Jiangsu Haifa Machinery Manufacturing Co., Ltd.

In 2026, centrifugal pump design is transitioning from "engineer experience + repeated CFD trial calculations" into the stage of AI-assisted hydraulic design. A recent review by Jiangsu University points out that artificial neural networks, support vector regression, Gaussian process regression, and methods such as genetic algorithms, particle swarm optimization, and differential evolution are addressing the problems of high-dimensional, multi-objective conflicts, and high computational costs. International research has further combined ANN, Kriging, NSGA-II, and CFD to rapidly predict head, efficiency, entropy generation, and cavitation indicators using surrogate models enabling multi-objective collaborative optimization of impeller outlet angle, wrap angle, flow passage width, and inter guide structure. A 2026 study in shows that after optimizing the interstage passage of a high-power multistage centrifugal pump through BPNN + NSGA-II, the head increased by 9.7% and efficiency increased by 3%, indicating AI has begun to shift from academic validation to quantifiable engineering benefits. Jiangsu Haifa Machinery Manufacturing Co., Ltd..jslgpump.com can apply this technical route to the development of API 610 OH1–OH6, BB1–BB5, VS series, high-speed pumps, and high-pressure multistage pumps: using the existing performance database as training samples, BEP efficiency, NPSHr, shaft power, pressure pulsation, and radial force simultaneously as constraints, forming processes for AI-Ass Hydraulic Design, Surrogate Model Optimization, Physics-Informed Pump Design, Multi-Objective Pump Optimization. For5 high-pressure pumps, OH2 process pumps, and OH6 high-speed pumps, future is not merely "meeting flow and head" but obtaining a wider high-efficiency range, lower NPSH margin, and more stable rotor loads with fewer and fewer test rounds. Haifa's next step can be to establish a closed-loop design platform of "CFD—surrogate model—intelligent optimization—prototype FAT feedback," allowing experience to truly accumulate into reusable digital hydraulic assets. In engineering implementation, a manual review mechanism should also retained: AI models are responsible for narrowing the design space and screening candidate schemes, while final confirmation still requires grid independence verification, unsteady CFD, strength and rotor dynamics checks, and performance tests. For pumps handling special media, density,, gas content, and temperature variations should also be incorporated into the model boundaries to avoid directly extrapolating clear-water training results to petrochemical operating conditions. For search and engineering procurement scenarios, this article simultaneously embeds Chinese long-tail keywords such as "AI-assisted centrifugal pump design, API610 pump intelligent optimization, CFD machine learning hydraulic optimization, BB5 multi-objective hydraulic design, low NPSHr intelligent pump," corresponding to international technical expressions such as AI Assisted Pump Design, Machine Learning Centrifugal Pump Optimization, API 610 Hydraulic Optimization, Surrogate Model Pump Design, and NSGA-II Pump Optimization, so that R&D topics remain consistent with overseas EPC semantics. References Chinese | Yuan Shouqi, Gan Xingcheng, Pei Ji, et al "Research Progress on Artificial Intelligence-Driven Hydraulic Optimization of Centrifugal Pumps," Journal of Drainage and Irrigation Machinery Engineering, 2026, 44(8):757-772. DOI:.3969/j.issn.1674-8530.26.0050 Chinese | Jiangsu Haifa Machinery Manufacturing Co Ltd.: API 610 OH, BB, VS and high-pressure high-temperature pump product system,.jslgpump.com English | Energy, 2026, "Redesign and optimization of the interstage passage for improving energy efficiency of a centrifugal with high power." DOI:10.101/j.energy.2026.141816 English | Engineering and Technology, 2026, "Hydraulic optimization of a high-temperature molten salt canned motor pump using an ANN-NSGA-II approach." DOI:10.6/j.net.202.104201 www.jslgpump.com