Flow Field Numerical Simulation of Artificial Submerged Cavitating Water Jet Based on Fluent

Lei, C; Deng, Shiyi; Guan, Jinfa; Zhang, Shi-xia; Guo, Guang-dong; Zhang, Tengfei; Guo, L.H.

doi:10.1142/9789813146426_0129

Cited by 2 publications

(6 citation statements)

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“…(2) APE max is the maximum absolute percentage error, which reflects the deviation of the worst point by Equation (15). APE reflects the deviation of each predicted point from the experimental value by Equation (16).…”

Section: Performance Evaluationmentioning

confidence: 99%

Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

Chen

Zhang

et al. 2020

Applied Sciences

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In order to accurately predict the erosion effect of underwater cleaning with an angle nozzle under different working conditions, this paper uses refractory bricks to simulate marine fouling as the erosion target, and studies the optimized erosion prediction model by erosion test based on the submerged low-pressure water jet. The erosion test is conducted by orthogonal experimental design, and experimental data are used for the prediction model. By combining with statistical range and variance analysis methods, the jet pressure, impact time and jet angle are determined as three inputs of the prediction model, and erosion depth is the output index of the prediction model. A virtual data generation method is used to increase the amount of input data for the prediction model. This paper also proposes a Mind-evolved Advanced Genetic Algorithm (MAGA), which has a reliable optimization effect in the verification of four stand test functions. Then, the improved back-propagating (BP) neural network prediction models are established by respectively using Genetic Algorithm (GA) and MAGA optimization algorithms to optimize the initial thresholds and weights of the BP neural network. Compared to the prediction results of the BP and GA-BP models, the R2 of the MAGA-BP model is the highest, reaching 0.9954; the total error is reduced by 47.31% and 35.01%; the root mean square error decreases by 51.05% and 31.80%; and the maximum absolute percentage error decreases by 65.79% and 64.01%, respectively. The average prediction accuracy of the MAGA-BP model is controlled within 3%, which has been significantly improved. The results show that the prediction accuracy of the MAGA-BP prediction model is higher and more reliable, and the MAGA algorithm has a good optimization effect. This optimized erosion prediction method is feasible.

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Section: Performance Evaluationmentioning

confidence: 99%

Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

Chen

Zhang

et al. 2020

Applied Sciences

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“…The submerged water jet is a turbulent flow in the vaporliquid two-phase flow field (Carlomagno & Ianiro, 2014;Hutli et al, 2016aHutli et al, , 2016bLei et al, 2015;Lin, Zhang, Qin, & Dang, 2015;Liu et al, 2016). Multiphase model, turbulence model and cavitation model should be considered for the water jet cavitating nozzle numerical simulation.…”

Section: Governing Equationsmentioning

confidence: 99%

“…The Euler model has a long calculation time and its solution is easy to diverge, while the mixture model is suitable for studying the wide distribution of dispersed phases and is often used to study the location and intensity of cavitation. In general, the mixture model is selected to deal with similar issues by Celik, Arikan Ozden, and Bal (2014), Chen, Li, Gong, Chen, and Lu (2019), Ji, Luo, Wu, Zhang, and Xu (2010), Lei et al (2015), Li, Zheng, Hong, and Ni (2017), Yang and Lei (2014), Zhao, Wei, Zhang, Dong, and Jin (2009), and Zhou and Wang (2008), and hence, the mixture model is selected as the multiphase model in this study.…”

Section: Governing Equationsmentioning

confidence: 99%

“…It can be seen that the result of S-S model has a smaller error with the Yao's results. Moreover, the S-S model has more advantages in the calculation of the pressure coefficient, and the calculation is more stable and easy to converge (Lei et al, 2015). Therefore, the Schnerr-Sauer cavitation model is used as a cavitation model in this study.…”

Section: Cavitation Modelmentioning

confidence: 99%

“…By comparing the simulated cavitation results with the experimental erosion patterns, the axisymmetric annular distribution of the bubbles corresponds to the annular erosion region formed on the target surface. Lei, Deng, Guan, Chen, and Zhang (2015) studied the cavitation process and flow field characteristics of a submerged water jet with the vapor volume fraction as the evaluation index of cavitation intensity. This also indicates that the vapor volume fraction is reliable as an indicator.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structure optimization of submerged water jet cavitating nozzle with a hybrid algorithm

Chen

Zhang

2019

Engineering Applications of Computational Fluid Mechanics

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It is critical to obtain a suitable geometry for a cavitating nozzle of submerged water jet, since a suitable geometry determines its cavitation intensity and cavitation distribution. This paper puts forward a water jet cavitating nozzle optimization platform aiming to improve its axial maximum vapor volume fraction. Considering the influence of all eight geometric parameters of the nozzle itself, a hybrid optimization algorithm is developed to optimize the nozzle by combining with the CFD technique. After the optimization, the optimal geometry results to a 9.41% increment in the axial maximum vapor volume fraction at 50 m underwater depth, and improves the cavitation effect obviously especially in deeper water. From the results, it is found that the cylindrical section diameter, contraction angle and diffusion angle have a great influence on the axial maximum vapor volume fraction, while the inlet diameter have the least effect on the same. The optimization presented in this study can lay the foundation for further study on water jet cavitating nozzle.

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Flow Field Numerical Simulation of Artificial Submerged Cavitating Water Jet Based on Fluent

Cited by 2 publications

References 0 publications

Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

Structure optimization of submerged water jet cavitating nozzle with a hybrid algorithm

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