) have the potential to reduce aircraft fuel burn. One concept that has been recently explored is NASA’s STARC-ABL aircraft configuration, which offers the potential for fuel burn reduction by using a turboelectric propulsion system with an aft-mounted electrically driven BLI propulsor. So far, attempts to quantify this potential fuel burn reduction have not considered the full coupling between the aerodynamic and propulsive performance. To address the need for a more careful quantification of the aeropropulsive benefit of the STARC-ABL concept, we run a series of design optimisations based on a fully coupled aeropropulsive model. A 1D thermodynamic cycle analysis is coupled to a Reynolds-averaged Navier–Stokes simulation to model the aft propulsor at a cruise condition and the effects variation in propulsor design on overall performance. A series of design optimisation studies are performed to minimise the required cruise power, assuming different relative sizes of the BLI propulsor. The design variables consist of the fan pressure ratio, static pressure at the fan face, and 311 variables that control the shape of both the nacelle and the fuselage. The power required by the BLI propulsor is compared with a podded configuration. The results show that the BLI configuration offers 6–9% reduction in required power at cruise, depending on assumptions made about the efficiency of power transmission system between the under-wing engines and the aft propulsor. Additionally, the results indicate that the power transmission efficiency directly affects the relative size of the under-wing engines and the aft propulsor. This design optimisation, based on computational fluid dynamics, is shown to be essential to evaluate current BLI concepts and provides a powerful tool for the design of future concepts.

Keywords

Aerodynamicspropulsiongradient-based optimisationboundary-layer ingestionturboelectric propulsion
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1259 , January 2019 , pp. 121 - 137
Copyright
© Royal Aeronautical Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Smith, A.M.O. and Roberts, H.E. The jet airplane utilizing boundary layer ingestion for propulsion, J Aeronautical Sciences, 1947, 14, (2), pp 97–109.Google Scholar
2. Wislicenus, G.F. Hydrodynamics and propulsion of submerged bodies, J American Rocket Society, 1960, 30, pp 1140–1148.Google Scholar
3. Betz, A. Introduction to the Theory of Flow Machines . Pergamon Press, 1966. London.Google Scholar
4. Gearhart, W.S. and Henderson, R.E. Selection of a propulsor for a submersible system, J Aircraft, 1966, 3, (1), pp 84–90.Google Scholar
5. Smith, L.H. Wake ingestion propulsion benefit, J Propulsion and Power, 1993, 9, (1), pp 74–82.Google Scholar
6. Drela, M. Power balance in aerodynamic flows, AIAA J, 2009, 47, (7), pp 1761–1771, doi: 10.2514/1.42409.Google Scholar
7. Felder, J.L., Kim, H.D. and Brown, G.V. Turboelectric distributed propulsion engine cycle analysis for hybrid-wing-body aircraft, 47th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, AIAA 2009-1132, 2009, doi: 10.2514/6.2009-1132.Google Scholar
8. Drela, M. Development of the D8 transport configuration, 9th AIAA applied aerodynamics conference, AIAA 2011-3970, 2011, doi: 10.2514/6.2011-3970.Google Scholar
9. Liu, C., Doulgeris, G., Laskaridis, P. and Singh, R. Thermal cycle analysis of turboelectric distributed propulsion system with boundary layer ingestion, Aerospace Science and Technology, 2013, 27, (1), pp 163–170.Google Scholar
10. Laskaridis, P., Pachidis, V. and Pilidis, P. Opportunities and challenges for distributed propulsion and boundary layer ingestion, Aircr Engineering and Aerospace Technology, 2014, 86, (6), pp 451–458.Google Scholar
11. Welstead, J.R. and Felder, J.L. Conceptual design of a single-aisle turboelectric commercial transport with fuselage boundary layer ingestion, 54th AIAA aerospace sciences meeting, AIAA 2016-1027, 2016, doi: 10.2514/6.2016-1027.Google Scholar
12. Hardin, L., Tillman, G., Sharma, O., Berton, J. and Arend, D. Aircraft system study of boundary layer ingesting propulsion, 48th AIAA/ASME/SAE/ASEE joint propulsion conference and exhibit, AIAA-2012-2993, 2012, doi: 10.2514/6.2012-3993.Google Scholar
13. Uranga, A., Drela, M., Greitzer, E.M., Hall, D.K., Titchener, N.A., Lieu, M.K., Siu, N.M., Casses, C., Huang, A.C., Gatlin, G.M. and Hannon, J.A. Boundary layer ingestion benefit of the D8 transport aircraft, AIAA J, 2017, 55, (11), pp 3693–3708.Google Scholar
14. Gray, S., Mader, J., Kenway, C.A., , G.K.W. and Martins, J.R.R.A. Modeling boundary layer ingestion using a coupled aeropropulsive analysis, AIAA J Aircr, 2018, 55, pp 1191–1199.Google Scholar
