Identification of insulating materials thermal properties by inverse method using reduced order model

Castillo, A.G. Chavez; Gaume, Benjamin; Rouizi, Yassine; Quemener, Olivier; Glouannec, Patrick

doi:10.1016/j.ijheatmasstransfer.2020.120683

Cited by 13 publications

(2 citation statements)

References 41 publications

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“…[Yij-Tij] is the difference between the experimental temperatures (Yij) and the predicted temperatures (Tij), with index i refers to the time ti while the index j refers to the measurement points (nodes number), where 𝑁 𝑡 is the total number of time steps and 𝑁 𝑚𝑒𝑠 , the number of measurement points [7,8].…”

Section: 𝑓(𝑃) =mentioning

confidence: 99%

Inverse Method for Estimation of Thermophysics Properties of a Bimaterials

Koumekpo

Atchonouglo

Dupré

et al. 2022

KEM

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The aim of this study is to identify simultaneously the thermal conductivity tensor and the volume heat coefficient of a bimaterial (checkerboard) whose heat conduction obeys Fourier's law. This approach is validated by numerical simulation. The simulated temperature fields are obtained by the direct resolution heat conduction solved numerically by the finite element method formulation. To identify parameters, an inverse method is developed by using the finite element temperature approach (FEU-T) model fitting method based on the Levemberg-Marquardt algorithm. We validated the numerical procedure by using noiseless temperature fields at different time and space steps. The influence of the noise on the temperature fields is also studied and shows the efficiency of the inverse method. The results show that this procedure is not very sensitive to the number of elements (or space steps) and the number of time steps.

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Section: 𝑓(𝑃) =mentioning

confidence: 99%

Inverse Method for Estimation of Thermophysics Properties of a Bimaterials

Koumekpo

Atchonouglo

Dupré

et al. 2022

KEM

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“…Zhang [ 9 ] and Wang [ 20 ] et al used the software Fluent to conduct numerical studies on the TPS used to measure thin film and translucent materials. In addition, Bording [ 21 ] and Castillo [ 22 ] et al applied a numerical simulation to a study using a hot-wire method and obtained good test results. In order to reduce the error of the TPS model, a mathematical model that more closely represents the actual heat transfer process is established in this paper by considering the thickness and heat capacity of the probe.…”

Section: Introductionmentioning

confidence: 99%

Improved Bayesian Optimization Framework for Inverse Thermal Conductivity Based on Transient Plane Source Method

Liu

Lyu

et al. 2023

Entropy

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In order to reduce the errors caused by the idealization of the conventional analytical model in the transient planar source (TPS) method, a finite element model that more closely represents the actual heat transfer process was constructed. The average error of the established model was controlled at below 1%, which was a significantly better result than for the analytical model, which had an average error of about 5%. Based on probabilistic optimization and heuristic optimization algorithms, an optimization model of the inverse heat transfer problem with partial thermal conductivity differential equation constraints was constructed. A Bayesian optimization algorithm with an adaptive initial population (BOAAIP) was proposed by analyzing the influencing factors of the Bayesian optimization algorithm upon inversion. The improved Bayesian optimization algorithm is not affected by the range and individuals of the initial population, and thus has better adaptability and stability. To further verify its superiority, the Bayesian optimization algorithm was compared with the genetic algorithm. The results show that the inversion accuracy of the two algorithms is around 3% when the thermal conductivity of the material is below 100 Wm−1K−1, and the calculation speed of the improved Bayesian optimization algorithm is three to four times faster than that of the genetic algorithm.

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