Machine-learning-based reconstruction of transient vortex-airfoil wake interaction

Ye, Zhong; Fukami, Kai; An, Byungjin; Taira, Kunihiko

doi:10.2514/6.2022-3244

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Supervised Learning1

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2023

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“…Compared to solely using fully connected layers, the computational cost can be significantly reduced while maintaining the reconstruction accuracy. A similar MLP-CNN model was also considered by Zhong et al [140,141] for a vortex-airfoil gust interaction problem. The model estimates a two-dimensional vorticity field from pressure sensor measurements on an airfoil surface.…”

Section: Supervised Learningmentioning

confidence: 99%

Super-resolution analysis via machine learning: a survey for fluid flows

Fukami

Fukagata

Taira

2023

Theor. Comput. Fluid Dyn.

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This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data. Graphical abstract

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Section: Supervised Learningmentioning

confidence: 99%