论文标题
热卷积替代和热设计的自由度
Deep convolutional surrogates and degrees of freedom in thermal design
论文作者
论文摘要
我们提出了使用复合曲线曲线产生的复杂鳍几何形状的传热和压降预测的替代模型。热设计过程包括复杂，计算昂贵且耗时的迭代高保真模拟。随着机器学习算法的进步以及图形处理单元（GPU），我们可以利用GPU的并行处理体系结构，而不仅仅是仅依靠CPU来加速热流体模拟。在这项研究中，卷积神经网络（CNN）用于直接从保存为图像的拓扑中预测计算流体动力学（CFD）的结果。研究了带有单个鳍和多个形态鳍的表壳。为案例提供了单个FIN设计的Xpection网络和常规CNN的比较。结果表明，对于单鳍设计，特别是使用Xception网络，观察到高精度的预测精度。增加设计自由到多个鳍片会增加预测的误差。但是，对于设计目的而言，这种错误仍在压降和传热估计中保持在三％之内。
We present surrogate models for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bezier curves. Thermal design process includes iterative high fidelity simulation which is complex, computationally expensive, and time-consuming. With the advancement in machine learning algorithms as well as Graphics Processing Units (GPUs), we can utilize the parallel processing architecture of GPUs rather than solely relying on CPUs to accelerate the thermo-fluid simulation. In this study, Convolutional Neural Networks (CNNs) are used to predict results of Computational Fluid Dynamics (CFD) directly from topologies saved as images. The case with a single fin as well as multiple morphable fins are studied. A comparison of Xception network and regular CNN is presented for the case with a single fin design. Results show that high accuracy in prediction is observed for single fin design particularly using Xception network. Increasing design freedom to multiple fins increases the error in prediction. This error, however, remains within three percent for pressure drop and heat transfer estimation which is valuable for design purpose.
