Innovative Computational Fluid Dynamics Techniques for Enhanced Predictive Modeling in Multiphase Flow and Heat Exchanger Optimization

An innovative technique to combine “Innovative Computational Fluid Dynamics Techniques for Enhanced Predictive Modeling in Multiphase Flow and Heat Exchanger Optimization.” Five approaches tackle distinct predictive modeling problems in the framework. The hybrid CFD and Machine Learning Framework integrates CFD and ML without affecting each other. This enables the framework to be refined repeatedly until convergence. As solution mistakes occur, adaptive mesh refinement (AMR) adjusts the mesh. This improves multiphase flow models’ spatial accuracy and flexibility. The Lattice Boltzmann Method (LBM) with Machine Learning Enhancement is quicker and more accurate. The data-driven calibration and validation technique is unique in that it updates model parameters using real-world data, allowing estimations to match testing findings. The Parallelized Algorithm for Large-Scale Simulations maximizes computer performance by partitioning the region into parallel jobs. The performance evaluation in tables and charts reveals that the recommended framework is better in accuracy, processing efficiency, scalability, flexibility, stability, and applicability. Since the approaches are iterative and adaptive, they can improve. This makes the system suitable for modeling complicated multiphase flows. This comprehensive and cutting-edge technique improves multiphase flows and heat exchanges, providing scientists and engineers with new prediction tools. Future testing and implementation in other industries might establish the framework as a versatile and dependable computational fluid dynamics solution.