Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks

TL;DR

This paper introduces a physics-driven CNN approach for heat conduction layout optimization that reduces computational costs by accurately predicting physical fields without extensive training data, enabling efficient design improvements.

Contribution

The paper presents a novel PD-CNN method that infers heat conduction fields directly from physics principles, eliminating the need for large training datasets in layout optimization.

Findings

PD-CNN predicts heat conduction fields with high accuracy.

The method reduces computational costs compared to traditional simulations.

Optimization results achieve minimized heat transfer effectively.

Abstract

The layout optimization of the heat conduction is essential during design in engineering, especially for thermal sensible products. When the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods used usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model are trained by data-driven methods which requires intensive training samples that from numerical simulations and not really effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a Physics-driven Convolutional Neural Networks (PD-CNN) method to infer the physical field solutions for random varied loading cases. After that, the Particle Swarm Optimization (PSO) algorithm is used to optimize the sizes and the positions of the hole masks in the prescribed design domain, and the average temperature value of the entire heat conduction field is minimized, and the goal of minimizing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework not only predict field solutions that are highly consistent with conventional simulation results, but also generate the solution space with without any pre-obtained training data.