Predicting Surface Heat Flux on Complex Systems via Conv-LSTM

TL;DR

This paper introduces a Convolutional-LSTM based framework for real-time prediction of surface heat flux in complex 3D systems, leveraging deep learning to improve efficiency and accuracy over traditional iterative methods.

Contribution

The paper presents a novel Convolutional-LSTM framework for inverse heat conduction problems with complex structures, enabling faster and more accurate heat flux predictions.

Findings

High prediction accuracy achieved with the framework.

Significant reduction in computation time compared to traditional methods.

Effective handling of non-linear boundary conditions and complex geometries.

Abstract

Existing algorithms with iterations as the principle for 3D inverse heat conduction problems (IHCPs) are usually time-consuming. With the recent advancements in deep learning techniques, it is possible to apply the neural network to compute IHCPs. In this paper, a new framework based on Convolutional-LSTM is introduced to predict the transient heat flux via measured temperature. The inverse heat conduction models concerned in this work have 3D complex structures with non-linear boundary conditions and thermophysical parameters. In order to reach high precision, a forward solver based on the finite element method is utilized to generate sufficient data for training. The fully trained framework can provide accurate predictions efficiently once the measured temperature and models are acquired. It is believed that the proposed framework offers a new pattern for real-time heat flux inversion.