Determination of the length of hydraulic jumps using artificial neural networks

Naseri, Mahdi; Othman, Faridah

doi:10.1016/j.advengsoft.2012.01.003

Cited by 30 publications

(7 citation statements)

References 12 publications

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“…Barthelemy [22] reviewed the application of approximate models in terms of structural optimization. Approximate models have been applied in multi-objective optimization, such as the response surface method [23,24], artificial neural networks [25,26] and radial basis function [27,28]. Zhang [29] put forward a centrifugal pump optimization method based on the Kriging model, the optimization result of which was in good agreement with the experimental data.…”

Section: Introductionmentioning

confidence: 88%

Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Wang

Zhang

et al. 2021

JMSE

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In this study, an annular jet pump optimization method is proposed based on an RBF(Radial Basis Function) neural network model and NSGA-II(Non-Dominated Sorting Genetic Algorithm) optimization algorithm to improve the hydraulic performance of the annular jet pump applied in submarine trenching and dredging. Suction angle, diffusion angle, area ratio and flow ratio were selected as design variables. The computational fluid dynamics (CFD) model was used for numerical simulation to obtain the corresponding performance, and an accurate RBF neural network approximate model was established. Finally, the NSGA-II algorithm was selected to carry out multi-objective optimization and obtain the optimal design variable combination. The results show that the determination coefficient R2 of the two objective functions (jet pump efficiency and head ratio) of the approximate model of the RBF neural network were greater than 0.97. Compared with the original model, the optimized model's suction angle increased, and the diffusion angle, flow ratio and area ratio decreased. In terms of performance, the head ratio increased by 30.46% after the optimization of the jet pump, and efficiency increased slightly. The proposed jet pump performance optimization method provides a reference for improving the performance of other pumps.

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Section: Introductionmentioning

confidence: 88%

Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Wang

Zhang

et al. 2021

JMSE

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“…Some of the well-known methods in this area are auto-regression, Markov chain, or robust optimization techniques [7][8][9]. Among the empirical methods, machine learning has been widely used to solve real world problems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Artificial neural networks (ANNs) are well-known machine learning systems that have been utilized to predict the solar radiation [2][3][4][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Using measured daily meteorological parameters to predict daily solar radiation

et al. 2015

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“…Recently, there has been a great increase in the application of metamodels instead of the complex analytics models that are limited by assumptions [33][34]. Several metamodeling techniques with various degrees of complexity have been extensively applied, such as the response surface methodology [35][36][37][38], artificial neural network [39][40][41][42], radial basis function [43][44][45], and kriging [46][47][48][49]. Some of these techniques are suitable for global approximations, i.e., can be used for representing the complete design space, while others are more suitable for local approximations of a part of the design space.…”

Section: Introductionmentioning

confidence: 99%

“…The existing studies to predict springback in the air bending process mostly use training data from experiments with real-life systems. Consequently, these studies considered only inadequate materials and tool geometry [37,[39][40][41]50].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Springback in the Air Bending Process Using a Kriging Metamodel

Khadra

El-Morsy

2016

Eng. Technol. Appl. Sci. Res.

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This paper addresses the use of the kriging‏ approach to predict the springback in the air bending process. The materials and the geometrical parameters, which significantly affect the springback, were considered as inputs, and the springback angle was considered as the response. A verified nonlinear finite element model was used to generate the training data required to create the kriging‏ metamodel. The training examples were selected based on computer-generated D-optimal designs. A comparison between the kriging approaches and the response surface methodology is conducted and discussed. The results showed that kriging accurately predicts the finite element springback results. Comparing the accuracy of kriging with a response surface methodology shows that kriging with a 2nd degree polynomial and exponential correlation function predicts the springback more accurately than the response surface methodology.

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Determination of the length of hydraulic jumps using artificial neural networks

Cited by 30 publications

References 12 publications

Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Using measured daily meteorological parameters to predict daily solar radiation

Prediction of Springback in the Air Bending Process Using a Kriging Metamodel

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