CNN-based surrogate model and temperature prediction method using superposition principle

Abstract

A CNN-based surrogate model is being developed to accelerate CFD calculations. In order to use this surrogate model for design development, it is necessary to improve generalizability. One solution to this problem is to use the principle of superposition. For the multiple heating elements that make up the model, their temperatures are predicted by heating them individually. We devised a method to predict the temperature of the entire model by adding up these individually predicted temperature distributions. Radiation and convection phenomena, for which the superposition principle does not hold, were also considered.

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