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The rapid development of bespoke small unmanned aircraft

A Proposed Design Loop

Published online by Cambridge University Press:  30 October 2017

C. A. Paulson ,
A. Sóbester  and
J. P. Scanlan
C. A. Paulson*
Affiliation:
University of Southampton, Computational Engineering and Design, Southampton, United Kingdom
A. Sóbester
Affiliation:
University of Southampton, Computational Engineering and Design, Southampton, United Kingdom
J. P. Scanlan
Affiliation:
University of Southampton, Computational Engineering and Design, Southampton, United Kingdom
*
capaulson@gmail.com
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Abstract

The ability to quickly fabricate small unmanned aircraft system (sUAS) through Additive Manufacturing (AM) methods opens a range of new possibilities for the design and optimisation of these vehicles. In this paper, we propose a design loop that makes use of surrogate modelling and AM to reduce the design and optimisation time of scientific sUAS. AM reduces the time and effort required to fabricate a complete aircraft, allowing for rapid design iterations and flight testing. Co-Kriging surrogate models allow data collected from test flights to correct Kriging models trained with numerically simulated data. The resulting model provides physically accurate and computationally cheap aircraft performance predictions. A global optimiser is used to search this model to find an optimal design for a bespoke aircraft. This paper presents the design loop and a case study which demonstrates its application.

Keywords

additive manufacturingdesign optimisationgeometry parameterisationsurrogate modellingUAS
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1245 , November 2017 , pp. 1683 - 1710
Copyright
Copyright © Royal Aeronautical Society 2017 

