Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-14T09:29:22.515Z Has data issue: false hasContentIssue false
  • English
  • Français

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

Published online by Cambridge University Press:  12 May 2021

F. Akram ,
H. A. Khan Open the ORCID record for H. A. Khan [Opens in a new window] ,
T. A. Shams Open the ORCID record for T. A. Shams [Opens in a new window]  and
D. Mavris Open the ORCID record for D. Mavris [Opens in a new window]
F. Akram
Affiliation:
Aerospace Engineering Department National University of Sciences and Technology (NUST) H-12, Islamabad, 44000Pakistan
H. A. Khan*
Affiliation:
Aerospace Engineering Department National University of Sciences and Technology (NUST) H-12, Islamabad, 44000Pakistan
T. A. Shams
Affiliation:
Aerospace Engineering Department National University of Sciences and Technology (NUST) H-12, Islamabad, 44000Pakistan
D. Mavris
Affiliation:
Aerospace Engineering Department Georgia Institute of TechnologyAtlantaGA, 30332USA
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The research focuses on the design space optimisation of National Advisory Committee for Aeronautics (NACA) submerged inlets through the formulation of a hybrid data fusion methodology. Submerged inlets have drawn considerable attention owing to their potential for good on-design performance, for example during cruise flight conditions. However, complexities due to the geometrical topology and interactions among various design variables remain a challenge. This research enhances the current design knowledge of submerged inlets through the utilisation of data mining and Computational Fluid Dynamics (CFD) methodologies, focusing on design space optimisation. A two-pronged approach is employed where the first step encompasses a low-fidelity model through data mining and surrogate modelling to predict and optimise the design parameters, while the second step uses the Design of Experiments (DOE) approach based on the CFD results for the candidate design geometry to construct a surrogate model with high fidelity for design refinement. The feasibility of the proposed methodology is demonstrated for the optimisation of the total pressure recovery of a NACA submerged inlet for the subsonic flight regime. The proposed methodology is found to provide good agreement between the surrogate and CFD-based model and reduce the optimisation processing time by half in comparison with conventional (global-based) CFD optimisation approaches.

Keywords

Data fusionSurrogate modellingMDONACA submerged inlet designCFD
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1292 , October 2021 , pp. 1815 - 1832
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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.)

Footnotes

This work is an extension of research that was presented at the 28th AIAA Applied Aerodynamics Conference, 2010 in Chicago, IL, USA.

References

Mossman, E.A. and Randall, L.M. An Experimental Investigation of the Design Variables for NACA Submerged Duct Entrances, NACA RM-A7I30, 1948.Google Scholar
Lombardi, A., Ferrari, D. and Santos, L. Aircraft air inlet design optimization via Surrogate-Assisted evolutionary computation, In International Conference on Evolutionary Multi-Criterion Optimization, (pp 313–327), Springer, Cham, 2015.CrossRefGoogle Scholar
Barr, S.M., O’Gara, M.O’Gara, M. and Sinha, N. Highly Compact Supersonic Inlet Design Optimization, In 2018 Joint Propulsion Conference, (p 4842), 2018.CrossRefGoogle Scholar
Miansari, M., Ghezelsofloo, S., and Toghraie, D. Numerical investigation of geometrical design effect on the submerged inlet aerodynamics characteristics. Int. J. Aeronaut., Space Sci., 2020, 21, (1), pp 25–38.CrossRefGoogle Scholar
Cheng, D.S., Tan, H.J., Sun, S. and Tong, Y. Computational study of a high-performance submerged inlet with the bleeding vortex. J. Aircr., 2012, 49, (3), pp 852–860.CrossRefGoogle Scholar
Saheby, E.B., Guoping, H. and Hays, A. Design of hypersonic forebody with a submerged pump. Proc. Inst. Mech. Eng. G. J. Aerosp. Eng., 2019, 233, (9), pp 3153–3169.CrossRefGoogle Scholar
Pignier, N.J., O’Reilly, C.J. O’Reilly, C.J. and Boij, S. Aerodynamic and aeroacoustics analyses of a submerged air inlet in a low-Mach-number flow. Comput. Fluids, 2016, 133, pp 15–31.CrossRefGoogle Scholar
Akman, O. Subsonic-transonic submerged intake design for a cruise missile. Diss. master of science in aerospace engineering, Ankara, 2014.Google Scholar
Akram, F., Prior, M. and Mavris, D. Design Space Exploration of Submerged Inlet Capturing Interaction between Design Parameters. In 28th AIAA Applied Aerodynamics Conference, Chicago, Illinois, USA, 2010.CrossRefGoogle Scholar
Koch, S., RÜtten, M.RÜtten, M. and Rein, M. Study of Total Pressure Losses at the Engine Face of a Submerged Inlet with an Ingested Vortex. In New Results in Numerical and Experimental Fluid Mechanics XI, (pp 361–371). Springer, Cham, 2018.CrossRefGoogle Scholar
Panidis, T., Stalewski, W. and ŻÓŁtak, J.ŻÓŁtak, J. The preliminary design of the air-intake system and the nacelle in the small aircraft-engine integration process. Aircr. Eng. Aerosp. Technol., 2014.CrossRefGoogle Scholar
Simpson, T.W., Lin, D.K.J. and Chen, W. Sampling strategies for computer experiments: Design and analysis. Intl. J. Reliab. Appl., 2001, 2, (3), pp 209–240.Google Scholar
Singh, A.P., Duraisamy, K. and Pan, S. Characterizing and improving predictive accuracy in shock-turbulent boundary layer interactions using data-driven models. In the 55th AIAA Aerospace Sciences Meeting, pp. 0314, 2017.CrossRefGoogle Scholar
Duraisamy, K., Zhang, Z.J. and Singh, A.P. New approaches in turbulence and transition modeling using data-driven techniques. In the 53rd AIAA Aerospace Sciences Meeting, pp. 1284, 2015.CrossRefGoogle Scholar
Xiao, N.C., Zuo, M.J. and Zhou, C. A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis. Reliab. Eng. Syst. Saf., 2018, 169, pp 330–338.CrossRefGoogle Scholar
Cozad, A., Sahinidis, N.V. and Miller, D.C. Learning surrogate models for simulation-based optimization. AIChE J., 2014, 60, (6), pp 2211–2227.CrossRefGoogle Scholar
Spalart, P.R. and Allmaras, S.R. A one-equation turbulence model for aerodynamic flows. Recherche Aerospatiale, 1994, 1, pp 5–21.Google Scholar