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A novel algorithm for conceptual design and optimisation of an affordable gliding airdrop platform using TCOMOGA

Published online by Cambridge University Press:  08 May 2018

M. Nosratollahi  and
M.A. Ghapanvary
M. Nosratollahi*
Affiliation:
Department of Aerospace Engineering, Malek-Ashtar University, Tehran, Iran
M.A. Ghapanvary
Affiliation:
Department of Aerospace Engineering, Malek-Ashtar University, Tehran, Iran
*
[email protected]
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Abstract

Unlike conventional ballistic parachutes, gliding parachutes have been extensively used as guided precision aerial delivery system (GPADS) platforms in recent years. The reasoning may be found in gliding and manoeuvering capabilities, which make this kind of ram-air parachutes superior for precision aerial delivery application. In contrast, wing-shaped configuration along with more design variables create a cumbersome design procedure for this type of parachute. Especially, when an affordable configuration is demanded, the design procedure will be a more important problem. In this respect, an innovative integrated design framework is proposed in which significant design aspects are considered so that the gliding parachute configuration can be optimiseoptimised through a bi-objective optimisation problem to find optimum cost for achievable gliding ranges. To do so, the configuration is defined with minimum required parameters and design space is constrained by performance and stability as significant design requirements to guarantee the feasibility of the solutions in practice. The objective functions are defined in terms of gliding characteristics and amount of materials for fabrication which are representative for maximum reachable stand-off distance and unit cost, respectively. As an effective numerical optimisation method, a niched multi-objective genetic algorithm (MOGA) is used to generate a pareto-optimal set for a specific payload mass whereas constraints are handled through a tournament selection process. Based on results, the provided pareto front can aid the designers in decision-making and trade-off between demanded objectives for a payload weight. The underlying design problem is solved using an all-at-once (AAO) approach and the design loop converges to favourable results in a reasonable time. Finally, as a comparative study, an optimiseoptimised affordable cargo parachute is proposed for the GPADS application in which the material cost is reduced by at least 25% with respect to an available low-cost gliding parachute with the same glide ratio.

Keywords

Optimised affordable gliding parachuteniched multi-objective genetic algorithmintegrated design frameworkPareto optimal set
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1252 , June 2018 , pp. 933 - 959
Copyright
Copyright © Royal Aeronautical Society 2018 

