Design Optimization of Sandwich Composite Armors for Blast Mitigation Using Bayesian Optimization with Single and Multi-Fidelity Data

Valladares, Homero; Tovar, Andrés

doi:10.4271/2020-01-0170

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…The acquisition function employs the probabilistic predictions of the surrogate model to search for optimal designs. BGO has solved design problems in multiple domains including control engineering, manufacturing, structural optimization, and blast mitigation [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

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

Valladares¹,

Tovar²

2021

SAE Int. J. Adv. & Curr. Prac. in Mobility

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

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Section: Introductionmentioning

confidence: 99%

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

Valladares¹,

Tovar²

2021

SAE Int. J. Adv. & Curr. Prac. in Mobility

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“…Bayesian optimisation has also been applied for improved buckling performance of variable stiness composite plates and cylinders [2123] and of curved bre composite panels with cut-outs [24]. Other notable applications include optimisation of composite wind turbine blades for lightning strike and multi-axial fatigue loading [25] and optimisation of sandwich composite armour design for blast mitigation [26]. Bayesian optimisation has also been applied in the design of aligned discontinuous composites considering a variety of performance characteristics [27] and in the multi-objective design of parts containing ply-drops, where stiness, Tsai-Wu omni-strain failure criterion and manufacturing time requirements were considered [8].…”

Section: Introductionmentioning

confidence: 99%

A data-driven Bayesian optimisation framework for the design and stacking sequence selection of increased notched strength laminates

Chuaqui¹,

Rhead²,

Butler³

et al. 2021

Composites Part B: Engineering

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“…An additional benefit of employing GPs as surrogate models of 𝑓𝑓(𝐱𝐱) is that they facilitate the integration of multiple sources of information, which can improve the predictions of the model. Such GP models are known as multi-output GPs [11,12] and co-kriging surrogates [13,14]. These models employ specialized correlation functions that link the sources.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization of Active Materials for Lithium-Ion Batteries

Valladares¹,

Li²,

Zhu³

et al. 2021

SAE Technical Paper Series

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The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer's intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs. The cycle life cannot be predicted using a simple closed-form expression but only through the cycling performance test or a numerical simulation. BGO has two main components: a surrogate probabilistic model of the black-box function and an acquisition function that guides the optimization. This research employs BGO in the design of cathode active materials for LIB cells. The training data corresponds to the initial capacity and cycle life of five coin cells with different compositions of LiNi x Mn 2−x O 4 in their cathode, where x is the content of Ni. BGO utilizes the experimental data to identify five new compositions that can produce cells with high initial capacity and\or large cycle life. The surrogate models of the initial capacity and cycle life are Gaussian Processes. The acquisition function is the constrained multi-objective expected improvement. The results show that BGO can identify highperformance active materials for LIBs. Designers can use the data generated during the optimization to decide the composition of the next batch of active materials to be tested, i.e., guide the physical experimentation.

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Design Optimization of Sandwich Composite Armors for Blast Mitigation Using Bayesian Optimization with Single and Multi-Fidelity Data

Cited by 9 publications

References 20 publications

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

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

A data-driven Bayesian optimisation framework for the design and stacking sequence selection of increased notched strength laminates

Bayesian Optimization of Active Materials for Lithium-Ion Batteries

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