Effect of Heat Source and Imperfect Contact on Simultaneous Estimation of Thermal Properties of High‐Conductivity Materials

D'Alessandro, Giampaolo; Monte, Filippo de; Amos, Donald E.

doi:10.1155/2019/5945413

Cited by 10 publications

(4 citation statements)

References 17 publications

(18 reference statements)

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“…These difficulties were also observed by other researchers [43,51] who tried to obtain experimentally greater sensitivity coefficients, since points closer to the surface with the incidence of heat flux have higher sensitivity coefficients [52]. But, D'Alessandro et al [18] showed the temperature data at x = L has enough sensibility, and then this position can be taken as the best one for the thermocouple when simultaneously estimating thermal properties accounting for contact resistance.…”

Section: Stainless Steel 304mentioning

confidence: 93%

“…Moreover, it was found that the loading pressure exerts a limited impact on significantly improving the heat exchange on the contacting surfaces. D'Alessandro et al [18] used a lumped system to study the impact of imperfect thermal contact in thermal property estimation of high-conductivity materials; however, no experiment was carried out to assess the contact resistance between the test specimen and the heater.…”

Section: Introductionmentioning

confidence: 99%

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Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

Ramos

Carollo

Silva

2020

Meas. Sci. Technol.

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This work aims to present a method for simultaneously estimating the thermal conductivity, k, and specific heat, c p , in samples of stainless steel 304 and cemented carbide by accounting the imperfect thermal contact at the heater-sample interface. A transient one-dimensional heat conduction model with constant thermal properties is used. The metallic sample is placed in the middle of a polyimide heater and a thermal insulator. A constant heat flux is applied on the specimen upside surface and a thermal insulation condition is maintained on the opposing surface, where a type T thermocouple measures the temperature response. Contact resistance is determined and accounted for as a reducing factor on heat flux. Instead of considering a lumped model, a microscopic approach of the contacting regions is used to describe both the surface roughness and the fluid gap. With the intention of guaranteeing simultaneous and reliable estimates for both thermal properties, sensitivity analysis is used to establish heat flux intensity, experiment duration, time step for data acquisition, and other characteristics of the experiments. To simultaneously achieve both thermal properties, the optimization method BFGS (Broyden-Fletcher-Goldfarb-Shanno) is employed to minimize a least-squares objective function comparing experientially measured and numerically calculated temperatures. The numerical solution for the transient conduction problem is determined with COMSOL, which solves the transient heat diffusion problem by discretizing it applying the finite element method (FEM). The linear system of equations is solved using the PARDISO solver and backward differentiation formula (BDF) method. Heat flux is estimated employing the sequential function specification method (SFSM) as a means of verifying the robustness of parameter estimation and experimental procedure. Moreover, uncertainty analysis is performed to confirm the quality of the results obtained. Finally, the uncertainty analysis outcomes and thermal properties estimated are compared with literature.

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Section: Stainless Steel 304mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

Ramos

Carollo

Silva

2020

Meas. Sci. Technol.

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“…Furthermore, the non-negligible contact resistance can be included in the effective thermal conductivity of the heater [125]. Exceptions are the research papers authored by Bovesecchi and Coppa [128] and by D'Alessandro et al [129], who used specific types of boundary conditions to account for the imperfect thermal contact.…”

Section: One-dimensional Modelsmentioning

confidence: 99%

Modeling and Measuring Thermodynamic and Transport Thermophysical Properties: A Review

et al. 2022

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The present review describes the up-to-date state of the evaluation of thermophysical properties (TP) of materials with three different procedures: modeling (also including inverse problems), measurements and analytical methods (e.g., through computing from other properties). Methods to measure specific heat and thermal conductivity are described in detail. Thermal diffusivity and thermal effusivity are a combination of the previously cited properties, but also for these properties, specific measurement and calculation methods are reported. Experiments can be carried out in steady-state, transient, and pulse regimes. For modeling, special focus is given to the inverse methods and parameter estimation procedures, because through them it is possible to evaluate the thermophysical property, assuring the best practices and supplying the measurement uncertainty. It is also cited when the most common data processing algorithms are used, e.g., the Gauss–Newton and Levenberg–Marquardt least squares minimization algorithms, and how it is possible to retrieve values of TP from other data. Optimization criteria for designing the experiments are also mentioned.

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“…Thus, data from the 1D experiment was measured at the bottom surface, i.e., at location x=L. Researchers have already shown that this location can transmit sensitive temperature information [36,52]. In Reference [17], researchers utilized D-optimality-based sensitivity analysis considering experimental aspects to identify the most sensitive zones in the 3D geometry studied here.…”

Section: Experimental Aspectsmentioning

confidence: 99%

Complementary transient thermal models and metaheuristics to simultaneously identify linearly temperature-dependent thermal properties of austenitic stainless steels

Ramos¹,

Antunes²,

Silva³

2022

Phys. Scr.

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This paper presents an experimental approach for simultaneously identifying the temperature-dependent thermal conductivity (k) and specific heat (cp) of 304 austenitic stainless steel (ASS) using complementary transient experiments and metaheuristics. Inverse thermal analysis was based on two heat conducting solids with different geometries. In estimation problems in general, one seeks to obtain as much sensitive data as possible using as few sensors as possible. Single thermocouple data were collected for each thermal model. An objective function fitting these complementary measurements to the corresponding numerical temperatures was minimized using the Lichtenberg algorithm. This metaheuristic algorithm takes advantage of more sensitive information provided by using complementary data, enabling for an accurate inverse solution, even when dealing with wide search ranges. The proposed technique provides a cost-effective and robust property estimation from tests conducted at room temperature. Single-step estimation occurred throughout the whole temperature domain to determine the parameters for linear functions representing the temperature dependence of k and cp. The obtained lines agreed well with curves from the literature. The 95% confidence bounds for the parameters of interest indicated deviations below ± 8.5%. Error analysis considering numerical and experimental processes showed an uncertainty close to ± 3%, applied to all estimated parameters.

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Effect of Heat Source and Imperfect Contact on Simultaneous Estimation of Thermal Properties of High‐Conductivity Materials

Cited by 10 publications

References 17 publications

Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

Modeling and Measuring Thermodynamic and Transport Thermophysical Properties: A Review

Complementary transient thermal models and metaheuristics to simultaneously identify linearly temperature-dependent thermal properties of austenitic stainless steels

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