Computational Fluid Dynamics And Machine Learning For Predictive Analysis In Turbomachinery

Abstract

This study explores the integration of Computational Fluid Dynamics (CFD) and Machine Learning (ML) for predictive analysis in turbomachinery, focusing on the optimization of efficiency, power output, and structural integrity. CFD simulations are employed to model the fluid flow and thermal gradients within turbomachinery components, while ML techniques are used to analyze these simulations and develop predictive models for performance optimization. The results demonstrate that coupling CFD with ML allows for the identification of design modifications that optimize efficiency and power output under varying operational conditions. Surface plots generated from CFD simulations provide detailed insights into temperature and pressure gradients, revealing their impact on system performance. Scatter plots further highlight key correlations, such as the relationship between efficiency and temperature and power output and pressure, which are essential for fine-tuning operational parameters. Machine learning algorithms, such as regression models and neural networks, are trained on CFD data to predict performance trends, enabling faster, more accurate decision-making. The integration of these techniques not only enhances the operational efficiency of turbomachinery systems but also contributes to the development of real-time monitoring systems for predictive maintenance. This research lays the groundwork for future optimization algorithms and broader applications of CFD and ML in the design and operation of energy systems, offering significant potential to improve system reliability, reduce maintenance costs, and extend equipment lifespan.