Inverse identification of temperature-dependent thermal conductivity coefficients in an orthotropic charring composite

Tahmasbi, Vahid; Noori, Sahar

doi:10.1016/j.applthermaleng.2020.116219

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…Yin et al [16] used Abaqus 2019 to solve the forward problem based on a heat-proof tile structure, combined with Isight 2019 for the inversion, and used the LM algorithm to effectively invert the identification parameters. Tahmasbi et al [17] proposed using the idea of the inverse problem of heat conduction to invert the thermophysical parameters of carbonized composite materials. Considering that carbonized composite materials are orthotropic materials, they established a simultaneous inversion of the thermal conductivity coefficient in the plane and thickness directions using the multidimensional inversion method.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Reconstruction of Multiple Time-Varying Thermal Properties Based on Translucent Materials

Dong,

Fan,

Duan

et al. 2024

Materials

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In the realm of high-tech materials and energy applications, accurately measuring the transient heat flow at media boundaries and the internal thermal conductivity of materials in harsh heat exchange environments poses a significant challenge when using conventional direct measurement methods. Consequently, the study of photothermal parameter reconstruction in translucent media, which relies on indirect measurement techniques, has crucial practical value. Current research on reconstructing photothermal properties within participating media typically focuses on single-objective or time-invariant properties. There is a pressing need to develop effective methods for the simultaneous reconstruction of time-varying thermal flow fields and internal thermal conductivity at the boundaries of participating media. This paper introduces a computational model based on the numerical simulation theory of internal heat transfer systems in participating media, stochastic particle swarm optimization algorithms, and Kalman filter technology. The model aims to enable the simultaneous reconstruction of various thermal parameters within the target medium. Our results demonstrate that under varying levels of measurement noise, the inversion results for different target parameters exhibit slight oscillations around the true values, leading to a reduction in reconstruction accuracy. However, overall, the model demonstrates robustness and accuracy in ideal conditions, validating its effectiveness.

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Section: Introductionmentioning

confidence: 99%