Model updating method for hybrid simulation based on global sensitivity analysis

2021 ◽  
Author(s):  
Weipeng Zhong ◽  
Zaixian Chen
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Model Updating ◽  
Hybrid Simulation ◽  
Global Sensitivity ◽  
Simulation Based

scholarly journals A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

2021 ◽  
Vol 7 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Xujia Zhu ◽  
Giuseppe Abbiati ◽  
Stefano Marelli ◽  
Bruno Sudret ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Surrogate Model ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Hybrid Models ◽  
Analysis Framework ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Realistic Situation

Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.

scholarly journals A Global Sensitivity Analysis Framework for Hybrid Simulation

2021 ◽  
Author(s):  
Giuseppe Abbiati ◽  
Stefano Marelli ◽  
Nikolaos Tsokanas ◽  
Bruno Sudret ◽  
Bozidar Stojadinovic
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Analysis Framework ◽  
Global Sensitivity ◽  
Sensitivity Indices

Hybrid Simulation is a dynamic response simulation paradigm that merges physical experiments and computational models into a hybrid model. In earthquake engineering, it is used to investigate the response of structures to earthquake excitation. In the context of response to extreme loads, the structure, its boundary conditions, damping, and the ground motion excitation itself are all subjected to large parameter variability. However, in current seismic response testing practice, Hybrid Simulation campaigns rely on a few prototype structures with fixed parameters subjected to one or two ground motions of different intensity. While this approach effectively reveals structural weaknesses, it does not reveal the sensitivity of structure's response. This thus far missing information could support the planning of further experiments as well as drive modeling choices in subsequent analysis and evaluation phases of the structural design process.This paper describes a Global Sensitivity Analysis framework for Hybrid Simulation. This framework, based on Sobol' sensitivity indices, is used to quantify the sensitivity of the response of a structure tested using the Hybrid Simulation approach due to the variability of the prototype structure and the excitation parameters. Polynomial Chaos Expansion is used to surrogate the hybrid model response. Thereafter, Sobol' sensitivity indices are obtained as a by-product of polynomial coefficients, entailing a reduced number of Hybrid Simulations compared to a crude Monte Carlo approach. An experimental verification example highlights the excellent performance of Polynomial Chaos Expansion surrogates in terms of stable estimates of Sobol' sensitivity indices in the presence of noise caused by random experimental errors.

scholarly journals A global sensitivity analysis framework for hybrid simulation

2021 ◽  
Vol 146 ◽  
pp. 106997 ◽  
Author(s):  
G. Abbiati ◽  
S. Marelli ◽  
N. Tsokanas ◽  
B. Sudret ◽  
B. Stojadinović
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Analysis Framework ◽  
Global Sensitivity

Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty

2004 ◽  
Author(s):  
Wei Chen ◽  
Ruichen Jin ◽  
Agus Sudjianto
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simulation Models ◽  
Design Under Uncertainty ◽  
Global Sensitivity ◽  
Functional Relationships ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Input Variables ◽  
The Impact

The importance of sensitivity analysis in engineering design cannot be over-emphasized. In design under uncertainty, sensitivity analysis is performed with respect to the probabilistic characteristics. Global sensitivity analysis (GSA), in particular, is used to study the impact of variations in input variables on the variation of a model output. One of the most challenging issues for GSA is the intensive computational demand for assessing the impact of probabilistic variations. Existing variance-based GSA methods are developed for general functional relationships but require a large number of samples. In this work, we develop an efficient and accurate approach to GSA that employs analytic formulations derived from metamodels of engineering simulation models. We examine the types of GSA needed for design under uncertainty and derive generalized analytical formulations of GSA based on a variety of metamodels commonly used in engineering applications. The benefits of our proposed techniques are demonstrated and verified through both illustrative mathematical examples and the robust design for improving vehicle handling performance.

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

2021 ◽  
Vol 4 (1) ◽  
pp. 1-21
Author(s):  
Nikolaos Tsokanas ◽  
Roland Pastorino ◽  
Božidar Stojadinović
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Surrogate Modeling ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Model Response ◽  
Modeling Techniques

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.

scholarly journals A global sensitivity analysis framework for hybrid simulation with stochastic substructures

2021 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Xujia Zhu ◽  
Giuseppe Abbiati ◽  
Stefano Marelli ◽  
Bruno Sudret ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Hybrid Models ◽  
Analysis Framework ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Model Response ◽  
Realistic Situation

Hybrid simulation is used to investigate the experimental dynamic response of a component or sub-assembly of a prototype structure using a hybrid model. The latter comprises both physically-tested and numerically-simulated substructures interacting with each other in a real-time feedback loop. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol' sensitivity indices are computed for substructure quantity of interest response quantiles. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.

scholarly journals Parameter selection for model updating with global sensitivity analysis

2019 ◽  
Vol 115 ◽  
pp. 483-496 ◽  
Author(s):  
Zhaoxu Yuan ◽  
Peng Liang ◽  
Tiago Silva ◽  
Kaiping Yu ◽  
John E. Mottershead
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Model Updating ◽  
Parameter Selection ◽  
Global Sensitivity ◽  
Selection For

scholarly journals A survey of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

2021 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Roland Pastorino ◽  
Bozidar Stojadinovic
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Surrogate Modeling ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Model Response ◽  
Modeling Techniques

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 loading-rate-sensitive 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 has been extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation is conceived as deterministic. However, associated uncertainties are present whilst simulation deviation due to their presence could be significant. To 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 unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques are used to alleviate this burden. In this paper, three different surrogate modeling methods are examined, namely polynomial chaos expansion, Kriging and polynomial chaos Kriging. 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 results highlight potential deviations.

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