Kinetic data-driven approach to turbulence subgrid modeling Article Swipe
Giulio Ortali
,
Alessandro Gabbana
,
Nicola Demo
,
Gianluigi Rozza
,
Federico Toschi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/physrevresearch.7.013202
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/physrevresearch.7.013202
Numerical simulations of turbulent flows are well known to pose extreme computational challenges because of the huge number of dynamical degrees of freedom required to correctly describe the complex multiscale statistical correlations of the velocity. On the other hand, kinetic mesoscale approaches based on the Boltzmann equation, have the potential to describe a broad range of flows, stretching well beyond the special case of gases close to equilibrium, which results in the ordinary Navier-Stokes dynamics. Here, we demonstrate that, by properly tuning, a kinetic approach can statistically reproduce the quantitative dynamics of the larger scales in turbulence, thereby providing an alternative, computationally efficient and physically rooted approach toward subgrid scale (SGS) modeling in turbulence. More specifically, we show that by leveraging data from fully resolved direct numerical simulation (DNS), we can learn a collision operator for the discretized Boltzmann equation solver (the lattice Boltzmann method), which effectively implies a turbulence subgrid closure model. The mesoscopic nature of our formulation makes the learning problem fully local in both space and time, leading to reduced computational costs and enhanced generalization capabilities. We show that the model offers superior performance compared to traditional methods, such as the Smagorinsky model, being less dissipative and, therefore, able to more closely capture the intermittency of higher-order velocity correlations. This foundational study lays the basis for extending the proposed framework to different turbulent flow settings and—most importantly—to develop new classes of hybrid data-driven kinetic-based models capable of faithfully capturing the complex macroscopic dynamics of diverse physical systems such as emulsions, non-Newtonian fluid, and multiphase systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physrevresearch.7.013202
- http://link.aps.org/pdf/10.1103/PhysRevResearch.7.013202
- OA Status
- gold
- Cited By
- 1
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407911246
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407911246Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physrevresearch.7.013202Digital Object Identifier
- Title
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Kinetic data-driven approach to turbulence subgrid modelingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-02-25Full publication date if available
- Authors
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Giulio Ortali, Alessandro Gabbana, Nicola Demo, Gianluigi Rozza, Federico ToschiList of authors in order
- Landing page
-
https://doi.org/10.1103/physrevresearch.7.013202Publisher landing page
- PDF URL
-
https://link.aps.org/pdf/10.1103/PhysRevResearch.7.013202Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://link.aps.org/pdf/10.1103/PhysRevResearch.7.013202Direct OA link when available
- Concepts
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Turbulence, Statistical physics, K-omega turbulence model, Turbulence kinetic energy, Kinetic energy, K-epsilon turbulence model, Computer science, Physics, Mechanics, Classical mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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