Kriging-assisted topology optimization of crash structures

Raponi, Elena; Bujny, Mariusz; Olhofer, Markus; Aulig, Nikola; Boria, Simonetta; Duddeck, Fabian

doi:10.1016/j.cma.2019.02.002

Cited by 82 publications

(50 citation statements)

References 47 publications

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“…Bayesian optimization (BO) is an approach for solving optimization problems where the objective function is expensive to compute and the gradient of the objective function is not available. Such optimization problems occur, for instance, when evaluating the function corresponds to running a (possibly large-scale) simulation, as is common in virtual crash tests (Raponi et al 2019) or for complex chemical reactions (Häse et al 2018). Such problems might also be approached by automatic…”

Section: Bayesian Optimizationmentioning

confidence: 99%

Identifying material parameters in crystal plasticity by Bayesian optimization

et al. 2021

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In this work, we advocate using Bayesian techniques for inversely identifying material parameters for multiscale crystal plasticity models. Multiscale approaches for modeling polycrystalline materials may significantly reduce the effort necessary for characterizing such material models experimentally, in particular when a large number of cycles is considered, as typical for fatigue applications. Even when appropriate microstructures and microscopic material models are identified, calibrating the individual parameters of the model to some experimental data is necessary for industrial use, and the task is formidable as even a single simulation run is time consuming (although less expensive than a corresponding experiment). For solving this problem, we investigate Gaussian process based Bayesian optimization, which iteratively builds up and improves a surrogate model of the objective function, at the same time accounting for uncertainties encountered during the optimization process. We describe the approach in detail, calibrating the material parameters of a high-strength steel as an application. We demonstrate that the proposed method improves upon comparable approaches based on an evolutionary algorithm and performing derivative-free methods.

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Section: Bayesian Optimizationmentioning

confidence: 99%

Identifying material parameters in crystal plasticity by Bayesian optimization

et al. 2021

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“…e optimization problem can be de ned mathematically as follows: As an e ective approach, the Kriging surrogate model is employed to represent multimodal and nonlinear functions in this paper [41][42][43]. It can be expressed as…”

Section: Objectivesmentioning

confidence: 99%

Multiobjective Optimization of Carbon Fiber‐Reinforced Plastic Composite Bumper Based on Adaptive Genetic Algorithm

Gao

Liang

Shi

et al. 2019

Mathematical Problems in Engineering

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Genetic algorithm (GA) is a common optimization technique that has two fatal limitations: low convergence speed and premature convergence to the local optimum. As an effective method to solve these drawbacks, an adaptive genetic algorithm (AGA) considering adaptive crossover and mutation operators is proposed in this paper. Verified by two test functions, AGA shows higher convergence speed and stronger ability to search the global optimal solutions than GA. To meet the crashworthiness and lightweight demands of automotive bumper design, CFRP material is employed in the bumper beam instead of traditional aluminum. Then, a multiobjective optimization procedure incorporating AGA and the Kriging surrogate model is developed to find the optimal stacking angle sequence of CFRP. Compared with the conventional aluminum bumper, the optimized CFRP bumper exhibits better crashworthiness and achieves 43.19% weight reduction.

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“…Hence, the size of the optimization problem is drastically reduced, and it gives the opportunity to consider global optimisation methods. Thus, in 2019, Raponi et al [22], proposed to use the Kriging meta-modelling method together with the Efficient Global Optimization (EGO) algorithm [23] to solve a problem of topological optimisation for crash-worthiness where the sensitivities cannot be determined.…”

Section: Introductionmentioning

confidence: 99%

Topological Optimization of Under-Platform Dampers With Moving Morphable Components and Global Optimization Algorithm for Nonlinear Frequency Response

Denimal

Haddad

Wong

et al. 2021

Journal of Engineering for Gas Turbines and Power

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To limit the risk of high cycle fatigue, underplatform dampers (UDPs) are traditionally used in aircraft engines to control the level of vibration. Many studies demonstrate the impact of the geometry of the damper on its efficiency, thus the consideration of topological optimization (TO) to find the best layout of the damper seems natural. Because of the nonlinear behavior of the structure due to the friction contact interface, classical methods of TO are not usable. This study proposes to optimize the layout of an UDP to reduce the level of nonlinear vibrations computed with the multiharmonic balance method (MHBM). The approach of TO employed is based on the moving morphable components (MMC) framework together with the Kriging and the efficient global optimization algorithm to solve the optimization problem. The results show that the level of vibration of the structure can be reduced to 30% and allow for the identification of different efficient geometries.

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Kriging-assisted topology optimization of crash structures

Cited by 82 publications

References 47 publications

Identifying material parameters in crystal plasticity by Bayesian optimization

Identifying material parameters in crystal plasticity by Bayesian optimization

Multiobjective Optimization of Carbon Fiber‐Reinforced Plastic Composite Bumper Based on Adaptive Genetic Algorithm

Topological Optimization of Under-Platform Dampers With Moving Morphable Components and Global Optimization Algorithm for Nonlinear Frequency Response

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