Computational Evaluation of Thermal Barrier Coatings: Two-Phase Thermal Transport Analysis

Fathi

2020

Self Cite

Heterogeneity in a material’s composition can have significant impacts on the thermal response of the material system at both the fine-scale and macro-scale levels. This is especially true for porous materials, where voids in the bulk material induce thermal resistance across the system and divert heat flow within the material. Within the energy industry, understanding these effects can be critical to designing components and systems that are often subjected to high-temperature environments. The current investigation is dedicated to studying the effect of pore geometry on the effective thermal conductivity, keff, of the whole porous unit cell. To this end, a numerical assessment of non-circular porous material simulated thermal responses was performed. A set of two-dimensional, steady-state models were defined to observe the thermal effects of different pore geometries — including triangular, square, and elliptical — centered in a unit cell porous medium. The finite element method (FEM) was implemented in Fortran — using unstructured triangular meshes — to simulate the thermal response of the systems. The method of manufactured solutions (MMS) was used to perform code verification and shows that the implementation of the numerical methods is approximately second-order accurate. This paper presents a study on the effects of pore size, shape, and rotational orientation on a heterogeneous material’s dimensionless effective thermal conductivity, k*. Comparative thermal responses show the sensitivity of k* with respect to pore shape and the variable sensitivity of response to pore orientation, where pore size is a major driver in the effective thermal response.

Section: Solution Verificationmentioning

confidence: 99%

“…Oftentimes, heterogeneity is caused by porous structures within a material's solid domain. This is especially true in energy systems where thermal barrier coatings [1][2][3][4], semi-conductors [5], and/or energy storage materials [6] are used.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Pore Geometry Effects on Porous Cell Thermal Behavior

Fathi

2020

Self Cite

“…Using k2/k1=1 for convenience for the MMS study, the pO,H value for mesh H of each system configuration was expected to converge to 2, representing pf. A full, detailed description of the theory behind the code verification used in this study is explained in [21] and [22].…”

Section: Codementioning

confidence: 99%

“…The global deviation GCI method computes a factor of safety on the finest mesh k* solution value based on a quantitative estimation of where the solution falls with respect to the asymptotic solution regime. References [22] and [25] give a clear description of the global deviation GCI approach as used in…”

Section: Solutionmentioning

confidence: 99%

High-Fidelity Calculation of Effective Thermal Response of Composite Media With Heat Generation Source

Fathi

2020

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The complexity of conductive heat transfer in a structure increases with heterogeneity (e.g., multi-component solid-phase systems with a source of internal thermal heat generation). Any discontinuity of material property — especially thermal conductivity — would warrant a thorough analysis to evaluate the thermal behavior of the system of interest. Heterogeneous thermal conditions are crucial to heat transfer in nuclear fuel assemblies, because the thermal behavior within the assemblies is governed significantly by the heterogeneous thermal conditions at both the system and component levels. A variety of materials have been used as nuclear fuels, the most conventional of which is uranium dioxide, UO2. UO2 has satisfactory chemical and irradiation tolerances in thermal reactors, whereas the low thermal conductivity of porous UO2 can prove challenging. Therefore, the feasibility of enhancing the thermal conductivity of oxide fuels by adding a high-conductivity secondary solid component is still an important ongoing topic of investigation. Undoubtedly, long-term, stable development of clean nuclear energy would depend on research and development of innovative reactor designs and fuel systems. Having a better understanding of the thermal response of the unit cell of a composite that represents a fuel matrix cell would help to develop the next generation of nuclear fuel and understand potential performance enhancements. The aim of this article is to provide an assessment of a high-fidelity computational model response of heterogeneous materials with heat generation in circular fillers. Two-dimensional, steady-state systems were defined with a circular, heat-generating filler centered in a unit-cell domain. A Fortran-based finite element method (FEM) code was used to solve the heat equation on an unstructured triangular mesh of the systems. This paper presents a study on the effects of a heat-generating filler material’s relative size and thermal conductivity on effective thermal conductance, Geff, within a heterogenous material. Code verification using the method of manufactured solution (MMS) was employed, showing a second-order accurate numerical implementation. Solution verification was performed using a global deviation grid convergence index (GCI) method to assess solution convergence and estimate solution numerical uncertainty, Unum. Trend results are presented, showing variable response in Geff to filler size and thermal conductivity.

“…Proceedings of the ASME 2020 Verification and Validation Symposium VVS2020 May 20-22, 2020, Virtual, Online V001T03A004-1 systems-various forms of modeling are often employed to provide stakeholders with an assurance that the system of interest will perform in a certain way. For example, government agencies may want to be assured that weapon systems will engage properly and not cause unintentional harm [1][2][3], energy corporations may want to realize increased safety and longevity in power generation systems [4][5][6], or medical companies may want to reduce equipment failure rates [7][8][9]. Models can support system design goals by taking forms ranging from simple analytical models to complex physics finite element models.…”

Section: Vvs2020-8834mentioning

confidence: 99%

Full Function Sampling of Uncertain Correlations

Engerer

Lance

et al. 2020

Empirically-based correlations are commonly used in modeling and simulation but rarely have rigorous uncertainty quantification that captures the nature of the underlying data. In many applications, a mathematical description for a parameter response to some input stimulus is often either unknown, unable to be measured, or both. Likewise, the data used to observe a parameter response is often noisy, and correlations are derived to approximate the bulk response. Practitioners frequently treat the chosen correlation — sometimes referred to as the “surrogate” or “reduced-order” model of the response — as a constant mathematical description of the relationship between input and output. This assumption, as with any model, is incorrect to some degree, and the uncertainty in the correlation can potentially have significant impacts on system responses. Thus, proper treatment of correlation uncertainty is necessary. In this paper, a method is proposed for high-level abstract sampling of uncertain data correlations. Whereas uncertainty characterization is often assigned to scalar values for direct sampling, functional uncertainty is not always straightforward. A systematic approach for sampling univariable uncertain correlations was developed to perform more rigorous uncertainty analyses and more reliably sample the correlation space. This procedure implements pseudo-random sampling of a correlation with a bounded input range to maintain the correlation form, to respect variable uncertainty across the range, and to ensure function continuity with respect to the input variable.