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Title: Computational Efficient Upscaling Methodology for Predicting Thermal Conductivity of Nuclear Waste forms

Abstract

This study evaluated different upscaling methods to predict thermal conductivity in loaded nuclear waste form, a heterogeneous material system. The efficiency and accuracy of these methods were compared. Thermal conductivity in loaded nuclear waste form is an important property specific to scientific researchers, in waste form Integrated performance and safety code (IPSC). The effective thermal conductivity obtained from microstructure information and local thermal conductivity of different components is critical in predicting the life and performance of waste form during storage. How the heat generated during storage is directly related to thermal conductivity, which in turn determining the mechanical deformation behavior, corrosion resistance and aging performance. Several methods, including the Taylor model, Sachs model, self-consistent model, and statistical upscaling models were developed and implemented. Due to the absence of experimental data, prediction results from finite element method (FEM) were used as reference to determine the accuracy of different upscaling models. Micrographs from different loading of nuclear waste were used in the prediction of thermal conductivity. Prediction results demonstrated that in term of efficiency, boundary models (Taylor and Sachs model) are better than self consistent model, statistical upscaling method and FEM. Balancing the computation resource and accuracy, statistical upscaling is a computationalmore » efficient method in predicting effective thermal conductivity for nuclear waste form.« less

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1029432
Report Number(s):
PNNL-20736
AF5831060; TRN: US1200025
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
12 MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES; ACCURACY; AGING; COMPARATIVE EVALUATIONS; CORROSION RESISTANCE; DEFORMATION; EFFICIENCY; FINITE ELEMENT METHOD; FORECASTING; MICROSTRUCTURE; PERFORMANCE; RADIOACTIVE WASTES; SAFETY; STORAGE; THERMAL CONDUCTIVITY; WASTE FORMS; thermal conductivity, waste form, prediction, upscaling methodology, accuracy, efficiency, statistical upscaling,

Citation Formats

Li, Dongsheng, Sun, Xin, and Khaleel, Mohammad A. Computational Efficient Upscaling Methodology for Predicting Thermal Conductivity of Nuclear Waste forms. United States: N. p., 2011. Web. doi:10.2172/1029432.
Li, Dongsheng, Sun, Xin, & Khaleel, Mohammad A. Computational Efficient Upscaling Methodology for Predicting Thermal Conductivity of Nuclear Waste forms. United States. doi:10.2172/1029432.
Li, Dongsheng, Sun, Xin, and Khaleel, Mohammad A. Wed . "Computational Efficient Upscaling Methodology for Predicting Thermal Conductivity of Nuclear Waste forms". United States. doi:10.2172/1029432. https://www.osti.gov/servlets/purl/1029432.
@article{osti_1029432,
title = {Computational Efficient Upscaling Methodology for Predicting Thermal Conductivity of Nuclear Waste forms},
author = {Li, Dongsheng and Sun, Xin and Khaleel, Mohammad A.},
abstractNote = {This study evaluated different upscaling methods to predict thermal conductivity in loaded nuclear waste form, a heterogeneous material system. The efficiency and accuracy of these methods were compared. Thermal conductivity in loaded nuclear waste form is an important property specific to scientific researchers, in waste form Integrated performance and safety code (IPSC). The effective thermal conductivity obtained from microstructure information and local thermal conductivity of different components is critical in predicting the life and performance of waste form during storage. How the heat generated during storage is directly related to thermal conductivity, which in turn determining the mechanical deformation behavior, corrosion resistance and aging performance. Several methods, including the Taylor model, Sachs model, self-consistent model, and statistical upscaling models were developed and implemented. Due to the absence of experimental data, prediction results from finite element method (FEM) were used as reference to determine the accuracy of different upscaling models. Micrographs from different loading of nuclear waste were used in the prediction of thermal conductivity. Prediction results demonstrated that in term of efficiency, boundary models (Taylor and Sachs model) are better than self consistent model, statistical upscaling method and FEM. Balancing the computation resource and accuracy, statistical upscaling is a computational efficient method in predicting effective thermal conductivity for nuclear waste form.},
doi = {10.2172/1029432},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2011},
month = {9}
}

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