Crashworthiness-based lightweight design problem via new robust design method considering two sources of uncertainties

Metamodel-based crashworthiness optimization is an expensive and highly nonlinear design problem. Due to the lack of finite element simulations, the responses fitted by different metamodeling methods are not fully equivalent to the real responses. The metamodel error may induce to find a local or an infeasible design solution. Compared to the traditional one-step sampling DOE method, the objective-oriented sequential sampling strategies have been demonstrated as a higher efficient way to find the true optimum design. However, existing infilling criteria of the sequential sampling methods are restricted to specify the number of the sequential samples obtained in each iteration. It is not practical for the real engineering optimization applications. In this paper, a new adaptive multi-point sequential sampling method is developed. The sequential samples obtained in each iteration are determined by the prediction states of the fitting metamodels. To demonstrate the benefits, the new proposed method is applied to a high-dimensional and highly nonlinear frontal crashworthiness optimization problem. Results show that the proposed method can mitigate the effect of the metamodel prediction error and more efficiently find the global design solution compared to the conventional approach.

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“…These crash performances are all highly nonlinear, and the prediction error has large influence on the design solution. 20…”

Section: Engineering Application In a Crashworthiness Design Problemmentioning

confidence: 99%

Adaptive multi-point sequential sampling methodology for high-dimensional and highly nonlinear crashworthiness optimization problems

Zhang

Song

Shi

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Surrogate models are then developed based on the scattered samples and serve as the basis for further optimization and uncertainty quantification. With development in the past decades, surrogate models have proved to be a major scheme for effective design optimization and uncertainty quantification of various mechanical systems including but not limited to composite laminates [4], thermodynamic modeling [5], chemo-thermal modeling of composites [6], computational fluid dynamics [7], high-performance computing [8], structural prognosis [9], crashworthiness-based lightweight design [10], time-dependent reliability design optimization [11], flapping wing design [12]. The accuracy of the surrogate model relies on the sampling scheme which systematically determines the location and number of samples.…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Adaptive Sampling of Multi-Fidelity Gaussian Processes to Reduce Predictive Uncertainty

Ghosh

Kristensen

Zhang

et al. 2019

Volume 2B: 45th Design Automation Conference

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Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next sampling location of the design variable, but also the level of the simulator fidelity. This issue is often addressed by including the cost of the simulator as an another factor in the searching criterion in conjunction with the uncertainty reduction metric. In this work, we extent the traditional design of experiment framework for the multi-fidelity Gaussian process by partitioning the prediction uncertainty based on the fidelity level and the associated cost of execution. In addition, we utilize the concept of Believer which quantifies the effect of adding an exploratory design point on the Gaussian process uncertainty prediction. We demonstrated our framework using academic examples as well as a industrial application of steady-state thermodynamic operation point of a fluidized bed process

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“…25 A robust design method that considered metamodel uncertainty and design variables uncertainty was proposed and applied to the crashworthiness-based lightweight design problem. 26,27…”

Section: Introductionmentioning

confidence: 99%

“…25 A robust design method that considered metamodel uncertainty and design variables uncertainty was proposed and applied to the crashworthiness-based lightweight design problem. 26,27 Most metamodel construction methods mentioned above for robust design optimization have been developed considering only one or two of metamodel uncertainty, design variables uncertainty, and noise parameters uncertainty, and so it is still a challenge to simultaneously measure all the uncertainties of metamodel, design variables, and noise parameters. To address this issue, a new surrogate modeling method considering all these uncertainties is proposed for simulation-based robust design optimization.…”

Section: Introductionmentioning

confidence: 99%

Simulation-based robust design of complex product considering uncertainties of metamodel, design variables, and noise parameters

Liu

Peng

Qiu

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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The uncertainties of design variables, noise parameters, and metamodel are important factors in simulation-based robust design optimization. Most conventional metamodel construction methods only consider one or two uncertainties. In this paper, a new surrogate modeling method simultaneously measuring all the uncertainties is proposed for simulation-based robust design optimization of complex product. The effect of metamodel uncertainty on product performance uncertainty is quantified through uncertainty propagation analysis among design variables uncertainty, noise parameters uncertainty, metamodel uncertainty, and performance uncertainty. Then, the sampling points are selected and the metamodel is constructed based on the predictive interval of product performance and mean square error of the Kriging metamodel. The constructed metamodel is applied to robust design optimization considering multiple uncertainties. Results of two mathematical examples show that the proposed metamodel considering multiple uncertainties increases the result accuracy of robust design optimization. Finally, the proposed algorithm is applied to robust design optimization of a heat exchanger, and the total heat transfer rate is enhanced under uncertainties of fin structural parameters, operation conditions parameters and simulation metamodel.

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Crashworthiness-based lightweight design problem via new robust design method considering two sources of uncertainties

Cited by 19 publications

References 30 publications

Adaptive multi-point sequential sampling methodology for high-dimensional and highly nonlinear crashworthiness optimization problems

Adaptive multi-point sequential sampling methodology for high-dimensional and highly nonlinear crashworthiness optimization problems

A Strategy for Adaptive Sampling of Multi-Fidelity Gaussian Processes to Reduce Predictive Uncertainty

Simulation-based robust design of complex product considering uncertainties of metamodel, design variables, and noise parameters

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