The use of partially converged simulations in building surrogate models

This paper deals with the advantages of using variable-fidelity metamodeling strategies in order to develop a valid metamodel more rapidly than by using traditional methods. In our mechanical assembly design, we use the term "variable-fidelity" in reference to the convergence (or accuracy) level of the iterative solver being used. Variable-fidelity metamodeling is a way to improve the prediction of the output of a complex system by incorporating rapidly available auxiliary lower-fidelity data. This work uses two fidelity levels, but more levels can be added. The LATIN iterative algorithm is used along with a "multiparametric" strategy to calculate the various data and their different fidelity levels by means of an error indicator. Three main categories of variable-fidelity strategies are currently available. We tested at least one method from each of these categories, which comes to a total of five methods for calculating a valid metamodel using low-and high-fidelity data. Here, our objective is to compare the performances of these five methods in solving three mechanical examples.

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“…In this context, the use of metamodels is mandatory. More generally, as mentioned in [2,3], metamodels can be classified into three categories:…”

Section: Introductionmentioning

confidence: 99%

Variable-fidelity modeling of structural analysis of assemblies

2015

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“…On the other hand, the iteration for the partially converged simulation to be used as the LF simulations is set to 100, which means that the computational cost ratio between the LF and HF simulation is 0.2 for this case. This fully and partially converged simulation strategy has been employed for optimization [83,84], surrogate model construction [85], and UQ [71]. One advantage of this strategy is that some LF samples are evaluated together with the HF samples, which leads to further reduction in the computational cost.…”

Section: Transonic Airfoil In Inviscid Flowmentioning

confidence: 99%

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Palar

Zuhal

Shimoyama

et al. 2018

Reliability Engineering & System Safety

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The presence of uncertainties are inevitable in engineering design and analysis, where failure in understanding their effects might lead to the structural or functional failure of the systems. The role of global sensitivity analysis in this aspect is to quantify and rank the effects of input random variables and their combinations to the variance of the random output. In problems where the use of expensive computer simulations are required, metamodels are widely used to speed up the process of global sensitivity analysis. In this paper, a multi-fidelity framework for global sensitivity analysis using polynomial chaos expansion (PCE) is presented. The goal is to accelerate the computation of Sobol sensitivity indices when the deterministic simulation is expensive and simulations with multiple levels of fidelity are available. This is especially useful in cases where a partial differential equation solver computer code is utilized to solve engineering problems. The multi-fidelity PCE is constructed by combining the lowfidelity and correction PCE. Following this step, the Sobol indices are computed using this combined PCE. The PCE coefficients for both low-fidelity and correction PCE are computed with spectral projection technique and sparse grid integration. In order to demonstrate the capability of the proposed method for sensitivity analysis, several simulations are conducted. On the aerodynamic example, the multi-fidelity approach is able to obtain an accurate value of Sobol indices with 36.66% computational cost compared to the standard single-fidelity PCE for a nearly similar accuracy.

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“…Expected Improvement criterion involves a measure of possible improvement. This criterion is largely used [68][69][70][71][72][73]. The Particle Swarm Optimisation Intelligent Sampling method, which combines a Particle Swarm Optimisation method, used to add new experiments, and adaptive response surface methods metamodel are used [52].…”

Section: Adaptive Design Of Experimentsmentioning

confidence: 99%

Simulation data management for adaptive design of experiments: A litterature review

Blondet

Duigou

Boudaoud

et al. 2015

Mechanics & Industry

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-Recent evolutions of computer-aided product development and massive integration of numerical simulations to the design process require new methodologies to manage the continuously increasing flow of data and decrease the computational costs of numerical design of experiments. This paper presents a literature review of Simulation Data Management strategy and adaptive design of experiments methodology to detect possible links between these two fields and identify potential improvements for simulation process shortening. Adaptive design of experiments is based on several methods implying a profusion of different technics. Re-using best practices may help designers to choose relevant methods to reduce computational cost and simulation process duration.

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The use of partially converged simulations in building surrogate models

Cited by 10 publications

References 37 publications

Variable-fidelity modeling of structural analysis of assemblies

Variable-fidelity modeling of structural analysis of assemblies

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Simulation data management for adaptive design of experiments: A litterature review

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