Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-29T17:18:37.107Z Has data issue: false hasContentIssue false
  • English
  • Français

Possibility-Based Multidisciplinary Optimisation For Electric-Powered Unmanned Aerial Vehicle Design

Published online by Cambridge University Press:  27 January 2016

N. V. Nguyen ,
J.-W. Lee ,
M. Tyan  and
D. Lee
N. V. Nguyen*
Affiliation:
Aerospace Information Engineering, Konkuk University, Seoul, South Korea
J.-W. Lee*
Affiliation:
Aerospace Information Engineering, Konkuk University, Seoul, South Korea
M. Tyan
Affiliation:
Aerospace Information Engineering, Konkuk University, Seoul, South Korea
D. Lee
Affiliation:
Lig Nex1, PGM R&D Center, South Korea
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes a possibility-based multidisciplinary optimisation for electric-powered unmanned aerial vehicles (UAVs) design. An in-house integrated UAV (iUAV) analysis program that uses an electric-powered motor was developed and validated by a Predator A configuration for aerodynamics, weight, and performance parameters. An electric-powered propulsion system was proposed to replace a piston engine and fuel with an electric motor, power controllers, and battery from an eco-system point of view. Moreover, an in-house Possibility-Based Design Optimisation (iPBDO) solver was researched and developed to effectively handle uncertainty variables and parameters and to further shift constraints into a feasible design space. A sensitivity analysis was performed to reduce the dimensions of design variables and the computational load during the iPBDO process. Maximising the electric-powered UAV endurance while solving the iPBDO yields more conservative, but more reliable, optimal UAV configuration results than the traditional deterministic optimisation approach. A high fidelity analysis was used to demonstrate the effectiveness of the process by verifying the accuracy of the optimal electric-powered UAV configuration at two possibility index values and a baseline.

Type
Research Article
Information
The Aeronautical Journal , Volume 119 , Issue 1221 , November 2015 , pp. 1397 - 1414
Copyright
Copyright © Royal Aeronautical Society 2015

