A versatile inversion approach for space/temperature/time-related thermal conductivity via deep learning

Abstract

Identifying the thermophysical properties of unknown material through the measurement of temperature is of great significance in computational heat transfer. Existing numerical algorithms are generally computationally cumbersome and resource demanding. Rapid advances in deep learning (DL) offer an alternative pathway to speed up the inversion process by fully utilizing the parallel computing ability of Graphics Processing Units (GPUs). In this paper, a DL framework is proposed to reconstruct the thermal conductivity related to space, temperature or time. The whole framework consists of a forward data generation module, a denoising module and an inversion module. It is noteworthy that the physics informed neural network (PINN) is employed in the process of generating training data, which avoids the use of commercial software based on traditional methods. In order to simulate the measurement error in practical scenarios, a certain intensity of Gaussian noise is added to the generated data. After denoising by the U-net, the measured temperature is fed to the nonlinear mapping module (NMM) for the inversion of the unknown thermal conductivity. As a result, a well-trained framework can realize high precision real-time inversion even with intensive environmental noise, offering great potential for applications pertaining to the reconstruction of thermophysical properties.