Multiple parameters and multiple conditions optimization based on two steps strategy for an axial flow pump

Han, Bingfu; Tan, Lei; Lu, Yangping; Dai, Zhenxing

doi:10.1016/j.oceaneng.2023.114732

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…The mechanism of the grooved flow control technology to improve the energy performance of axial flow pumps was also revealed. Han et al [14] proposed a two-step strategy for multi-operating condition optimization. In this optimization strategy, the first step is to optimize pump performance under the design condition, and the second step is to optimize pump performance under various other operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Hydraulic Efficiency and Internal Flow Characteristics of a Multi-Stage Pump Using RBF Neural Network

Zhang,

Wang,

Yang

et al. 2024

Water

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In order to improve the hydraulic efficiency and internal flow pattern of a multi-stage pump under multiple flow conditions, an intelligent optimization design was proposed for its hydraulic components. Sensitivity analysis was used to select the key parameters influencing the hydraulic efficiency of a multi-stage pump. The optimal Latin hypercube sampling and non-dominated sorting genetic algorithm Ⅱ were employed to build a multi-objective optimization system. Moreover, a radial basis function neural network was adopted as the surrogate model of hydraulic efficiency. The research results showed that the impeller outlet width, impeller blade wrap angle, impeller outlet blade angle, and diffuser inlet width were the key factors affecting the hydraulic efficiency. The efficiency of the optimized model increased by 4.35% under the design condition and the matching of the internal flow between the optimized impeller and diffuser was significantly enhanced under the nominal condition. The improved flow pattern could be clearly observed in the flow passage of both the pump impeller and the diffuser. After optimization, the wear performance of the model was also improved compared to the original design. The wear area decreased in size and was distributed more evenly, resulting in a noticeable decrease in the maximum amount of wear.

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Section: Introductionmentioning

confidence: 99%

Optimization of Hydraulic Efficiency and Internal Flow Characteristics of a Multi-Stage Pump Using RBF Neural Network

Zhang,

Wang,

Yang

et al. 2024

Water

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“…1 The forms of hydraulic turbines include reverse pump type, pump type, and water turbine type, [2][3][4] among which the commonly used one is reverse pump type with a simple structure. [5][6][7] Given the advantages of pump reversal as a hydraulic turbine, many scholars have conducted relevant research on it. Carravetta 8 and Mohammad 9 conducted a study on the use of reverse pumps for residual pressure recovery in water supply networks.…”

Section: Introductionmentioning

confidence: 99%

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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Experiment on cavitation-vibration correlation of a centrifugal pump under steady state and start-up conditions in energy storage station

Lu,

Tan,

Zhao

et al. 2024

Journal of Energy Storage

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Multiple parameters and multiple conditions optimization based on two steps strategy for an axial flow pump

Cited by 7 publications

References 33 publications

Optimization of Hydraulic Efficiency and Internal Flow Characteristics of a Multi-Stage Pump Using RBF Neural Network

Optimization of Hydraulic Efficiency and Internal Flow Characteristics of a Multi-Stage Pump Using RBF Neural Network

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Experiment on cavitation-vibration correlation of a centrifugal pump under steady state and start-up conditions in energy storage station

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