Optimization of Fan Blade Design Using CFD and Reinforcement Learning

Abstract

The design of fan blades has undergone significant advancements over the past century. However, it is important to consider whether conventional design approaches may unintentionally constrain the range of possible blade geometries. This paper investigates the potential of integrating Machine Learning and CFD to further improve and accelerate the fan blade design process. To achieve this, the study focuses on two main tasks: developing an accurate numerical model of a centrifugal fan in STAR-CCM+ and creating a Reinforcement Learning (RL) framework which implements the STAR-CCM+ model to optimize the fan blade geometry. CFD simulations were performed using the k − ω SST solver, and a mesh convergence study was performed. The RL framework for blade optimization was based on the Deep Q-Network (DQN) algorithm, implemented in Python using Pytorch. The CFD Model validation was carried out by comparing the performance curve obtained from STAR-CCM+ simulations with the manufacturer’s fan curve. The results indicate that the developed model accurately predicts the flow field generated by the fan. The static pressure rise across the fan serves as the primary performance metric for evaluating design improvements. The Reinforcement Learning (RL) approach successfully produced new and improved blade designs in each iteration. However, none of the generated designs outperformed the original fan blade. Despite this, the approach shows strong potential for improving blade design given more time and computational resources. For future studies, additional methods can be incorporated to better evaluate blade design. For instance, investigating other Reinforcement Learning methods or alter the current environment to reduce its design constraints.