Data-driven prediction of the performance of enhanced surfaces from an extensive CFD-generated parametric search space

Abstract

Machine learning has rapidly been adopted in virtually all areas of engineering in recent years. This paper develops a machine learning model capable of predicting the performance of parametrically generated enhanced microsurface geometries for cooling electronic and power systems. Designing this type of geometry usually involves expensive computational fluid dynamics (CFD) simulations, limiting the number of candidate geometries that may be tested. For this reason, when searching for new geometries for a given application, designs are usually restricted to a simplified subset of basic shapes to reduce the complexity and dimension of the search space. In an effort to add geometrical diversity and explore singular morphologies, we have developed an algorithm capable of characterizing almost any geometry, based on an extensive CFD database with more than 15800 geometries obtained from a Monte Carlo sampling of the space of possible geometries. With this framework, it is possible to estimate various quantities of interest, such as the heat flux in the enhanced zone and total drag, with relative errors below 10% and 2%, respectively. Thus, we establish the utility of machine learning to develop surrogate models for the rapid performance prediction of novel enhanced microsurfaces.