Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus

Kumar, Abhishek; Ridha, Syahrir; Ilyas, Suhaib Umer; Dzulkarnain, Iskandar; Pratama, Agus Darma Yoga

doi:10.1007/s00521-022-07092-w

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…For example, Bose and Roy (2022) presented two deep learning-based strategies to predict sub-grid scale stress components for large-eddy simulations according to the scalar coefficients corresponding to the five symmetric integrity basis tensors. As one of deep learning techniques, convolutional neural networks (CNNs) have been recognized as an efficient and powerful tool for data-driven fluid dynamics modeling (Peng et al 2020a, 2020b, 2021a, Duru et al 2021, Kumar et al 2022 and analysis of complex fluid flow (Zhu and Zabaras 2018, Long et al 2019, Peng et al 2021b. The most prominent advantage of CNN is that it is good at distinguishing the geometry or the spatial feature to reveal the underlying laws of the given physical problem whereas the traditional methods usually have to exploit proper parameterization of the geometry.…”

Section: Introductionmentioning

confidence: 99%

Computationally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network

Peng

Zhi-ming³

et al. 2023

Fluid Dyn. Res.

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This work proposes an innovative approach for supersonic flow field modeling around airfoils based on sparse convolutional neural networks (SCNN) and Bézier generative adversarial network (GAN), where 1) the SCNN model is built to end-to-end predict supersonic compressible physical flow fields around airfoils from spatially-sparse geometries and 2) the trained Bézier-GAN is utilized to generate plenty of smooth airfoils as well as the latent codes representing airfoils. The spatially-sparse positions of airfoil geometry are represented using Signed Distance Function (SDF). Particularly, the latent codes are merged with the SDF matrix and the Mach number to form the input of the SCNN model, effectively making the SCNN model possess more robust geometric adaptability to different flow conditions. The most significant contribution compared to the regular convolutional neural network is that SCNN introduces sparse convolutional operations to process spatially-sparse input matrix, specifically, which only focuses on the local area with flow information when performing convolution, eventually saving memory usage and improving the network’s attention on the flow area. Further, the testing results show that the SCNN model can more accurately predict supersonic flow fields with a mean absolute error lower than 5% and save 40% of GPU (Graphics Processing Unit) memory. These results indicate that the proposed SCNN model can capture the shock wave features of supersonic flow fields and improve learning efficiency and computing efficiency.

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Section: Introductionmentioning

confidence: 99%

Computationally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network

Peng

Zhi-ming³

et al. 2023

Fluid Dyn. Res.

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Flow pattern identification of gas-liquid two-phase flow based on integrating mechanism analysis and data mining

Zhang

Hou

Zhu

et al. 2023

Geoenergy Science and Engineering

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Prediction of internal and external flow with sparse convolution neural network: A computationally effective reduced-order model

et al. 2023

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This paper presents a novel reduced-order model for internal and external flow field estimations based on a sparse convolution neural network. Since traditional convolution neural network requires “rectangular” matrixes as input, the convolutional operation is computationally inefficient when applied to these problems with input matrix having sparse information. In our approach, we innovatively introduce a sparse convolution neural network (SCNN) to collect spatial information on geometries that are inherently sparse, e.g., the flow in thin pipelines in a much larger domain or the pipelines with random branches. Different from the traditional convolution neural network (CNN) model, the SCNN only collects features from areas with flow information for both the input matrix and each convolutional layer, which not only reduces the consumption of computational resources but also significantly increases network attention to flow area. The model learns the mapping relationship between geometries and the physical field of fluid flow, and the spatial positions of geometry are represented using the nearest wall signed distance function. The proposed SCNN model has the promising adaptability to arbitrary geometry and less computational resource cost compared to the traditional CNN model: the mean error of the SCNN is less than 5.4% (while the CNN is 7.1%) for the internal flow and less than 6.5% (while the CNN is 8.1%) for the external flow. Moreover, the SCNN has 72% less GPU resource usage and 52% less random access memory cost than the CNN for internal flow. For the first time, our framework introduces the sparse convolution network to flow field prediction, and the SCNN shows outstanding performance on prediction accuracy and computational resource saving for the flow problems with a sparse input information.

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Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus

Cited by 4 publications

References 54 publications

Computationally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network

Computationally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network

Flow pattern identification of gas-liquid two-phase flow based on integrating mechanism analysis and data mining

Prediction of internal and external flow with sparse convolution neural network: A computationally effective reduced-order model

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