Fluid Dynamics in Turbomachinery Optimization Techniques and Performance Analysis
Keywords:
Turbomachinery, Fluid Dynamics, Computational Fluid Dynamics (CFD), Optimization Techniques, Performance Analysis, Genetic Algorithms, Machine Learning, Axial Flow MachinesAbstract
Turbomachinery plays a crucial role in various industrial applications, including power generation, aviation, and manufacturing. Understanding and optimizing fluid dynamics within these machines is essential for enhancing performance, efficiency, and reliability. This study focuses on the fluid dynamics of turbomachinery, exploring advanced optimization techniques and conducting comprehensive performance analysis. By integrating computational fluid dynamics (CFD) simulations, genetic algorithms, and machine learning, this research aims to develop innovative solutions for improving turbomachinery efficiency. The findings are expected to provide valuable insights for engineers and designers, contributing to advancements in turbomachinery technology and applications.
The research encompasses a detailed examination of different types of turbomachinery, including axial, radial, and mixed flow machines. It also addresses the challenges associated with turbulence modeling, mesh generation, and solver validation. Through a combination of theoretical analysis, computational methods, and experimental validation, this study seeks to identify optimal design parameters and performance indicators. The ultimate goal is to enhance the operational efficiency and lifespan of turbomachinery, thereby reducing energy consumption and operational costs in various industries.
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