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References

REFERENCES

1. Jalbert, D.C. Multi-cell wing type aerial device, US Patent No. 3285546, 1966.Google Scholar
2. Nathe, G.A, Knapp, C.F. and Hall, C. R. Wind-Tunnel and Free Flight Testing of Parafoil Model Number 125, Aerspace Engineering, Departmental Technical Report, University of Notre Dame, Notre Dame, Indiana, US, June 1966.Google Scholar
3. Nicolaides, J.D. Parafoil Performance in Tethered, Gliding, and Towed Flight, Aerospace Engineering Department Technical Report, University of Notre Dame, Notre Dame, Indiana, US, October 1969.Google Scholar
4. Nicolaides, J.D. and Greco, J.R. Para-Foil Wind Tunnel Tests, Aero-Space Engineering Dept., University of Notre Dame, Notre Dame, Indiana, US, November 1969.Google Scholar
5. Speelman, J. Parafoil Steerable Parachute, Exploratory Development for Airdrop System Application, Airforce Flight Dynamics Laboratory, Technical Report, TR-71-37, Wright-Patterson Air force base, Ohio, US, 1972.Google Scholar
6. Burk, S.M. and Ware, G.M. Static Aerodynamic Characteristics of Three Ram-Air-Inflated Low-Aspect-Ratio Fabric Wings, NASA Technical Note, NASA, Langley Space Research Center, Hampton, Virginia, US, 1967.Google Scholar
7. Ware, G.M. and Hassell, J.L. Wind-Tunnel Investigations of Ram-Air Inflated All Flexible Wing, Technical Report, NASA SX-1923, NASA, Langley Space Research Center, Hampton, Virginia, US, 1969.Google Scholar
8. Lingard, J.S. The Performance and Design of Ram-Air Gliding Parachutes, Royal Aircraft Establishment, Great Britain, Technical Report, TR 81103, 1981.Google Scholar
9. Lingard, J.S. The Aerodynamic of Gliding Parachutes, Technical Report, A88-11201, RAS, GQ defense Equipment, Parachute Devision, London, England, 1986.CrossRefGoogle Scholar
10. Lingard, J.S. Ram-air parachute design, 13th AIAA Aerodynamic Decelerator Systems Technology Conference, 15-18 May 1995, Clearwater Beach, Florida, US.Google Scholar
11. Ross, J.C. Computational aerodynamics in the design and analysis of Ram-Air inflated wings, 12th Aerodynamic Decelerator Systems Technology, May 1993, London, UK.Google Scholar
12. Kalro, V., Aliabadi, S., Garrard, W., Tezduyar, T., Mital, S., and Stein, K. Parallel finite element simulation of large Ram-Air parachutes, Int. J for Numerical Methods in Fluids, June 1997, 24, (12), pp 1353-1369.3.0.CO;2-6>CrossRefGoogle Scholar
13. Tezduyar, T.E., Kalro, V., and Garrard, W. Parallel computational methods for 3D simulation of a parafoil with prescribed shape changes, Parallel Computing, September 1997, 23, (9), pp 1349-1363.CrossRefGoogle Scholar
14. Mohammadi, M.A. and Johari, H. Computation of flow over a high-performance parafoil canopy, J Aircraft, July–August 2010, 47, (4), pp 1338-1345.CrossRefGoogle Scholar
15. Fogell, N. and Iannucci, L. Fluid-structure interaction simulations of the inflated shape and associated flow field of the MC4/5 parafoil during steady gliding flight, 24th AIAA Aerodynamic Decelerator Systems Technology Conference, 5–9 June 2017, Denver, Colorado, US.CrossRefGoogle Scholar
16. Slegers, N., Beyer, E., and Castello, M. Use of variable incidence angle for glide slope control of autonomous parafoils, J Guidance,Control, and Dynamics, June 2008, 31, (3), pp 585-596.CrossRefGoogle Scholar
17. Gorman, C.M. and Slegers, N.J. Modeling of parafoil-payload relative yawing motion on autonomous parafoils, 21st AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar, 23–26 May 2011, Dublin, Ireland.CrossRefGoogle Scholar
18. Slegers, N. Effects of canopy-payload relative motion on control of autonomous parafoils, J Guidance,Control, and Dynamics, Jan.-February 2010, 33, (1), pp 116-125.CrossRefGoogle Scholar
19. Gang, Y. Nine-degree of freedom modeling and flight dynamic analysis of parafoil aerial delivery system, 2014 Asia-Pacific International Symposium on Aerospace Technology, APISAT2014, 24–26 September 2014, Shanghai, China.Google Scholar
20. Goodrick, T.F. Theoretical study of the longitudinal stability of high-performance gliding airdrop systems, 5th AIAA Aerodynamic Decelerator Systems Conference, 17–19 November 1975, Albuquerque, New Mexico, US.CrossRefGoogle Scholar
21. Iosilevski, G. Center of gravity and minimal lift coefficient limits of a gliding parachute, J Aircraft, November-December 1995, 32, (6), pp 1297-1302.CrossRefGoogle Scholar
22. Prakash, O. and Ananthkrishnan, N. Modeling and simulation of 9 DOF parafoil-payload system flight dynamics, AIAA Atmospheric Flight Mechanics Conference and Exhibit, 21–24 August 2006, Keystone, Colorado, US.CrossRefGoogle Scholar
23. Crimi, P. Lateral stability of gliding parachutes, J Guidance,Control, and Dynamics, 1990, 13, (6), pp 1060-1063.CrossRefGoogle Scholar
24. Lissaman, P.B. and Broen, G.J. Apparent mass effects on parafoil dynamics, Aerospace Design Conference, 16–19 February 1993, Irvine, California, US.CrossRefGoogle Scholar
25. Prakash, O., Daftary, A., and Ananthkrishnan, N. Bifurcation analysis of parafoil-payload system flight dynamics, AIAA Atmospheric Flight Mechanics Conference and Exhibit, 15–18 August 2005, San Francisco, California, US.CrossRefGoogle Scholar
26. Yang, H., Song, L., and Chen, W. Research on parafoil stability using a rapid estimate model, Chinese J Aeronautics, October 2017, 30, (5), pp 1670-1680.CrossRefGoogle Scholar
