论文标题
使用物理驱动的卷积神经网络进行热传导板布局优化
Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks
论文作者
论文摘要
在工程设计过程中，尤其是对于热敏感产品，热传导的布局优化是必不可少的。当优化算法迭代评估不同的加载案例时，使用的传统数值模拟方法通常会导致实质性的计算成本。为了有效地减少计算工作，使用数据驱动的方法来训练替代模型，作为规定的外部负载和各种几何形状之间的映射。但是，现有模型是通过数据驱动方法培训的，该方法需要从数值模拟中进行的密集培训样本，但并不能真正有效地解决该问题。本文选择稳定的热传导问题作为示例，提出了一个物理驱动的卷积神经网络（PD-CNN）方法，以推断物理场解决方案以进行随机多种负载病例。之后，使用粒子群优化（PSO）算法来优化规定的设计域中孔口罩的尺寸和位置，并最大程度地降低了整个热传导场的平均温度值，并实现了最小化热传递的目标。与现有的数据驱动方法相比，提出的PD-CNN优化框架不仅可以预测与常规仿真结果高度一致的现场解决方案，而且还可以在没有任何预先预测的培训数据的情况下生成解决方案空间。
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.
