A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

Nikolaos Tsokanas,Božidar Stojadinović,Roland Pastorino

doi:10.3390/make4010001

Nikolaos Tsokanas, Božidar Stojadinović + Show 1 more

Open Access

https://doi.org/10.3390/make4010001

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Abstract

Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.

Highlights

Hybrid simulation (HS), known as hardware-in-the-loop (HiL), is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario.The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically and, correspond to the physical substructures (PS), whereas the remaining substructures are simulated numerically, namely, the numerical substructures (NS)
This paper shows how different surrogate modeling methods can be used to perform global sensitivity analysis via Sobol’ indices in hybrid simulation
Estimation of the Sobol’ indices through Monte Carlo simulations of the original hybrid model is rarely affordable since many hybrid model evaluations are essential, while each evaluation relies on a single hybrid simulation

Summary

Introduction

Hybrid simulation (HS), known as hardware-in-the-loop (HiL), is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario.The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically and, correspond to the physical substructures (PS), whereas the remaining substructures are simulated numerically, namely, the numerical substructures (NS). Hybrid simulation (HS), known as hardware-in-the-loop (HiL), is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The coupling of PS and NS forms the so-called hybrid model. The response of the latter is obtained from a step-by-step numerical solution of the equations governing the motion of the underlying hybrid model, combined with measurements acquired from the PS. The employed transfer system accounts for the substructure coupling and, for synchronizing their dynamic boundary conditions in every time step of the HS. From the coupling of substructures, several challenges arise, e.g., time delays due to inherent transfer system dynamics or due to computational power needed to compute the NS response. A detailed overview of HS can be found in [7,8]

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