# Deep learning at scale for subgrid modeling in turbulent flows

@inproceedings{Bode2019DeepLA, title={Deep learning at scale for subgrid modeling in turbulent flows}, author={Mathis Bode and Michael Gauding and Konstantin Kleinheinz and Heinz Pitsch}, booktitle={ISC Workshops}, year={2019} }

Modeling of turbulent flows is still challenging. One way to deal with the large scale separation due to turbulence is to simulate only the large scales and model the unresolved contributions as done in large-eddy simulation (LES). This paper focuses on two deep learning (DL) strategies, regression and reconstruction, which are data-driven and promising alternatives to classical modeling concepts. Using three-dimensional (3-D) forced turbulence direct numerical simulation (DNS) data, subgrid…

## 11 Citations

### Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows

- Computer ScienceArXiv
- 2019

This work presents a novel subgrid modeling approach based on a generative adversarial network (GAN), which is trained with unsupervised deep learning (DL) using adversarial and physics-informed losses to improve the generalization capability, especially extrapolation, of the network.

### Automated Dissipation Control for Turbulence Simulation with Shell Models

- PhysicsArXiv
- 2022

This work constructs a strongly simplified representation of turbulence by using the Gledzer-OhkitaniYamada shell model and proposes an approach that aims to reconstruct statistical properties of turbulence such as the self-similar inertial-range scaling, where it could achieve encouraging experimental results.

### Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data

- Computer Science, PhysicsArXiv
- 2022

The recently developed PIESRGAN modeling approach to turbulent premixed combustion can very accurately reproduce direct numerical simulation data on much coarser meshes, which is hardly possible with classical subﬁlter models, and enables studying statistical processes more ef ﬁciently due to the smaller computing cost.

### Sparse identification of multiphase turbulence closures for coupled fluid–particle flows

- EngineeringJournal of Fluid Mechanics
- 2021

Abstract In this work, model closures of the multiphase Reynolds-averaged Navier–Stokes (RANS) equations are developed for homogeneous, fully developed gas–particle flows. To date, the majority of…

### Multiphase turbulence modeling using sparse regression and gene expression programming

- Computer Science
- 2021

This work proposes an approach that blends sparse regression and gene expression programming (GEP) to generate closed-form algebraic models from simulation data to capture the physics of turbulence closure modeling.

### Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors

- Computer ScienceArXiv
- 2022

The modeling approach of PIESRGAN is modiﬁed to accurately account for the challenges in the context of laminar ﬁnite-rate-chemistry ﬂows and a reduced PiesRGAN-based model is presented that solves only the major species on a reconstructed �áeld and employs PIERSGAN lookup for the remaining species, utilizing staggering in time.

### Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow

- PhysicsArXiv
- 2022

This paper extends the methodology to use physics-informed enhanced super-resolution generative adversarial networks (PIESR-GANs) for LES subﬁlter modeling in turbulent ﬂows with ﬁnite-rate chemistry…

### Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows

- PhysicsLecture Notes in Computer Science
- 2021

### Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data

- Computer ScienceArXiv
- 2020

A novel deep learning framework SR-DNS Net is introduced, which aims to mitigate this inherent trade-off between solution fidelity and computational complexity by leveraging deep learning techniques used in image super-resolution.

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