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References

REFERENCES

1. Nair, P. and Keane, A.J. Computational Approaches for Aerospace Design. The Pursuit of Excellence, August 2005, John Wiley & Sons, Chichester, England.Google Scholar
2. Forrester, A.I.J. Black-box calibration for complex-system simulation, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, August 2010, 368, (1924), pp 3567-3579.Google ScholarPubMed
3. Kuya, Y., Takeda, K., Zhang, X. and Forrester, A.I.J. Multifidelity Surrogate modeling of experimental and computational aerodynamic data sets, AIAA J, February 2011, 49, (2), pp 289-298.CrossRefGoogle Scholar
4. Martins, J.R. R.A., Marriage, C. and Tedford, N. pyMDO: An object-oriented framework for multidisciplinary design optimization, ACM Transactions on Mathematical Software, August 2009, 36, (4), pp 20-25.CrossRefGoogle Scholar
5. 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, May 2011, 45, (1), pp 101-118.CrossRefGoogle Scholar
6. Marks, P. 3D printing: The world’s first printed plane, http://www.newscientist.com/article/dn20737-3d-printing-the-worlds-first-printed-plane.html, 2011.CrossRefGoogle Scholar
7. Aurora Flight Sciences. Press release. http://www.aurora.aero/media/press/item.aspx?id=apr-294, 2012. (Accessed 19/1/2014).Google Scholar
8. Stern, M. and Cohen, E. VAST AUAV (Variable AirSpeed Telescoping Additive Unmanned Air Vehicle), RAPID, pp 1–20. MIT Lincoln Laboratory, Pittsburgh, Pennsylvania, September 2013.Google Scholar
9. Nicholson, G. Rapid manufactured fixed wing powered uav, http://www.namtec.co.uk/userfiles/files/Powered_CASESTUDY.pdf, 2014. (Accessed 25/11/2014).Google Scholar
10. Sóbester, A. Four suggestions for better parametric geometries, 10th AIAA Multidisciplinary Design Optimization Conference, National Harbor, Maryland, January 2014.CrossRefGoogle Scholar
11. Sóbester, A. and Forrester, A.I.J. Aircraft Aerodynamic Design: Geometry and Optimization, 2014, Wiley, Chichester, England.CrossRefGoogle Scholar
12. McNeel. Rhinoceros 5. http://www.rhino3d.com, 2014. (Accessed 10 March 2014).Google Scholar
13. OpenNurbs. OpenNURBS SDK. http://www.rhino3d.com/opennurbs, 2014. (Accessed 10 March 2014).Google Scholar
14. Jones, E., et al SciPy: Open source scientific tools for Python. http://www.scipy.org/, 2001.Google Scholar
15. Nelder, J.A. and Mead, R. A simplex method for function minimization, The Computer J, January 1965, 7, (4), pp 308-313.CrossRefGoogle Scholar
16. Morris, M.D. and Mitchell, T.J. Exploratory designs for computational experiments, J Statistical Planning and Inference, February 1995, 43, (3), pp 381-402.CrossRefGoogle Scholar
17. Jones, D.R. A taxonomy of global optimization methods based on response surfaces, J Global Optimization, December 2001, 21, (4), pp 345-383.CrossRefGoogle Scholar
18. Forrester, A.I.J., Sobester, A. and Keane, A.J. Engineering Design via Surrogate Modelling. A Practical Guide, September 2008, John Wiley & Sons, Chichester, England.CrossRefGoogle Scholar
19. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, March 2002, 6, (2), pp 182-197.CrossRefGoogle Scholar
20. Ruffin, P.B. Opportunities and challenges for MEMS technology in Army missile systems applications, 1999 Symposium on Smart Structures and Materials, Newport Beach, California, July 1999, 3673, pp 34–44.CrossRefGoogle Scholar
21. Jang, J.S. and Liccardo, D. Small UAV automation using MEMS, Aerospace and Electronic Systems Magazine, May 2007, 22, (5), pp 30-34.CrossRefGoogle Scholar
22. Chao, H., Cao, Y. and Chen, Y. Autopilots for small unmanned aerial vehicles: A survey, Int J Control Automation Systems, 2010, 8, (1), pp 36-44.CrossRefGoogle Scholar
23. Cory, R. and Tedrake, R. Experiments in fixed-wing UAV perching, Proceedings of the AIAA Guidance, Honolulu, Hawaii, 2008.CrossRefGoogle Scholar
24. Hoburg, W. and Tedrake, R. System identification of post stall aerodynamics for uav perching, Proceedings of the AIAA Infotech@ Aerospace Conference, Seattle, Washington, April 2009.CrossRefGoogle Scholar
25. Uhlig, D.V. and Selig, M.S. Stability characteristics of micro air vehicles from experimental measurements, 29th AIAA Applied Aerodynamics Conference, Honolulu, Hawaii, 2011.CrossRefGoogle Scholar
26. Li, Z., Hoffer, N., Stark, B. and Chen, Y. Design, modeling and validation of a T-Tail unmanned aerial vehicle. J Intelligent & Robotic Systems, August 2012, 69, (1–4), 91-107.CrossRefGoogle Scholar
27. Dorobantu, A., Murch, A., Mettler, B. and Balas, G. System identification for small, low-cost, fixed-wing unmanned aircraft, J Aircr, 2013, 50, (4), pp 1117-1130.CrossRefGoogle Scholar
28. Johansen, T.A., Cristofaro, A., Sørensen, K., Hansen, J.M. and Fossen, T.I. On estimation of wind velocity, angle-of-attack and sideslip angle of small UAVs using standard sensors, 2015 International Conference on Unmanned Aircraft Systems, June 2015, IEEE, Denver, Colorado, US, pp 510–519.CrossRefGoogle Scholar
29. Sørensen, K.L., Blanke, M. and Johansen, T.A. Diagnosis of wing icing through lift and drag coefficient change detection for small unmanned aircraft, Proceedings of 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015, September 2015, Paris, France, pp 541–546.CrossRefGoogle Scholar
30. Salowitz, N., Guo, Z., Nardari, R., Li, Y.H., Kim, S.J., Kopsaftopoulos, F. and Chang, F.U. Recent advancements and vision toward stretchable bio-inspired networks for intelligent structures, Structural Health Monitoring, November 2014, 13, (6), pp 609-620.CrossRefGoogle Scholar
31. Kopsaftopoulos, F., Nardari, R., Li, Y.H., Wang, P., Ye, B. and Chang, F.U. Experimental identification of structural dynamics and aeroelastic properties of a self-sensing smart composite wing, Proceedings of the 10th International Workshop on Structural Health Monitoring, Stanford, California, 2015.CrossRefGoogle Scholar
32. Kopsaftopoulos, F., Nardari, R., Li, Y.H., Wang, P. and Chang, F.U. Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, April 2016, 9805, pp 98051V–98051V–15.Google Scholar
33. Lopes, A.J., MacDonald, E. and Wicker, R.B. Integrating stereolithography and direct print technologies for 3D structural electronics fabrication, Rapid Prototyping J, 2012, 18, (2), pp 129-143.CrossRefGoogle Scholar
34. Farahani, R.D., Dalir, H., Le Borgne, V., Gautier, L.A., El Khakani, M.A., Lévesque, M. and Therriault, D. Direct-write fabrication of freestanding nanocomposite strain sensors, Nanotechnology, March 2012, 23 (8), 085502.CrossRefGoogle ScholarPubMed
35. MacDonald, E., Salas, R., Espalin, D., Perez, M., Aguilera, E., Muse, D. and Wicker, R.B. 3D printing for the rapid prototyping of structural electronics, IEEE Access, 2014, 2, 234–242.CrossRefGoogle Scholar
36. Lopes, A.J., Lee, I.H., MacDonald, E., Quintana, R. and Wicker, R. Laser curing of silver-based conductive inks for in situ 3D structural electronics fabrication in stereolithography, J Materials Processing Technology, September 2014, 214, (9), pp 1935-1945.CrossRefGoogle Scholar
37. Espalin, D., Muse, D.W., MacDonald, E. and Wicker, R.B. 3D Printing multifunctionality: Structures with electronics, Int J Advanced Manufacturing Technology, March 2014, 72, (5–8), pp 963-978.CrossRefGoogle Scholar
38. Carmichael, B.H. Low Reynolds Number Airfoil Survey, Technical report, 1981.Google Scholar
39. 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, January 2012, 46, (2), pp 273-284.CrossRefGoogle Scholar