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First, a highly parameterized geometric design theory is constructed using six featured variables to achieve a complete three-dimensional modeling of the blade geometry. Four hundred preliminary cases are studied using the computational fluid dynamics method with various combinations of these featured variables to obtain a preliminary solution. The best preliminary design has an efficiency of 83.33%, and a head of 5.495 m. To further improve this performance, this paper also presents a high-precision prediction model for the energy performance of axial flow pump based on back-propagation neural network and the encoding layers of random sampling and local feature aggregator network created. Afterwards, a multi-population genetic algorithm is used to quickly find the optimal solution within the prediction mode range. The algorithm achieved a highest efficiency of 86.373% and was validated by numerical simulation with a value of 86.057% and a prediction error of 0.316%. Compared with the preliminary solution, the efficiency of the optimized axial flow pump is increased by 1.615%, with a wider high-efficiency range and an optimal operating point closer to the design conditions. Overall, this intelligent optimization method has the potential to significantly reduce the design time of axial pumps and increase their performance.<\/jats:p>","DOI":"10.1093\/jcde\/qwae013","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T13:25:35Z","timestamp":1708003535000},"page":"325-342","source":"Crossref","is-referenced-by-count":6,"title":["Intelligent optimization of axial-flow pump using physics-considering machine learning"],"prefix":"10.1093","volume":"11","author":[{"given":"Kan","family":"Kan","sequence":"first","affiliation":[{"name":"College of Energy and Electrical Engineering, Hohai University , Nanjing 211100 , PR China"},{"name":"College of Water Conservancy and Hydropower Engineering, Hohai University , Nanjing 210098 , PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Energy and Electrical Engineering, Hohai University , 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