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Multilevel Design of Sandwich Composite Armors for Blast Mitigation using Bayesian Optimization and Non-Uniform Rational B-Splines
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 06, 2021 by SAE International in United States
Event: SAE WCX Digital Summit
In regions at war, the increasing use of improvised explosive devices (IEDs) is the main threat against military personnel in lightweight vehicles. Large cabin’s penetrations cause serious injuries in the lower body, and high accelerations have adverse effects in the spine. Critical penetrations and accelerations can cause death. This investigation employs multi-objective Bayesian optimization (MBO), non-uniform rational B-splines (NURBS) and K-means to design sandwich composite armors that simultaneously mitigate the cabin’s penetrations and the reaction force at the armor’s supports, which works as a mechanism for acceleration reduction. MBO is a methodology to solve optimization problems that require the evaluation of expensive black-box functions such as the finite element simulations of composite armors under blast events. MBO has two main components: the surrogate model of the black-box function and the acquisition function that guides the optimization. In this study, the surrogate models are Gaussian process regressions and the acquisition function is the multi-objective expected improvement function. The numerical examples show five design alternatives that optimize the armor at multiple levels including the thicknesses of the sandwich’s layers, the armor’s shape and the thickness distribution of a functionally graded honeycomb (FGHC). NURBS generate the armor’s shape and spatial distributions of the FGHC wall thickness. When designing FGHCs, K-means reduces the possible number of wall thicknesses before the construction of the armor’s finite element model. The sandwich composite is made of steel, carbon fiber reinforced polymer and aluminum HC layers. The simultaneous optimization of the thicknesses of the sandwich composite and the armor’s shape is the most effective approach to mitigate penetration and acceleration. The use of FGHCs does not provide substantial improvements.