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.Sobieszczanski-Sobieski, J. and Haftka, R.T.Multidisciplinary aerospace design optimization: survey of recent developments, Structural and Multidisciplinary Optimization, 1997, 14, (1), pp 1–23.CrossRefGoogle Scholar
2.Mavris, D.N.Bandte, O. and Delaurentis, D.A.Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design, J Aircr, 1999, 36, (1), pp 298–307.CrossRefGoogle Scholar
3.Rao, S.S. and Cao, L.Optimum Design of Mechanical Systems Involving Interval Parameters, ASME J, 2002, 124, (3), pp 465472.Google Scholar
4.Taguchi, T., Yokota, T. and Gen, M.Reliability optimal design problem with interval coefficients using hybrid genetic algorithms, Computers & industrial engineering, 1998, 35, (1), pp 373–376.CrossRefGoogle Scholar
5.Taguchi, G. and Rafanelli, A.J. Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream, 1994.CrossRefGoogle Scholar
6.Park, G.-J.Lee, T.H., Lee, K.H. and Hwang, K.-H, Robust design: An overview, AIAA J, 2006, 44, (1), pp 181–191.CrossRefGoogle Scholar
7.Chen, W., Garimella, R. and Michelena, N.robust design for improved vehicle handling under a range of maneuver conditions, Engineering Optimization, February 2001, 33, (3), pp 303–326.CrossRefGoogle Scholar
8.Neufeld, D., Joon, C. and Kamran, B.Aircraft conceptual design optimization considering fidelity uncertainties, J Aircr, October 2011, 48, (5), pp 1602–1613.CrossRefGoogle Scholar
9.Neufeld, D., Behdinan, K. and Chung, J. Aircraft wing box optimization considering uncertainty in surrogate models, July 2010, pp 745–753.CrossRefGoogle Scholar
10.Oh, S., Lee, G.J. and Nah, S.-H. Aerodynamic Design and Aviation Procedure Considering Global Warming, in KSASS, 2010.Google Scholar
11. Selection Of Electronic Motors For Aerospace Applications, Nasa, Marshall Space Flight Center, Practice No.Pd-Ed-1229.Google Scholar
12. Chapter 1. Electric Vehicles For Aerospace Applications.Google Scholar
13.Fehrenbacher, J.>, Stanley, D.L.Johnson, M.E. and Honchell, J.Electric Motor & Power Source Selection for Small Aircraft Propulsion, Purdue University, USA, 2011.Google Scholar
14.Harmon, F.G., Frank, A.A. and Chattot, J.Conceptual design and simulation of a small hybrid-electric unmanned, aerial vehicle, J Aircr, 2006, 43, (5), pp 1490–1498.CrossRefGoogle Scholar
15.Nguyen, N.-V., Choi, S.-M., Kim,W.-S., , Lee, J.-W., Kim, S., Neufeld, D and Byun, Y.-H.Multidisciplinary unmanned combat air vehicle system design using multi-fdelity model, Aerospace Science and Technology, 26, 2013, Pp 200–210.CrossRefGoogle Scholar
16.Drela, M. and Youngren, H.Athena Vortex Lattice, 2008.Google Scholar
17.Raymer, D.Aircraft Design: A Conceptual Approach, 4th ed, AIAA, 2006.Google Scholar
18.Roskam, J.Airplane Design Parts I through VIII, 2nd ed. Darcorporation, 2003.Google Scholar
19.Gundlach, J.Designing Unmanned Aircraft Systems: A Comprehensive Approach, AIAA Education Series, 2012.CrossRefGoogle Scholar
20.Lowry, J.T.Performance of Light Aircraft, AIAA education series, 1999.CrossRefGoogle Scholar
21.Drela, M. XFoil Subsonic Airfoil Development System. Online. Available: http://web.mit.edu/drela/Public/web/xfoil/. Accessed: 2 March 2013.Google Scholar
22.Nhu Van Nguyen, N.V., Lee, D., Park, H.-U., Tyan, M. and Lee, J.W.A Multidisciplinary Robust, Optimization Framework for UAV Conceptual Design, Aeronaut J, February 2014, 118, (1199), pp 123–142.CrossRefGoogle Scholar
23.Möller, B and Beer, M.Engineering computation under uncertainty-capabilities of non-traditional models, Computers & Structures, 2008, 86, (10), pp 1024–1041.CrossRefGoogle Scholar
24.Pham, T.D. and Valliappan, S.Constructing the Membership Function of a Fuzzy Set with Objective and Subjective Information, Computer-Aided Civil and Infrastructure Engineering, 2008, 8, (1), pp 75–82.CrossRefGoogle Scholar
25.He, L.P., Huang, H.Z., Du, L., Zhang, X.-D. and Miao, Q.A Review of Possibilistic Approaches to Reliability Analysis and Optimization in Engineering Design – Springer, Human-Computer Interaction, HCI Applications and Services, 2007, 4553, pp 1075–1084.Google Scholar
26.Neufeld, D., Nhu Van, N., Lee, J.-W. and Kim, S.A Multidisciplinary Possibilistic Approach to Light Aircraft Conceptual Design, AIAA 2012-1434, 8th MDO Specialists, Hawaii, USA, April 2012.CrossRefGoogle Scholar
27. FLUENT 6.0 User’S Guide. Available: http://202.118.250·111:8080/fluent/Fluent60_help/html/ug/main_pre.htm. 28. Ansys Fluent 13, Ansys, Inc., www.ansys.Com.Google Scholar
29. PHX Modelcenter® | Phoenix Integration. Online.. Available: http://www.phoenix-int.com/software/phx – modelcenter.php.Google Scholar
30. Pointwise 16 User Manual: http://www.pointwise.com/Google Scholar