27. Nosratollahi, M. and Ghapanvary, M.A. Gliding parachute platform design optimization with performance & stability Constraints, Modares Mechanical Engineering, 2017, 17, (4), pp 211-218, (in Persian).Google Scholar
28. Ewing, E.G., Bixby, H.W., and Knacke, T.W. Recovery Systems Design Guide, Airforce Flight Dynamics Laboratory, Technical Report, TR-78-151, U.S. Airforce Flight Dynamics Laboratory, Irvine, California, US, December 1978.CrossRefGoogle Scholar
29. Poynter, D. The Parachute Manual: A Technical Treatise on Aerodynamic Decelerators (Vol. 1), Para Publishing CO., Santa Barbara, California, US, May 1991.Google Scholar
30. Potvin, J., Brocato, G., and Peek, B. Modeling the inflation of Ram-Air parachutes reefed with sliders, J Aircraft, September–October 2001, 38, (5), pp 818-827.CrossRefGoogle Scholar
31. Potvin, J. Universality considerations for graphing parachute opening shock factor versus mass ratio, J Aircraft, March–April 2007, 44, (2), pp 528-538.CrossRefGoogle Scholar
32. FALCON MAIN ARACHUTE, Para gear Equipment CO. Inc., Skokie, Illinois, US, 2016. Available at: http://www.paragear.com/skydiving/10000220/C88/PRECISION-AERODYNAMICS-FALCON-MAIN-PARACHUTE.html.Google Scholar
33. Sabre2, Performance Designs, Inc., Deland, Florida, US, 2018. Available at: http://www.performancedesigns.com/products/sabre2/.Google Scholar
34. The Raven Dash-M Reserve, Pepperell, Massachusetts, US, 2018. Available at: http://parachuteshop.com/Raven%20Dash-M%20Series.htm.Google Scholar
35. Kalro, V. and Tezduyar, T.E. A parallel 3D computational method for fluid-structure interactions in parachute systems, Computer Methods in Applied Mechanics and Engineering, 27 October 2000, 190, (3–4), pp 321-332.CrossRefGoogle Scholar
36. Altmann, H. Numerical simulation of parafoil aerodynamics and dynamic behavior, 20th AIAA Aerodynamic DeceleratorSystems Technology Conference and Seminar, Aerodynamic Decelerator Systems Technology Conference, 4–7 May 2009, Seattle, Washington, US.CrossRefGoogle Scholar
37. Peyada, N.K., Singhal, A., and Ghosh, A.K. Trajectory modeling of a parafoil in motion using analytically derived stability derivative at high angle of attack, 19th AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar, 21–24 May 2007, Williamsburg, Virginia, US.CrossRefGoogle Scholar
38. Yakimenko, O.A. Precision Aerial Delivery Systems: Modeling, Dynamics, and Control, Progress in Astronautics and Aeronautics, Volume 248, American Institute of Aeronautics and Astronomics, Incorporated, 2015.CrossRefGoogle Scholar
39. Mieloszyk, J. and Goetzendorf-Grabowsky, T. Introduction of full flight dynamic stability constraints in aircraft multidisciplinary optimization, Aerospace Science and Technology, September 2017, 68, pp 252-260, DOI: 10.1016/j.ast.2017.05.024.CrossRefGoogle Scholar
40. Martins, J.R.R.A. and Lambe, A.B. Multidisciplinary design optimization: A survey of architectures, AIAA J, September 2013, 51, (9), pp 2049-2075, DOI: 10.2514/1.J051895.CrossRefGoogle Scholar
41. Balling, R. and Wilkinson, C. Execution of multidisciplinary design optimization approaches on common test problems, 6th Symposium on Multidisciplinary Analysis and Optimization, 4-6 September 1996, Bellevue, Washington, US.CrossRefGoogle Scholar
42. Tedford, N.P. and Martins, J.R.R.A. Optimization and Engineering, 11, (1) https://link.springer.com/journal/11081/11/1/page/1, February 2010, pp 159-183, DOI: 10.1007/s11081-009-9082-6.CrossRefGoogle Scholar
43. Deb, K. Multi-objective Optimization using Evolutionary Algorithms, 2001, John Wiley & Sons.Google Scholar
44. Fonseca, C.M. and Fleming, P.J. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization, Proceedings of the 5th International Conference on Genetic Algorithms, July 1993, Urbana-Champaign, Illinois, US, 416–423.Google Scholar
45. Fonseca, C.M. and Fleming, P.J. Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part I: A unified formulation, IEEE Transactions on System, Man and Cybernetic Part A: Systems and Humans, January 1998, 28, (1), pp 26-37.CrossRefGoogle Scholar
46. Jing, C. and Shou, T. Research on integrated optimization design of hypersonic cruise vehicle, Aerospace Science and Technology, October 2008, 12, (7), pp 567-572, DOI: 10.1016/j.ast.2008.01.008.Google Scholar
47. Obayashi, S., Sasaki, D., and Oyama, A. Finding tradeoffs by using multiobjective optimization algorithms. Transactions of JASS, May 2004, 47, (155), pp 51-58.Google Scholar
48. Oyama, A. and Liou, M.S. Multiobjective optimization of a multi-stage compressor using evolutionary algorithm, 38th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 7–10 July 2002, Indianapolis, Indiana, US.CrossRefGoogle Scholar
49. Konak, A., Coit, D.W., and Smith, A.E. Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering and System Safety, September 2006, 91, (9), pp 992-1007.CrossRefGoogle Scholar
50. Military Specification MIL-C-44378: Cloth, Parachute, Nylon, Low Permeability, 30 June 1989, Natick, Massachusetts, US. Available at: http://everyspec.com/MIL-SPECS/MIL-SPECS-MIL-C/MIL-C-44378_47035/.Google Scholar
51. Military Specification T-C-2754, Military Standard: Cord, Polyester Coreless, 5 February 1990, Natick, Massachusetts, US. Available at: http://everyspec.com/FED_SPECS/T/T-C-2754_32045/Google Scholar
52. Ram air parachute owner manual: Raven parachutes-Part Pl 3001, 1998, Precision Aerodynamics Inc., Dunlap, Tennessee, US. Available at: http://www.performancedesigns.com/docs/MainUsersManual.pdfGoogle Scholar