Thermal Conductivity of Uranium Dioxide and Armco Iron by an Improved Radial Heat Flow Technique

Godfrey, T.G.; Fulkerson, W.; Kollie, T.G.; Moore, J. P.; McElroy, D.L.

doi:10.2172/4073146

Cited by 15 publications

(8 citation statements)

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“…Figure 6(b) shows comparison between our predicted effective lattice thermal conductivity and the available experiments for sintered-powder bulk UO 2 . 1, 13,22,73,74 The effective lattice thermal conductivity results for the polycrystalline and porous UO 2 are in good agreement with the experiments. In Appendices A to C, we estimate the contributions from electrons, small polarons, and photons.…”

Section: B Effective Thermal Conductivitysupporting

confidence: 70%

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Lattice thermal conductivity of UO2 using ab-initio and classical molecular dynamics

Kim

Kaviany³

2014

Journal of Applied Physics

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Section: B Effective Thermal Conductivitysupporting

confidence: 70%

“…(b) Temperature dependence of the predicted effective lattice thermal conductivity of UO 2 . Available experimental results13,22,73,74 and fit to them 1,22 are also shown.…”

mentioning

confidence: 99%

Lattice thermal conductivity of UO2 using ab-initio and classical molecular dynamics

Kim

Kaviany³

2014

Journal of Applied Physics

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“…* Thermal-conductivity data consist of measurements by Godfrey et ai. 5 and Stora et at. 6 The only data excluded in the analysis are the data of Bates from cycles 1 and 2 of sample 1 and the heating portion of cycle 1 of sample 3 as recommended by Bates.…”

Section: A Selection Of Data Basementioning

confidence: 91%

“…D. Slagle 9 based on measurements of Christensen 10 was used. This equation is p = p o (l.0056 -1.6324 * 10" 5 T -8.3281 x 10" 9 T 2 + 2.0176 x 10" 13 T 3…”

Section: B Methodsmentioning

confidence: 99%

Thermal conductivity and thermal diffusivity of solid UO/sub 2/

Fink¹,

Chasanov²,

Leibowitz³

1981

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New equations for the thermal conductivity of solid UOg were derived based upon a nonlinear least squares fit of the data available in the literature. In the development of these equations, consideracion was given to their thermodynamic consistency with heat capacity and density and theoretical consistency with enthalpy and heat capacity. Consistent with our previous treatment of enthalpy and heat capacity, 2670 K was selected as the temperature of a phase transition. A nonlinear equation, whose terms represent contributions due to phonons and electrons, was selected for the temperature region below 2670 K. Above 2670 K, the data were fit by a linear equation.

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“…To accomplish this, the info-gap model in Equation 17is modified such that all of the uncertain model parameters are held fixed at their nominal values, except for the th parameter. Equation (20) presents this modified info-gap model of uncertainty as…”

Section: Robustness Of Individual Model Parametersmentioning

confidence: 99%

Assessing the Effect of Parametric Uncertainty on the FRAPCON-3.4 Thermal Conductivity Model

Stull

Williams

Unal

2012

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In the absence of a well-accepted, mathematical description of how units of material respond to one or more external actions, scientists must often rely upon empirically-derived models to make predictions. These models are often employed to model very complex phenomena, with an unfortunate side-effect that intuition, as to the values of particular model parameters, may not serve to guide their use. Given our increased reliance on modeling and simulation to make predictions in the absence of experimental data, it befits the scientific and engineering communities to explore and report upon uncertainty quantification techniques applied to previously adopted (or at least well-accepted) empirical models that derive from or pertain to substantial experimental data sets. This report represents a collection of three methodologies aimed at assessing the predictive capability of the empirical thermal conductivity model adopted by the nuclear fuel performance code, FRAPCON-3.4. Each of these methodologies considers the effect of uncertain parameters-a plausible reality in the context of empirically-derived models-on the ability of the model to predict uranium dioxide conductivity data from open literature sources. The results lead the authors to question the predictive capability of the FRAPCON model for predicting the thermal conductivities associated with irradiated fuel samples. The report concludes with a preliminary examination of Idaho National Laboratory's (INL) nuclear fuel performance code, BISON, developed under INL's Multiphysics Object Oriented Simulation Environment (MOOSE).

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Thermal Conductivity of Uranium Dioxide and Armco Iron by an Improved Radial Heat Flow Technique

Abstract: A description of a n improved r a d i a l h e a t flow technique for measuring t h e thermal conductivity of s o l i d s in the range-57 t o 1 1 0 0~~ is presented. T h e technique yielded results with a probable accuracy of f1.5% and a reproducibility of f0.1% in t h i s range.

Cited by 15 publications

References 0 publications

Lattice thermal conductivity of UO2 using ab-initio and classical molecular dynamics

Lattice thermal conductivity of UO2 using ab-initio and classical molecular dynamics

Thermal conductivity and thermal diffusivity of solid UO/sub 2/

Assessing the Effect of Parametric Uncertainty on the FRAPCON-3.4 Thermal Conductivity Model

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