Feature selection and processing of turbulence modeling based on an artificial neural network

Yin, Yajun; Yang, Pu; Zhang, Yufei; Chen, Haixin; Fu, Song

doi:10.1063/5.0022561

Cited by 71 publications

(16 citation statements)

References 40 publications

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“…It is found that when the players play the game, they can effectively improve their corresponding decision-making ability calculation and compression capacity [ 13 ]. Yin et al used the ANN to obtain specific patterns from high-fidelity data and then constructed a data-driven turbulence model with significantly improved prediction accuracy [ 14 ]. Using the ANN technology, Hong et al have constructed a general framework for induced sludge aliasing correction, which reduces the detection error under different aliasing conditions [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Hua

2021

Computational Intelligence and Neuroscience

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The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students’ basketball tactical basic cooperation ability.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Hua

2021

Computational Intelligence and Neuroscience

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“…The random drift particle swarm optimization (RDPSO) simulates the random thermal motion of particles by adding a temperature term to the PSO, which improves the global search capability of particles Sun, Wu, Palade, Fang, & Shi (2015) . In addition, some scholars have tried to use deep learning to improve the numerical solution of <u> Pawar et al (2019) ; Yin et al (2020) .…”

Section: Introductionmentioning

confidence: 99%

Multi-network collaborative lift-drag ratio prediction and airfoil optimization based on residual network and generative adversarial network

Zhao

Wu²,

Chen³

et al. 2022

Front. Bioeng. Biotechnol.

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As compared with the computational fluid dynamics(CFD), the airfoil optimization based on deep learning significantly reduces the computational cost. In the airfoil optimization based on deep learning, due to the uncertainty in the neural network, the optimization results deviate from the true value. In this work, a multi-network collaborative lift-to-drag ratio prediction model is constructed based on ResNet and penalty functions. Latin supersampling is used to select four angles of attack in the range of 2°–10° with significant uncertainty to limit the prediction error. Moreover, the random drift particle swarm optimization (RDPSO) algorithm is used to control the prediction error. The experimental results show that multi-network collaboration significantly reduces the error in the optimization results. As compared with the optimization based on a single network, the maximum error of multi-network coordination in single angle of attack optimization reduces by 16.0%. Consequently, this improves the reliability of airfoil optimization based on deep learning.

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“…For example, the ability of a deep convolutional neural network (CNN) to accurately classify images has been widely exploited in search engines, face recognition, and cancer diagnosis, among many other tasks [27,28,29,30]. Already in the field of fluid mechanics [31], ANNs have been utilized for various purposes [32,33,34,35,36,37,38,39], such as bubble pattern recognition [40], turbulence modeling [41,42,43], and classification of vortex wakes [44].…”

Section: Introductionmentioning

confidence: 99%

Image features of a splashing drop on a solid surface extracted using a feedforward neural network

Yee,

Yamanaka,

Tagawa

2022

Preprint

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This article reports nonintuitive characteristic of a splashing drop on a solid surface discovered through extracting image features using a feedforward neural network (FNN). Ethanol of area-equivalent radius about 1.29 mm was dropped from impact heights ranging from 4 to 60 cm (splashing threshold 20 cm) and impacted on a hydrophilic surface. The images captured when half of the drop impacted the surface were labeled according to their outcome, splashing or nonsplashing, and were used to train an FNN. A classification accuracy ≥ 96% was achieved. To extract the image features identified by the FNN for classification, the weight matrix of the trained FNN for identifying splashing drops was visualized. Remarkably, the visualization showed that the trained FNN identified the contour height of the main body of the impacting drop as an important characteristic differentiating between splashing and nonsplashing drops, which has not been reported in previous studies. This feature was found throughout the impact, even when one and threequarters of the drop impacted the surface. To confirm the importance of this image feature, the FNN was retrained to classify using only the main body without checking for the presence of ejected secondary droplets. The accuracy was still ≥ 82%, confirming that the contour height is an important feature distinguishing splashing from nonsplashing drops. Several aspects of drop impact are analyzed and discussed with the aim of identifying the possible mechanism underlying the difference in contour height between splashing and nonsplashing drops.

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Feature selection and processing of turbulence modeling based on an artificial neural network

Cited by 71 publications

References 40 publications

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Multi-network collaborative lift-drag ratio prediction and airfoil optimization based on residual network and generative adversarial network

Image features of a splashing drop on a solid surface extracted using a feedforward neural network

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