CFD and Machine Learning based Simulation of Flow and Heat Transfer Characteristics of Micro Lattice Structures

In this paper, systematic CFD analysis using ANSYS Fluent was carried out to generate the dataset for developing the Machine Learning model, which predicts the average final temperature of water and the pressure drop from the set of input parameters considered for applications. There are six micro lattice structures, kagome, tetrahedral, pyramidal, hexagonal, windward bent and hexagonal-windward bent, modelled for this study using FUSION 360 by Autodesk. The study of heat transfer between liquid water and the micro lattice structures realized with the independent variables, initial fluid flow velocity, lattice temperature, and fluid temperature as well as lattice materials and its different structures. About 2146 output data of average final fluid temperature and the pressure drop were collected from the CFD simulations by varying input parameters. To predict the output parameter against the set of input parameters, Machine Learning model with regression based classification algorithm was adopted while training the ML model. The quality metric of the ML model was calculated using residual sum of squares method. The final average temperature of the fluid and pressure drop as predicted by the ML model is closer to simulated data.