A Review on Optimal Design of Fluid Machinery Using Machine Learning Techniques

Abstract

The design of fluid machinery is a complex task that requires careful consideration of various factors that are interdependent. The correlation between performance parameters and geometric parameters is highly intricate and sensitive, displaying strong nonlinear characteristics. Machine learning techniques have proven to be effective in assisting with optimal fluid machinery design. However, there is a scarcity of literature on this subject. This study aims to present a state-of-the-art review on the optimal design of fluid machinery using machine learning techniques. Machine learning applications primarily involve constructing surrogate models or reduced-order models to explore the correlation between design variables or the relationship between design variables and performance. This paper provides a comprehensive summary of the research status of fluid machinery optimization design, machine learning methods, and the current application of machine learning in fluid machinery optimization design. Additionally, it offers insights into future research directions and recommendations for machine learning techniques in optimal fluid machinery design.