Variational Multi-Scale Super-Resolution: A Data-Driven Approach for Reconstruction and Predictive Modeling of Unresolved Physics

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

Fukami

Fukagata

Taira

2023

Theor. Comput. Fluid Dyn.

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A unified understanding of scale-resolving simulations and near-wall modelling of turbulent flows using optimal finite-element projections

Pradhan

Duraisamy²

2023

J. Fluid Mech.

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The main objective of this work is to develop a unified framework that can be used as a lens to quantitatively assess and augment a wide range of coarse-grained models of turbulence, namely large eddy simulations (LES), hybrid Reynolds-averaged/LES methods and wall-modelled (WM)LES. Taking a turbulent channel flow as an example, optimality is assessed in the wall-resolved limit, the hybrid RANS–LES limit and the WMLES limit, via projections at different resolutions suitable for these approaches. These optimal a priori estimates are shown to have similar characteristics to existing a posteriori solutions reported in the literature. Consistent accuracy metrics are developed for scale-resolving methods using the optimal solution as a reference, and evaluations are performed. We further characterise the slip velocity in WMLES in terms of the near-wall under-resolution and develop a universal scaling relationship. Insights from the a priori tests are used to augment existing slip-based wall models. Various a posteriori tests reveal superior performance over the dynamic slip wall model. Guidance for the development of improved slip-wall models is provided, including a target for the dynamic procedure.

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Variational Multi-Scale Super-Resolution: A Data-Driven Approach for Reconstruction and Predictive Modeling of Unresolved Physics

Cited by 2 publications

References 58 publications

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

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

A unified understanding of scale-resolving simulations and near-wall modelling of turbulent flows using optimal finite-element projections

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