On developing data-driven turbulence model for DG solution of RANS

Sun, Liang; An, Wei; Liu, Xuejun; Lyu, Hongqiang

doi:10.1016/j.cja.2019.04.004

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…An artificial neural network (ANN) turbulence model was developed using data generated using the Spalart-Allmaras (SA) turbulence model by Liang et al In complex turbulence models, data-driven turbulence models based on artificial neural networks have the potential to improve the convergence efficiency of RANS. However, it should be noted that the developed ANN model was trained using data generated by the SA turbulence model, which may prevent it from being generalizable to other turbulence models or flow conditions [51].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Zhang

Jiang

2023

JMSE

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Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Zhang

Jiang

2023

JMSE

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“…This increased proficiency has allowed CFD to play a crucial role in both theoretical research and engineering applications. At present, numerical simulation methods mainly include direct numerical simulations (DNS), 1 large Eddy simulation (LES), 2 and RANS simulation, but due to the complexity and expensive computational cost of DNS and LES meshes, it is difficult to apply in engineering 3 . In engineering applications such as aerodynamic design and optimization, the average flow rate is the focus of attention.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification and identification of SST turbulence model parameters based on Bayesian optimization algorithm in supersonic flow

Yang,

Guo,

Zhang

et al. 2023

Numerical Methods in Fluids

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The Reynolds‐Averaged Navier–Stokes (RANS) model is the main model in engineering applications today. However, the normal value of the closure coefficient of the RANS turbulence model is determined based on some simple basic flows and may no longer be applicable for complex flows. In this paper, the closure coefficient of shear stress transport (SST) turbulence model is recalibrated by combining Bayesian method and particle swarm optimization algorithm, so as to improve the numerical simulation accuracy of wall pressure in supersonic flow. First, the obtained prior samples were numerically calculated, and the Sobol index of the closure coefficient was calculated by sensitivity analysis method to characterize the sensitivity of the wall pressure to the model parameters. Second, combined with the uncertainty of propagation parameters by non‐intrusive polynomial chaos (NIPC). Finally, Bayesian optimization is used to quantify the uncertainty and obtain the maximum likelihood function estimation and optimal parameters. The results show that the maximum relative error of wall pressure predicted by the SST turbulence model decreases from 29.71% to 9.00%, and the average relative error decreases from 9.86% to 3.67% through the parameter calibration of Bayesian optimization method. In addition, the system evaluated the calibration effect of three criteria, and the calibration results parameters under the three criteria were all better than the calculated results of the nominal values. Meanwhile, the velocity profile and density profile of the flow field were also analyzed. Finally, the same calibration method was applied to the supersonic hollow cylinder and BSL (Baseline) turbulence model, and the same calibration results were obtained, which verified the universality of the calibration method.

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“…In the current development trend, convolutional neural network is developing, and will generate convolutional neural network for various application scenarios, such as 3D convolutional neural network for video understanding. It is worth noting that the convolutional neural network is not only applicable to image related networks, but also includes image like networks, such as the chessboard analysis (Gutta et al, 2019;Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Convolution Neural Network in Financial Forecasting

Dai

2021

Journal of Organizational and End User Computing

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Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.

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On developing data-driven turbulence model for DG solution of RANS

Cited by 16 publications

References 22 publications

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Uncertainty quantification and identification of SST turbulence model parameters based on Bayesian optimization algorithm in supersonic flow

Application of Improved Convolution Neural Network in Financial Forecasting

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