Radiative heat transfer in aerogels (semi-transparent materials) acts as a participating medium, causing notable errors in conventional steady-state thermal conductivity measurements. Coupled conduction–radiation heat transfer is numerically simulated to examine the influence of variations in the heating plate-specimen interface emissivity on thermal conductivity measurements, and the simulation results are experimentally validated using test systems with differing interface emissivities. The results show that the effect of interface emissivity on effective thermal conductivity is more obvious under high temperatures and low extinction coefficients. When the average temperature is 1273 K, the emissivity decreases from 1 to 0.2, and the effective thermal conductivity with extinction coefficients of 3.5 m⁻¹ and 3500 m⁻¹ decreases by 76.1% and 24.1%, respectively. Experimental results show that when the hot surface temperature is 873 K, the cold surface temperature differences in different test systems can reach 30 K. The experimental results have the same trend as the steady-state simulation results, which verifies the accuracy of the numerical simulations. Quantitative analysis of the steady-state heating measurement results demonstrates the effect of medium radiation in semi-transparent materials on the obtained results. The findings contribute to a more accurate characterization of silica aerogel composites and provide new insights into the influence of radiative heat transfer on thermal conductivity evaluation in semi-transparent aerogel materials, which is important for the development and application of aerogel-based thermal insulation systems.

This study proposes a prediction method for ceramic matrix composite thin plates (CMCTPs) with protective coatings under high-temperature conditions, based on the first-order shear deformation theory and the energy principle while considering thermal effects. It can successfully predict the variations in natural frequencies and resonant responses of CMCTPs over the experimentally validated temperature range of 25 °C to 800 °C, using a thermo-vibrational platform with a maximum temperature capability of 1500 °C. In addition, a thorough investigation of multiple key parameter influences on the dynamic characteristics of CMCTPs with and without coating is performed, with special focus on the effect of the coating on enhancing the thermo-vibrational resistance of such structures. The analytical results demonstrate that the protective coating significantly enhances the thermo-vibrational resistance of the structure. Optimization of the coating-to-substrate thickness ratio, elastic modulus ratio, and thermal expansion coefficient ratio is recommended to maximize vibration suppression performance, providing critical guidance for the dynamic design of coated CMCTP components in aerospace.

Accurate prediction of the stress–strain fields in fiber-reinforced composites is crucial for performance analysis and structural design. However, due to their complex microstructures, traditional finite element analysis (FEA) entails a very high computational cost. Therefore, this study proposes a conditional generative adversarial network (cGAN) framework, named FiberGAN, to enable rapid prediction of the microscopic stress–strain fields in fiber-reinforced composites. The method employs an adaptive representative volume element (RVE) generation algorithm to construct random fiber arrangements with fiber volume fractions ranging from 30% to 50% and uses FEA to obtain the corresponding stress and strain fields as training data. A U-Net generator, combined with a PatchGAN discriminator, captures both global distribution patterns and fine local details. Under tensile and shear loading conditions, the R² values of FiberGAN predictions range from 0.96 to 0.99, while the structural similarity index (SSIM) values range from 0.95 to 0.99. The error maps show that prediction residuals are mainly concentrated in high-gradient regions with small magnitudes. These results demonstrate that the proposed deep learning model can successfully predict stress–strain field distributions for different fiber volume fractions under various loading conditions.