Using of Computational Fluid Dynamics (CFD) and Machine Learning (ML) for predictive modelling in fluid dynamic

Abstract

Fluid dynamics predictive modeling for multiphase flow and heat exchanger optimization is undergoing a paradigm shift with the advent of deep learning techniques. The five different approaches under this framework are each concerned with the challenges of predictive modeling. At the heart of this is the ability for a hybrid integration of Computational Fluid Dynamics (CFD) and Machine Learning (ML) where both can coexist without interfering with each other allowing iterative refinement till convergence. When errors occur in the solution, the framework employs adaptive mesh refinement (AMR) to modify the mesh dynamically so that improved spatial accuracy and flexibilities are maintained for multiphase flow models. Moreover, the Machine learning bolstered Lattice Boltzmann Method (LBM), not only makes it superfast but also improves accuracy. An innovation aspect of the framework is its data-driven calibration and validation approach in which statistical parameters within each individual model are updated in real-time utilizing real-world data so that predictions remain tightly coupled with experimental results. To achieve a reasonable computation speed, parallelized algorithm is used in large simulations, which works by dividing the whole domain into independent pieces for parallel executions. A qualitative performance comparison is shown in tables and charts, demonstrating the advantage of the framework over others in accuracy, processing efficiency, scalability, flexibility stability and real-world applicability. The iterative and adaptive nature of the framework allows constant improvements to be made, which is ideal for modelling complex multiphase flows. This complete, state-of-the-art approach provides robust new tool for predictive modeling scientists and engineers alike and significant future cross-industry applicability will cement its status as an all-in-one CFDS solution.