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Title: Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

Abstract

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditions can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogatemore » and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

Authors:
 [1];  [2]; ORCiD logo [2];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1426798
Alternate Identifier(s):
OSTI ID: 1438146
Report Number(s):
SAND-2018-2012J; LA-UR-17-29959
Journal ID: ISSN 0927-0256; 660923
Grant/Contract Number:  
AC04-94AL85000; NA0003525; AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 148; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Radiation damage; Cluster dynamics modeling; Design of computer experiments; Statistical analysis; Reduced-order model

Citation Formats

Stewart, James A., Kohnert, Aaron A., Capolungo, Laurent, and Dingreville, Rémi Philippe Michel. Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions. United States: N. p., 2018. Web. doi:10.1016/j.commatsci.2018.02.048.
Stewart, James A., Kohnert, Aaron A., Capolungo, Laurent, & Dingreville, Rémi Philippe Michel. Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions. United States. doi:10.1016/j.commatsci.2018.02.048.
Stewart, James A., Kohnert, Aaron A., Capolungo, Laurent, and Dingreville, Rémi Philippe Michel. Tue . "Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions". United States. doi:10.1016/j.commatsci.2018.02.048.
@article{osti_1426798,
title = {Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions},
author = {Stewart, James A. and Kohnert, Aaron A. and Capolungo, Laurent and Dingreville, Rémi Philippe Michel},
abstractNote = {The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditions can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.},
doi = {10.1016/j.commatsci.2018.02.048},
journal = {Computational Materials Science},
number = C,
volume = 148,
place = {United States},
year = {Tue Mar 06 00:00:00 EST 2018},
month = {Tue Mar 06 00:00:00 EST 2018}
}

Journal Article:
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