Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading

Valladares, Homero; Jones, Alison Snow; Tovar, Andrés

doi:10.4271/2018-01-1023

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…As in the single objective case, the MEI function is maximized to determine the next design to be simulated or to stop the optimization. The EI and MEI functions can be extended to solve constrained optimization problems [30,31]. Other fields that employ NURBS include robotics, self-driving cars, virtual reality, and isogeometric analysis [32,33,34].…”

Section: Multi-objective Expected Improvementmentioning

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: Multi-objective Expected Improvementmentioning

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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“…For example, if the acquisition function is the EI and there is enough information to identify unfeasible zones, the EI function can return a value of zero at those locations. If there is not enough information to determine the feasibility of a design, GP models of the constraint functions can be trained to quantify the probability that the design is feasible (Figure 4) [7,18].…”

Section: Constrained Expected Improvementmentioning

confidence: 99%

“…BGO is a methodology to solve black-box function optimization problems, which has been employed in several disciplines including life sciences, information mining, and structural optimization [6][7][8]. In a black-box function problem, a closed form of 𝑓𝑓(𝐱𝐱) is not available; however, 𝑓𝑓(𝐱𝐱) can be evaluated at any location 𝐱𝐱 of the design domain.…”

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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Performance-based control co-design of building structures with controlled rocking steel braced frames via neural dynamic model

Hormozabad

Soto

2021

Struct Multidisc Optim

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Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading

Cited by 11 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

Bayesian Optimization of Active Materials for Lithium-Ion Batteries

Performance-based control co-design of building structures with controlled rocking steel braced frames via neural dynamic model

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