Abstract
The coupling between different disciplines for multi-disciplinary optimization greatly increases the complexity of a computational framework, while at the same time increasing CPU time and memory usage. To overcome these difficulties, first, proper orthogonal decomposition and radial basis function were used to generate a reduced-order model from the initial experimental points. Second, analysis results for additional experimental points were predicted using the reduced-order model. Third, using automated machine learning, surrogate models for the objective and constraint functions were obtained from the analysis results at the initial and additional experimental points. Last, optimization was performed using the surrogate models for the objective and constraint functions. As an example, the multi-disciplinary optimization problem of determining the thicknesses of the composite lamina and sandwich core when the composite sandwich structure was used as an aircraft wing skin material was analyzed.
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References
Joe DS, Yoo JH, Joe CY, Park CW (2010) Development of an automated aero-structure interaction system for multidisciplinary design optimization for the large AR aircraft wing. J Korean Soc Aeronaut Space Sci 38(7):716–726
Park CW, Chu JM, Shul CW, Jun SM (2013) Optimization of sandwich structures of a small aircraft wing using automated aero-structure interaction systems. J Korean Soc Precis Eng 30(10):1061–1068
Park KH, Jun SO, Cho MH, Lee DH (2010) Design optimization of transonic wing/fuselage system using proper orthogonal decomposition. J Korean Soc Aeronaut Space Sci 38(5):414–420
Park CR, Lee CJ (2014) Proper orthogonal decomposition analysis of flow characteristics in hybrid rocket engine. J Korean Soc Aeronaut Space Sci 42(5):383–389
** R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidiscipl Optimiz 23(1):1–13
Wang GG, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129(4):370–380
Forrester AIJ, Keane AJ (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79
Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Tucker PK (2005) Surrogate-based analysis and optimization. Prog Aerosp Sci 41(1):1–28
Simpson TW, Toropov VV, Balabanov V, Viana FAC (2008) Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come—or not. In: Proceedings of the 12th AIAA/ISSMO multidisciplinary analysis and optimization conference, AIAA 2008–5802, American Institute of Aeronautics and Astronautics, p 1–22
Mifsud M (2008) Reduced-order modelling for high-speed aerial weapon aerodynamics, Ph.D. thesis. In: Cranfield University—College of Aeronautics
Iuliano E, Quagliarella D (2013) Proper orthogonal decomposition, surrogate modelling and evolutionary optimization in aerodynamic design. Comput Fluids 84:327–350
Simpson TW, Dennis L, Chen W (2001) Sampling strategies for computer experiments: design and analysis. Int J Reliab Appl 2(3):209–240
Simpson TW (1998) A concept exploration method for product family design, Ph.D Dissertation, G.W. In: Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
Hounjet MHL, Meijer JJ (1995) Evaluation of elastomechanical and aerodynamic data transfer methods for non-planar configuration in computational aeroelastic analysis. In: National Aerospace Laboratory NLP, NLP-TP-95690 U
Shmuel RS (1911) An algorithm for selecting a good value for the parameter c in radial basis function interpolation. Adv Comput Math 11:193–210
Randal SO, Nathan B, Ryan JU, Jason HM (2016) Evaluation of a tree-based pipeline optimization tool for automating data science. In: Proceedings of GECCO, pp 485–492
Zhengjiang S (2018) A genetic algorithm framework in Python (GAFT). https://github.com/pytlab/gaft
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Kim, Y.S., Park, C. Multi-disciplinary Optimization of Wing Sandwich Structure using Proper Orthogonal Decomposition and Automatic Machine Learning. Int. J. Aeronaut. Space Sci. 22, 1085–1091 (2021). https://doi.org/10.1007/s42405-021-00378-8
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DOI: https://doi.org/10.1007/s42405-021-00378-8