Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks

TL;DR

This paper introduces a novel PINNs-based framework employing transfer learning for efficient simulation of transient heat conduction with a moving heat source, reducing computational effort while maintaining accuracy.

Contribution

A new transfer learning approach within PINNs is proposed to simulate large temporal intervals efficiently without increasing network complexity.

Findings

Good agreement with finite element method results

Reduced computational effort for long-term simulations

Effective handling of moving heat sources in heat transfer modeling

Abstract

In this paper, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source. To reduce the computational effort, a new training method is proposed that uses a continuous time-stepping through transfer learning. Within this, the time interval is divided into smaller intervals and a single network is initialized. On this single network each time interval is trained with the initial condition for (n+1)th as the solution obtained at nth time increment. Thus, this framework enables the computation of large temporal intervals without increasing the complexity of the network itself. The proposed framework is used to estimate the temperature distribution in a homogeneous medium with a moving heat source. The results from the proposed framework is compared with traditional finite element method and a good agreement is seen.