人工智能驱动的离心泵水力优化研究进展OA

Driven by China's dual-carbon strategy and the ongoing transformation toward high-end manufacturing,improving the energy efficiency and operational reliability of centrifugal pumps has be-come a critical task in the field of fluid machinery.Within the complete technical loop of intelligent de-sign,intelligent manufacturing,as well as intelligent operation and maintenance of centrifugal pumps,intelligent design occupies the foremost position,as its optimization quality directly determines the per-formance ceiling and energy-saving potential of the subsequent manufacturing and maintenance stages.However,constrained by high-dimensional design spaces,strong nonlinear coupling,and conflicting multi-objective requirements,conventional optimization methods can no longer meet current enginee-ring demands in terms of modeling accuracy,solution efficiency,and global search capability.In recent years,the rapid advancement of artificial intelligence has provided a new research paradigm for overcoming these bottlenecks.This paper systematically reviews the research progress on AI-driven hy-draulic optimization of centrifugal pumps over the past decade.Centered on the two core challenges of efficient and accurate representation of functional relationships and global solution of high-dimensional multi-objective problems,the review is organized along two dimensions,namely machine learning-driven and computational intelligence-driven approaches.The solution mechanisms and applicability boundaries of machine learning methods such as artificial neural networks,support vector regression,and Gaussian process regression,as well as intelligent algorithms including genetic algorithms,particle swarm optimization,and differential evolution,are elaborated in the context of centrifugal pump hy-draulic optimization.Finally,through a quantitative comparative analysis of existing studies,significant differences between the two categories of methods in terms of applicable problem scale and computa-tional cost are revealed.Future research directions are further discussed from three aspects:improving model accuracy,reducing sample validation costs,and enhancing the performance of intelligent algo-rithms,with the aim of providing theoretical references and technical support for the intelligent design of efficient and highly reliable centrifugal pumps.