Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses

Zhang, Yingqi; Liu, Yaning; Pau, George; Oladyshkin, Sergey; Finsterle, Stefan

doi:10.1016/j.ijggc.2016.03.003

Title: Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses

Journal Article · Tue Mar 22 00:00:00 EDT 2016 · International Journal of Greenhouse Gas Control

DOI:https://doi.org/10.1016/j.ijggc.2016.03.003· OSTI ID:1471015

Zhang, Yingqi ^[1]; Liu, Yaning ^[1]; Pau, George ^[1]; Oladyshkin, Sergey ^[2]; Finsterle, Stefan ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. of Stuttgart, Stuttgart (Germany). Dept. of Stochastic Simulation and Safety Research for Hydrosystems (IWS/SRC SimTech)

Variance-based global sensitivity analysis (e.g., the Sobol' sensitivity index) can be used to identify the important parameters over the entire parameter space. However, one often cannot afford the computational costs of sampling-based approaches in combination with expensive high-fidelity forward models. Reduced-order models (ROM) can substantially accelerate calculation of these sensitivities. However, it is usually difficult to determine what type of ROM should be used and how accurately the ROM represents the high-fidelity model (HFM) results. Here in this paper, we propose to concurrently use multiple ROMs as a way to assess the robustness of the model-reduction method. Two sets of HFM simulations are needed, one set for building ROMs and the other for validating ROMs. Our goal is to keep the total number of HFM simulations to a minimum. Ideally some of the HFM simulations in the first set can be shared by different ROMs. Based on validation results, the ROMs can be combined with different schemes. We demonstrate that we can achieve the goal by using four different ROMs and still considerably save computational time compared to using traditional HFM simulation for calculating sensitivity indices. We apply the approach to an example problem of a large-scale geological carbon dioxide storage system, in which the objective is to calculate a sensitivity index to identify important parameters. For this problem, the locally best ROM provides better estimates than the weighted average from all ROMs.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1471015

Alternate ID(s):: OSTI ID: 1349128

Journal Information:: International Journal of Greenhouse Gas Control, Vol. 49, Issue C; ISSN 1750-5836

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 12 works

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