Enhancing Computational Fluid Dynamics with Machine Learning Integration
Keywords:
Computational Fluid Dynamics, Machine Learning, Turbulent Flows, Neural Networks, Data-Driven ModelsAbstract
This study explores the integration of machine learning algorithms into computational fluid dynamics (CFD) simulations to enhance accuracy and efficiency. By leveraging neural networks and data-driven approaches, the research demonstrates improved prediction capabilities of turbulent flows and complex fluid interactions. The findings suggest significant potential for reducing computational costs while maintaining high fidelity in simulations, paving the way for more robust engineering designs.
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