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Design Optimization of Sandwich Composite Armors for Blast Mitigation Using Bayesian Optimization with Single and Multi-Fidelity Data
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The most common and lethal weapons against military vehicles are the improvised explosive devices (IEDs). In an explosion, critical cabin’s penetrations and high accelerations can cause serious injuries and death of military personnel. This investigation uses single and multi-fidelity Bayesian optimization (BO) to design sandwich composite armors for blast mitigation. BO is an efficient methodology to solve optimization problems that involve black-box functions. The black-box function of this work is the finite element (FE) simulation of the armor subjected to blast. The main two components of BO are the surrogate model of the black-box function and the acquisition function that guides the optimization. In this investigation, the surrogate models are Gaussian Process (GP) regression models and the acquisition function is the multi-objective expected improvement (MEI) function. Information from low and high fidelity FE models is used to train the GP surrogates. The high fidelity model considers the nonlinear behavior of each layer of the composite armor while the low fidelity model only considers the elastic behavior. The sandwich composite is made of four layers: steel, carbon fiber reinforced polymer (CFRP), aluminum honeycomb (HC) and an additional layer of CFRP. The design variables are the thickness of each layer and the fiber orientations of the CFRP laminas. The optimization problem includes constraints over the maximum thickness of the sandwich composite and fiber orientations. Two objective functions are minimized, the cabin’s penetrations and the reaction force at the armor’s supports. The results show that sandwich composites are an excellent alternative to generate lightweight armors. In terms of penetration, an optimized 100-kg composite armor performs similarly to a 195-kg steel armor. The results also show that the multi-fidelity BO approach is an appealing alternative if the number of function evaluations (of the high fidelity FE model) to perform optimization is low (less than 25). However, if the optimization stage can use a large number of function evaluations (larger than 80), the single fidelity BO approach produces better designs.
CitationValladares, H. and Tovar, A., "Design Optimization of Sandwich Composite Armors for Blast Mitigation Using Bayesian Optimization with Single and Multi-Fidelity Data," SAE Technical Paper 2020-01-0170, 2020.
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- “Recent Trends in Active-Duty Military Deaths,” Congressional Research Service, 2019.
- Goetz, J. et al. , “Two-Material Optimization of Plate Armour for Blast Mitigation Using Hybrid Cellular Automata,” Engineering Optimization 44(8):985-1005, 2012.
- Hoffenson, S., Arepally, S., and Papalambros, P.Y. , “A Multi-Objective Optimization Framework for Assessing Military Ground Vehicle Design for Safety,” The Journal of Defense Modeling and Simulation 11(1):33-46, 2014.
- Covey, D.C. , “Blast and Fragment Injuries of the Musculoskeletal System,” JBJS 84(7):1221-1234, 2002.
- Qi, C. et al. , “Blast Resistance and Multi-Objective Optimization of Aluminum Foam-Cored Sandwich Panels,” Composite Structures 105:45-57, 2013.
- Liu, X. et al. , “Blast Resistance of Sandwich-Walled Hollow Cylinders with Graded Metallic Foam Cores,” Composite Structures 94(8):2485-2493, 2012.
- Batra, R. and Hassan, N. , “Blast Resistance of Unidirectional Fiber Reinforced Composites,” Composites Part B: Engineering 39(3):513-536, 2008.
- Phadnis, V.A. et al. , “Optimising Curvature of Carbon Fibre-Reinforced Polymer Composite Panel for Improved Blast Resistance: Finite-Element Analysis,” Materials & Design 57:719-727, 2014.
- Frazier, P.I. , “A Tutorial on Bayesian Optimization,” arXiv preprint arXiv:1807.02811, 2018.
- Shahriari, B. et al. , “Taking the Human Out of the Loop: A Review of Bayesian Optimization,” Proceedings of the IEEE 104(1):148-175, 2015.
- Kennedy, M.C. and O’Hagan, A. , “Predicting the Output from a Complex Computer Code When Fast Approximations Are Available,” Biometrika 87(1):1-13, 2000.
- Forrester, A., Sobester, A., and Keane, A. , Engineering Design via Surrogate Modelling: A Practical Guide (John Wiley & Sons, 2008).
- Garud, S.S., Karimi, I.A., and Kraft, M. , “Design of Computer Experiments: A Review,” Computers & Chemical Engineering 106:71-95, 2017.
- Costas, M. et al. , “A Multi-Objective Surrogate-Based Optimization of the Crashworthiness of a Hybrid Impact Absorber,” International Journal of Mechanical Sciences 88:46-54, 2014.
- Montgomery, D.C. and Myers, R.H. , Response Surface Methodology: Process and Product Optimization Using Designed Experiments (A Wiley-Interscience Publications, 1995).
- Nielsen, H.B., Lophaven, S.N., and Sondergaard, J. , “DACE, a MATLAB Kriging Toolbox,” Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Lyngby-Denmark, 2002.
- Crombecq, K. et al. , “A Novel Sequential Design Strategy for Global Surrogate Modeling,” in Proceedings of the 2009 Winter Simulation Conference (WSC), IEEE, 2009.
- Crombecq, K. and Dhaene, T. , “Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods,” in Asia-Pacific Conference on Simulated Evolution and Learning, Springer, 2010.
- Crombecq, K. et al. , “Space-Filling Sequential Design Strategies for Adaptive Surrogate Modelling,” in The First International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering. 2009.
- Schulz, E., Speekenbrink, M., and Krause, A. , “A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions,” Journal of Mathematical Psychology 85:1-16, 2018.
- Rasmussen, C.E. and Williams, C.K. , Gaussian Process for Machine Learning. Adaptive Computation and Machine Learning (Cambridge, MA: MIT Press, 2006).
- Couckuyt, I., Dhaene, T., and Demeester, P. , “ooDACE Toolbox, a Matlab Kriging Toolbox: Getting Started,” Universiteit Gent, 2013.
- Jones, D.R., Schonlau, M., and Welch, W.J. , “Efficient Global Optimization of Expensive Black-Box Functions,” Journal of Global Optimization 13(4):455-492, 1998.
- Forrester, A.I., Sóbester, A., and Keane, A.J. , “Multi-Fidelity Optimization via Surrogate Modelling,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463(2088):3251-3269, 2007.
- Valladares, H., Jones, A., and Tovar, A. , “Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading,” SAE Technical Paper 2018-01-1023, 2018, https://doi.org/10.4271/2018-01-1023.
- Williams, K. et al. , “Validation of a Loading Model for Simulating Blast Mine Effects on Armoured Vehicles,” in The 7th International LS-DYNA Users Conference, 2002.
- Hallquist, J.O. , “LS-DYNA Theory Manual,” 3, Livermore Software Technology Corporation, 2006.
- Nayak, S. et al. , “Process for Design Optimization of Honeycomb Core Sandwich Panels for Blast Load Mitigation,” Structural and Multidisciplinary Optimization 47(5):749-763, 2013.