2019
DOI: 10.1017/jfm.2019.814
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Prediction of turbulent heat transfer using convolutional neural networks

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Cited by 128 publications
(63 citation statements)
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References 82 publications
(132 reference statements)
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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%
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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%
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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%
“…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%