Towards Incompressible Laminar Flow Estimation Based on Interpolated Feature Generation and Deep Learning

Thi-Thu-Huong Le (), Hyoeun Kang and Howon Kim ()
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Thi-Thu-Huong Le: IoT Research Center, Pusan National University, Busan 609735, Korea
Hyoeun Kang: School of Computer Science and Engineering, Pusan National University, Busan 609735, Korea
Howon Kim: School of Computer Science and Engineering, Pusan National University, Busan 609735, Korea

Sustainability, 2022, vol. 14, issue 19, 1-16

Abstract: For industrial design and the improvement of fluid flow simulations, computational fluid dynamics (CFD) solvers offer practical functions and conveniences. However, because iterative simulations demand lengthy computation times and a considerable amount of memory for sophisticated calculations, CFD solvers are not economically viable. Such limitations are overcome by CFD data-driven learning models based on neural networks, which lower the trade-off between accurate simulation performance and model complexity. Deep neural networks (DNNs) or convolutional neural networks (CNNs) are good illustrations of deep learning-based CFD models for fluid flow modeling. However, improving the accuracy of fluid flow reconstruction or estimation in these earlier methods is crucial. Based on interpolated feature data generation and a deep U-Net learning model, this work suggests a rapid laminar flow prediction model for inference of Naiver–Stokes solutions. The simulated dataset consists of 2D obstacles in various positions and orientations, including cylinders, triangles, rectangles, and pentagons. The accuracy of estimating velocities and pressure fields with minimal relative errors can be improved using this cutting-edge technique in training and testing procedures. Tasks involving CFD design and optimization should benefit from the experimental findings.

Keywords: CFD (computational fluid dynamics); laminar flow; Naiver–Stokes equation; simulation; interpolation; U-Net (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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