Optimization of Fluid Dynamic Processes: Integration of Computational Fluid Dynamics with Machine Learning and Metaheuristics

Computational Fluid Dynamics (CFD) is an essential tool for the computational analysis of fluids and their interactions with solid surfaces. Despite offering advantages such as reduced manufacturing costs, flexibility, and technological integration, it faces challenges such as the need for experienced professionals and long simulation times. The integration with Machine Learning (ML) and meta-heuristics (MH) provides advanced solutions to address these challenges. ML, by understanding the problems, generates models for predicting results, while MH finds optimal output values with the best configuration of input parameters. This integration, observed in various studies, provides effective and reliable solutions. Aligning with the principles of Industry 4.0, it promotes digital transformation, automation, and customization in industrial processes. The proposed method, applied in a case study of an industrial centrifugal fan, presents efficient results, promoting an intelligent and adaptable approach to dealing with complex challenges in the industry.