15. Gray, J.S., Hearn, T.A., Moore, K.T., Hwang, J.T., Martins, J.R.R.A. and Ning, A. “Automatic Evaluation of multidisciplinary derivatives using a graph-based problem formulation in OpenMDAO, 15th AIAA/ISSMO multidisciplinary analysis and optimization conference, American Institute of Aeronautics and Astronautics, August 2014, doi: 10.2514/6.2014-2042.Google Scholar
16. Hwang, J.T. and Martins, J.R.R.A. A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives. ACM Transactions on Mathematical Software, 2018, 44, (4), Article 37.Google Scholar
17. Lyu, Z., Kenway, G.K.W. and Martins, J.R.R.A. Aerodynamic shape optimization investigations of the common research model wing benchmark, AIAA J, 2015, 53, (4), pp 968–985.Google Scholar
18. Lyu, Z., Kenway, G.K., Paige, C. and Martins, J.R.R.A. Automatic differentiation adjoint of the Reynolds-averaged Navier–Stokes equations with a turbulence model, 21st AIAA computational fluid dynamics conference, San Diego, CA, July 2013, doi: 10.2514/6.2013-2581.Google Scholar
19. Martins, J.R.R.A. and Hwang, J.T. Review and unification of methods for computing derivatives of multidisciplinary computational models, AIAA J, 2013, 51, (11), pp 2582–2599.Google Scholar
20. Coder, J.G., Pulliam, T.H., Hue, D., Kenway, G.K. and Sclafani, A.J. Contributions to the 6th AIAA CFD drag prediction workshop using structured grid methods, AIAA SciTech Forum, American Institute of Aeronautics and Astronautics, January 2017. doi: 10.2514/6.2017-0960.Google Scholar
21. Kenway, G.K.W., Secco, N.R., Martins, J.R.R.A., Mishra, A. and Duraisamy, K. An efficient parallel overset method for aerodynamic shape optimization, Proceedings of the 58th AIAA/ASCE/AHS/ASC structures, structural dynamics, and materials conference, AIAA SciTech Forum, January 2017, doi: 10.2514/6.2017-0357.Google Scholar
22. Kenway, G.K., Kennedy, G.J. and Martins, J.R.R.A. A CAD-free approach to high-fidelity aerostructural optimization, Proceedings of the 13th AIAA/ISSMO multidisciplinary analysis optimization conference, No. AIAA 2010-9231, Fort Worth, TX, September 2010, doi: 10.2514/6.2010-9231.Google Scholar
23. Luke, E., Collins, E. and Blades, E. A fast mesh deformation method using explicit interpolation, J Computational Physics, 2012, 231, (2), pp 586–601.Google Scholar
24. Gray, J., Chin, J., Hearn, T., Hendricks, E., Lavelle, T. and Martins, J.R.R.A. Chemical equilibrium analysis with adjoint derivatives for propulsion cycle analysis, J Propulsion and Power, 2017, 33, (5), pp 1041–1052.Google Scholar
25. Hearn, D.T., Hendricks, E., Chin, J., Gray, J. and Moore, D.K.T. Optimization of turbine engine cycle analysis with analytic derivatives, 17th AIAA/ISSMO multidisciplinary analysis and optimization conference, Part of AIAA Aviation 2016 (Washington, DC), 2016, doi: 10.2514/6.2016-4297.Google Scholar
26. Jones, S. An introduction to thermodynamic performance analysis of aircraft gas turbine engine cycles using the numerical propulsion system simulation code, 2007, NASA TM-2007-214690.Google Scholar
27. Lambe, A.B. and Martins, J.R.R.A. Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes, Structural and Multidisciplinary Optimization, 2012, 46, pp 273–284.Google Scholar
28. Greitzer, E., Bonnefoy, P., la Rosa Blanco, E.D., Dorbian, C., Drela, M., Hall, D., Hansman, R., Hileman, J., Liebeck, R., Lovegren, J., Mody, P., Pertuze, J., Sato, S., Spakovszky, Z., Tan, C., Hollman, J., Duda, J., Fitzgerald, N., Houghton, J., Kerrebrock, J., Kiwada, G., Kordonowy, D., Parrish, J., Tylko, J., Wen, E. and Lord, W. N+3 aircraft concept designs and trade studies, final report, NASA CR 2010-216794, National Aeronautics and Space Administration, 2010.Google Scholar
29. Bradley, M.K. and Droney, C.K. Subsonic ultra green aircraft research: Phase I final report, NASA CR 2011-216847, National Aeronautics and Space Administration, 2011.Google Scholar
30. Gray, J.S., Kenway, G.K.W. and Martins, J.R.R.A. Aero-propulsive design optimization of a turboelectric boundary layer ingestion propulsion system, 2018 AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Atlanta, GA, AIAA 2018-3976, June 2018.Google Scholar
31. Gill, P.E., Murray, W. and Saunders, M.A. SNOPT: an SQP algorithm for large-scale constrained optimization, SIAM Review, 2005, 47, (1), pp 99–131.Google Scholar
32. Perez, R.E., Jansen, P.W. and Martins, J.R.R.A. pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization, Structural and Multidisciplinary Optimization, 2012, 45, 101–118.Google Scholar
33. Martins, J.R.R.A. and Lambe, A.B. Multidisciplinary design optimization: a survey of architectures, AIAA J, 2013, 51, pp 2049–2075.Google Scholar
34. McCullers, L.A. Aircraft configuration optimization including optimized flight profiles, NASA CR CP-2327, National Aeronautics and Space Administration, 1984.Google Scholar