Comparison of Different Upscaling Methods for Predicting Thermal Conductivity of Complex Heterogeneous Materials System:
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THERMAL conductivity is an important property of complex heterogeneous materials in nuclear application, electronic packaging, thermal insulation, etc. The effective thermal conductivity of a material may be (1) tailored by microstructure design through thermomechanical processing or (2) improved by adjusting more elements or components. It is critical to predict the effective thermal conductivity to evaluate the life or performance of a complex system in industry including nuclear application. Building up this capability is also essential in inverse materials design. In predicting the thermal conductivity of waste forms, most current models do not consider the microstructure evolution under irradiation or anisotropy in a microstructure. The influence of molecular structure, lattice parameters, and conductive mechanisms has been investigated, and several empirical and theoretical laws have been proposed. The influence of microstructure, however, has not been addressed in the modeling of thermal conductivity because traditional unloaded glass exhibits random microstructure and thus possesses isotropic properties. Such randomness, a hidden assumption in most current models, does not facilitate the prediction of
DONGSHENG LI, XIN SUN, and MOHAMMAD KHALEEL, Staff Scientists, are with the Fundamental and Computational Science Directorate, Pacific Northwest National Laboratory, Richland, WA 99352. Contact e-mail: [email protected] Manuscript submitted January 24, 2012. Article published online June 16, 2012 METALLURGICAL AND MATERIALS TRANSACTIONS A
thermal conductivity in waste forms with engineered microstructures, which can be designed with anisotropic properties and may ultimately be preferable. Generally, fabricated pure waste form packages are isotropic. However, loaded and irradiated waste forms usually do not retain the same random microstructure. Taking into consideration the anisotropy introduced during loading and after storage is crucial in property prediction and risk analysis. Balancing efficiency and accuracy is an interesting and universal topic in industry, science, and engineering. This study investigates different thermal conductivity prediction models to develop a strategy for efficient upscaling. The Taylor model, Sachs model, self-consistent model, statistical upscaling method, and finite-element method (FEM) are developed, implemented, and compared. The efficiency and accuracy in predicting thermal conductivity of loaded nuclear waste form is investigated. Predicting the thermal conductivity of nuclear waste is critical to nuclear industry. After several decades of stagnation, nuclear power is experiencing a revival.[1–3] In early 2008, 439 nuclear power reactors accounted for approximately 15 pct of electricity production worldwide. In February 2010, the U.S. Department of Energy (DOE) approved an $ 8.3 billion loan guarantee to build two new reactors in the state of Georgia.[4] If this project moves forward, then these two new reactors would be the first in the United States since the 1970s. Compared with ot
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