Prediction of turbulent heat transfer using convolutional neural networks

Kim, Junhyuk; Lee, Changhoon

doi:10.1017/jfm.2019.814

Cited by 136 publications

(77 citation statements)

References 82 publications

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“…Such non-physical results, which are present in many previous works (Fukami et al. 2019 a , b ; Kim & Lee 2020 b ; Liu et al. 2020; Scherl et al.…”

Section: Resultsmentioning

confidence: 59%

“…statistics yielded by bicubic interpolation and CNN were rather similar to those of low-resolution input data. Such non-physical results, which are present in many previous works (Fukami et al 2019a,b;Kim & Lee 2020b;Liu et al 2020;Scherl et al 2020), might be inevitable consequences of minimizing the pointwise error against the target data because the given information is insufficient to determine the solution uniquely and the target is only one of the possible solutions. On the other hand, GAN-based models focus on more sophisticated errors related to spatial correlation and significant features in the turbulence.…”

Section: Resultsmentioning

confidence: 93%

“…Finally, the analysis of trained network was difficult because of the large number of parameters. Kim & Lee (2020 b ), for example, identified that the gradient map of the trained model could be used to extract the physics implied in the training data. This progress, with respect to super-resolution reconstruction, might help identify a nonlinear relationship between large-scale structures and small-scale ones.…”

Section: Resultsmentioning

confidence: 99%

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Unsupervised deep learning for super-resolution reconstruction of turbulence

et al. 2021

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“…Such non-physical results, which are present in many previous works (Fukami et al. 2019 a , b ; Kim & Lee 2020 b ; Liu et al. 2020; Scherl et al.…”

Section: Resultsmentioning

confidence: 59%

Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

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Unsupervised deep learning for super-resolution reconstruction of turbulence

et al. 2021

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“…The new architecture was designed to capture the spatial-temporal features of unsteady flows. Similar studies are [120]- [122]. In summary, the comparison results of the above studies are shown in Table 7.…”

Section: ) Temporal Continuity Oriented Unsteady Aerodynamic Responsmentioning

confidence: 58%

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

Zhang

Xiang

et al. 2020

IEEE Access

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This paper reviews studies on neural networks in aerodynamic data modeling. In this paper, we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs). Subsequently, the history and fundamental methodologies of neural networks are introduced. Furthermore, we classify the neural networks based studies in aerodynamic data modeling and illustrate comparisons among them. These studies demonstrate that neural networks are effective approaches to aerodynamic data modeling. Finally, we identify three important trends for future studies in aerodynamic data modeling: a) the transformation method and physics informed models will be combined to solve high-dimensional partial differential equations; b) in the research area of steady aerodynamic response predictions, model-oriented studies and data-integration-oriented studies will become the future research directions, while in unsteady aerodynamic response predictions, radial basis function neural networks (RBFNNs) are the best tools for capturing the nonlinear characteristics of flow data, and convolutional neural networks (CNNs) are expected to replace long short-term memories (LSTMs) to capture the temporal characteristics of flow data; and c) in the field of steady or unsteady flow field reconstructions, the CNN-based conditional generative adversarial networks (cGANs) will be the best frameworks in which to discover the spatiotemporal distribution of flow field data. INDEX TERMS Aerodynamics, convolutional neural networks, neural networks, generative adversarial networks, recurrent neural networks.

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“…Güemes, Discetti & Ianiro (2019) applied an extended POD and convolutional neural networks, respectively, to reconstruct large- and very large-scale motions in a turbulent channel flow based on the wall shear stress measurement, and showed that the convolutional neural networks performed significantly better than the extended POD. Kim & Lee (2020) used a nine-layer convolutional neural network (CNN) to predict the heat flux at the wall using wall variables (, and ), and showed that the CNN outperformed a linear regression. So far, there is no attempt to apply a CNN to the prediction of the near-wall flow () from the flow variables at the wall and to the flow control in a feedback manner.…”

Section: Introductionmentioning

confidence: 99%