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Title: Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling

Abstract

Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process) to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper,more » the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.« less

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Clemson Univ., SC (United States). Glenn Dept. of Civil Engineering
  3. Clemson Univ., SC (United States). Mathematical Sciences
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1440474
Report Number(s):
LA-UR-17-27338
Journal ID: ISSN 0264-4401
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Engineering Computations
Additional Journal Information:
Journal Volume: 35; Journal Issue: 2; Journal ID: ISSN 0264-4401
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Stevens, Garrison N., Atamturktur, Sez, Brown, D. Andrew, Williams, Brian J., and Unal, Cetin. Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling. United States: N. p., 2018. Web. doi:10.1108/EC-07-2016-0264.
Stevens, Garrison N., Atamturktur, Sez, Brown, D. Andrew, Williams, Brian J., & Unal, Cetin. Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling. United States. doi:10.1108/EC-07-2016-0264.
Stevens, Garrison N., Atamturktur, Sez, Brown, D. Andrew, Williams, Brian J., and Unal, Cetin. Mon . "Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling". United States. doi:10.1108/EC-07-2016-0264. https://www.osti.gov/servlets/purl/1440474.
@article{osti_1440474,
title = {Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling},
author = {Stevens, Garrison N. and Atamturktur, Sez and Brown, D. Andrew and Williams, Brian J. and Unal, Cetin},
abstractNote = {Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process) to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper, the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.},
doi = {10.1108/EC-07-2016-0264},
journal = {Engineering Computations},
number = 2,
volume = 35,
place = {United States},
year = {Mon Apr 16 00:00:00 EDT 2018},
month = {Mon Apr 16 00:00:00 EDT 2018}
}

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