Traditional updating methods based on numerical simulation are computationally expensive and are not suitable for model updating with limited resources or time constraints. The computational model updating calls for effi-ciency improvements urgently to address this issue. In this study, a computational model updating method that replaces numerical simulations with surrogate models is proposed to reduce computational costs. A Kriging model is established using the thermal conductivity and emissivity of each layer of the multi-layer insulation structure as input and the temperature of the inner surface of the structure as output. Compared to numerical simulation, the Kriging model offers a significant improvement in computational efficiency, resulting in a three-order-of-magnitude reduction in computational costs while maintaining a prediction accuracy within 0.02%. The proposed method successfully updated the computational model with a maximum relative error of only 0.015%. The method provided can serve as a valuable tool for the rapid thermal design and parameter inverse identification of multilayer thermal insulation structures.
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