Multilevel Design of Sandwich Composite Armors for Blast Mitigation using Bayesian Optimization and Non-Uniform Rational B-Splines

<div class="section abstract"><div class="htmlview paragraph">In regions at war, the increasing use of improvised explosive devices (IEDs) is main threat against military vehicles. Large cabin”s penetrations and high gross accelerations are primary threats occupants” survivability. The survivability under an IED event largely depends on design vehicle armor. Under a blast load, armor should maintain its structural integrity while providing low cabin accelerations. This investigation employs Bayesian global optimization (BGO) non-uniform rational B-splines (NURBS) to sandwich composite armors that simultaneously mitigate reaction force armor”s supports. made four layers: steel, carbon fiber reinforced polymer (CFRP), aluminum honeycomb, CFRP. BGO methodology solve problems require evaluation expensive black-box functions such as finite element (FE) simulations event. has two components: surrogate model function acquisition guides optimization. In this study, models Gaussian processes multi-objective expected improvement function. NURBS generate shape. numerical examples show three alternatives optimize levels: (1) thicknesses sandwich”s layers (2) differ in number optimized levels approach (sequential or simultaneous). results simultaneous shape most effective for mitigation.</div></div>