An Advanced Methodological Optimization for Multi-Objective Parametric Analysis in Computational Fluid Dynamics

Abstract

In computational fluid dynamics (CFD), the optimization of parametric models remains a significant challenge due to the multifaceted nature of fluid interactions. This study introduces a novel methodological framework that integrates machine learning algorithms with advanced numerical simulations to optimize multiple objectives simultaneously. Utilizing a comprehensive dataset derived from high-fidelity simulations, we applied Random Forest and Gradient Boosting techniques to enhance accuracy and efficiency in predicting fluid behaviors across various scenarios. Our findings demonstrate a reduction in computational time by up to 35% while maintaining an accuracy rate of 97% in predictive modeling, outperforming conventional CFD optimization methods. This research highlights the importance of integrating machine learning with computational techniques, providing new insights into CFD applications in engineering and environmental science.