Modeling, simulation and hybrid optimization method as design tools for range extension kit of a subsonic flying body
dc.Affiliation | October University for modern sciences and Arts (MSA) | |
dc.contributor.author | Elsherbiny A.M. | |
dc.contributor.author | Bayoumy A.M. | |
dc.contributor.author | Elshabka A.M. | |
dc.contributor.author | Abdelrahman M.M. | |
dc.contributor.other | Aeronautical Department | |
dc.contributor.other | Military Technical College | |
dc.contributor.other | Cairo | |
dc.contributor.other | Egypt; Aeronatuical department | |
dc.contributor.other | Mechatronics Department | |
dc.contributor.other | MSA University | |
dc.contributor.other | Giza | |
dc.contributor.other | Egypt; Aeronautical Department | |
dc.contributor.other | Cairo university | |
dc.contributor.other | Cairo | |
dc.contributor.other | Egypt | |
dc.date.accessioned | 2020-01-09T20:41:07Z | |
dc.date.available | 2020-01-09T20:41:07Z | |
dc.date.issued | 2018 | |
dc.description | Scopus | |
dc.description.abstract | In this paper a hybrid optimization method is introduced to convert the aerodynamic shape of a conventional aerial subsonic flying body into a glide one by providing a range extension kit and fins. The selections of configuration and airfoils are described depending on the tactical requirements and flight regimes. The wing and fins sizing is obtained using four different methods subjected to geometric constraints. The first method is an iterative optimization method using linear aerodynamic coefficients and derivatives. The second method is a multi-objective function genetic algorithm aims to maximize stability, controllability and lift-drag ratio within certain weights using linear aerodynamic data. The third method is a genetic algorithm optimization function integrated with MISSILE DATCOM aims to maximize lift-drag ratio. The fourth method is a hybrid optimization method that integrate MISSILE DATCOM with both genetic algorithm and gradient-based optimization method. Then perform a direct uncontrolled six degree of freedom simulation for the four designs and the conventional flying body. Comparing the results of ranges for these bodies reveals that the hybrid optimization method has the best range over the other designs including the conventional flying body. � 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. | en_US |
dc.identifier.doi | https://doi.org/10.2514/6.2018-0429 | |
dc.identifier.isbn | 9.78E+12 | |
dc.identifier.other | https://doi.org/10.2514/6.2018-0429 | |
dc.identifier.uri | https://arc.aiaa.org/doi/abs/10.2514/6.2018-0429 | |
dc.language.iso | English | en_US |
dc.publisher | American Institute of Aeronautics and Astronautics Inc, AIAA | en_US |
dc.relation.ispartofseries | AIAA Modeling and Simulation Technologies Conference, 2018 | |
dc.subject | Aerodynamic drag | en_US |
dc.subject | Aerodynamic stability | en_US |
dc.subject | Aerodynamics | en_US |
dc.subject | Antennas | en_US |
dc.subject | Aviation | en_US |
dc.subject | Degrees of freedom (mechanics) | en_US |
dc.subject | Drag | en_US |
dc.subject | Fins (heat exchange) | en_US |
dc.subject | Genetic algorithms | en_US |
dc.subject | Lift | en_US |
dc.subject | Lift drag ratio | en_US |
dc.subject | Missiles | en_US |
dc.subject | Optimization | en_US |
dc.subject | Vertical stabilizers | en_US |
dc.subject | Genetic-algorithm optimizations | en_US |
dc.subject | Geometric constraint | en_US |
dc.subject | Gradient-based optimization method | en_US |
dc.subject | Hybrid optimization method | en_US |
dc.subject | Iterative Optimization | en_US |
dc.subject | Multi-objective functions | en_US |
dc.subject | Range-extension kit | en_US |
dc.subject | Six degree-of-freedom | en_US |
dc.subject | Iterative methods | en_US |
dc.title | Modeling, simulation and hybrid optimization method as design tools for range extension kit of a subsonic flying body | en_US |
dc.type | Conference Paper | en_US |
dcterms.isReferencedBy | Maydew, R.G., Aerodynamic design of an extended range bomb (1980) Journal of Aircraft, 17 (6), pp. 385-386; Takahashi, T., (2008) "The Search for the Optimal Wing Configuration for Small Subsonic Air Vehicles," 12Th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p. 5915. , Victoria, British Columbia Canada; Wakayama, S., Kroo, I., Subsonic wing planform design using multidisciplinary optimization (1994) Journal of Aircraft, 32 (4), pp. 746-753; Andrews, S.A., Perez, R.E., (2015) "Parametric Study of Box-Wing Aerodynamics for Minimum Drag under Stability and Maneuverability Constraints," 33Rd AIAA Applied Aerodynamics Conference, p. 3291. , Dallas, TX; Viti, A., Druot, T., Dumont, A., (2016) "Aero-Structural Approach Coupled with Direct Operative Cost Optimization for New Aircraft Concept in Preliminary Design," 17Th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p. 3512. , Washington, D.C; Anderson, M., The potential of genetic algorithms for subsonic wing design (1995) Aircraft Engineering, Technology, and Operations Congress, p. 3925; Tang, L., Liu, D., Chen, P.-C., Extension of projectile range using oblique-wing concept (2007) Journal of Aircraft, 44 (3), pp. 774-779; Anderson, M., Gebert, G., Using pareto genetic algorithms for preliminary subsonic wing design," (1996) 6Th Symposium on Multidisciplinary Analysis and Optimization, p. 4023; Vicini, A., Quagliarella, D., Airfoil and wing design through hybrid optimization strategies (1999) AIAA Journal, 37 (5), pp. 634-641; Cummings, R.M., Liersch, C.M., Sch�tte, A., Huber, K.C., Aerodynamics and conceptual design studies on an unmanned combat aerial vehicle configuration (2016) Journal of Aircraft, pp. 1-21; Etkin, B., Reid, L.D., (1996) Dynamics of Flight: Stability and Control, , Wiley New York; Kamal, A.M., Bayoumy, A., Elshabka, A., Modeling and flight simulation of unmanned aerial vehicle enhanced with fine tuning (2016) Aerospace Science and Technology, 51, pp. 106-117; Kamal, A., Aly, A.M., Elshabka, A., Modeling, analysis and validation of a small airplane flight dynamics (2015) AIAA Modeling and Simulation Technologies Conference, p. 1138 | |
dcterms.source | Scopus |
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