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Title: REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling

Abstract

Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations. These computationally efficient versions are known as reduced-order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods that can be used to generate a ROM, automatically quantifies the ROM accuracy, and provides support for an iterative approach to improve ROM accuracy. REVEAL is designed to be extensible in order to utilize the core functionality with any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques to best suit the characteristics of their model. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1132693
Report Number(s):
PNNL-SA-90857
AA9010100
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Computing in Science & Engineering, 16(2):44-53
Additional Journal Information:
Journal Name: Computing in Science & Engineering, 16(2):44-53
Country of Publication:
United States
Language:
English
Subject:
Surrogate model, reduced order model, sensitivity analysis, simulation

Citation Formats

Agarwal, Khushbu, Sharma, Poorva, Ma, Jinliang, Lo, Chaomei, Gorton, Ian, and Liu, Yan. REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling. United States: N. p., 2013. Web. doi:10.1109/MCSE.2013.46.
Agarwal, Khushbu, Sharma, Poorva, Ma, Jinliang, Lo, Chaomei, Gorton, Ian, & Liu, Yan. REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling. United States. doi:10.1109/MCSE.2013.46.
Agarwal, Khushbu, Sharma, Poorva, Ma, Jinliang, Lo, Chaomei, Gorton, Ian, and Liu, Yan. Tue . "REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling". United States. doi:10.1109/MCSE.2013.46.
@article{osti_1132693,
title = {REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling},
author = {Agarwal, Khushbu and Sharma, Poorva and Ma, Jinliang and Lo, Chaomei and Gorton, Ian and Liu, Yan},
abstractNote = {Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations. These computationally efficient versions are known as reduced-order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods that can be used to generate a ROM, automatically quantifies the ROM accuracy, and provides support for an iterative approach to improve ROM accuracy. REVEAL is designed to be extensible in order to utilize the core functionality with any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques to best suit the characteristics of their model. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.},
doi = {10.1109/MCSE.2013.46},
journal = {Computing in Science & Engineering, 16(2):44-53},
number = ,
volume = ,
place = {United States},
year = {2013},
month = {4}
}