ML-ILES: End-to-end optimization of data-driven high-order Godunov-type finite-volume schemes for compressible homogeneous isotropic turbulence DOI Creative Commons
Deniz A. Bezgin, Aaron B. Buhendwa, Steffen J. Schmidt

et al.

Journal of Computational Physics, Journal Year: 2024, Volume and Issue: 522, P. 113560 - 113560

Published: Nov. 14, 2024

Language: Английский

OpenFOAMGPT: A retrieval-augmented large language model (LLM) agent for OpenFOAM-based computational fluid dynamics DOI
Sandeep Pandey,

Ran Xu,

Wenkang Wang

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

This work presents a large language model (LLM)-based agent OpenFOAMGPT tailored for OpenFOAM-centric computational fluid dynamics (CFD) simulations, leveraging two foundation models from OpenAI: the GPT-4o (GPT means Generative Pre-trained Transformer) and chain-of-thought–enabled o1 preview model. Both agents demonstrate success across multiple tasks. While price of token with is six times as that GPT-4o, it consistently exhibits superior performance in handling complex tasks, zero-shot/few-shot case setup to boundary condition modifications, zero-shot turbulence adjustments, code translation. Through an iterative correction loop, efficiently addressed single-phase multiphase flow, heat transfer, Reynolds-averaged Navier–Stokes modeling, eddy simulation, other engineering scenarios, often converging limited number iterations at low costs. To embed domain-specific knowledge, we employed retrieval-augmented generation pipeline, demonstrating how preexisting simulation setups can further specialize subdomains such energy aerospace. Despite great agent, human oversight remains crucial ensuring accuracy adapting shifting contexts. Fluctuations over time suggest need monitoring mission-critical applications. Although our demonstrations focus on OpenFOAM, adaptable nature this framework opens door developing LLM-driven into wide range solvers codes. By streamlining CFD approach has potential accelerate both fundamental research industrial advancements.

Language: Английский

Citations

3

Data-driven methods for flow and transport in porous media: A review DOI Creative Commons
Guang Yang, Ran Xu, Yusong Tian

et al.

International Journal of Heat and Mass Transfer, Journal Year: 2024, Volume and Issue: 235, P. 126149 - 126149

Published: Sept. 7, 2024

Language: Английский

Citations

15

Scientific machine learning for closure models in multiscale problems: a review DOI Open Access
Benjamin Sanderse, Panos Stinis, Romit Maulik

et al.

Foundations of Data Science, Journal Year: 2024, Volume and Issue: 7(1), P. 298 - 337

Published: Oct. 9, 2024

Language: Английский

Citations

10

Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning DOI Creative Commons
Pol Suárez, Francisco Alcántara-Ávila, Jean Rabault

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 29, 2024

Abstract Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control drag reduction using deep reinforcement learning. We introduce high-dimensional AFC setup on 3D cylinder, considering Reynolds numbers (Re_D) from 100 to 400, which range including transition wake instabilities. The involves multiple zero-net-mass-flux jets positioned top and bottom surfaces, aligned into two slots. method relies coupling computational-fluid-dynamics solver multi-agent reinforcement-learning (MARL) framework based proximal-policy-optimization algorithm. MARL offers several advantages: it exploits local invariance, adaptable across geometries, facilitates transfer learning cross-application agents, results in significant training speedup. For instance, our demonstrate 21% Re_D=300, outperforming classical periodic control, yields up 6% reduction. To authors' knowledge, present MARL-based represents first time where conducted cylinders. This breakthrough paves way conducting progressively more complex turbulent-flow configurations.

Language: Английский

Citations

7

Large eddy simulation modeling of incompressible turbulence governed by vorticity transport equations DOI
X.Q. Hou, Ning Chang, Zelong Yuan

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(1)

Published: Jan. 1, 2025

We study large eddy simulation (LES) in the form of vorticity transport equations (VTE), employing six subgrid-scale (SGS) models, including dynamic Smagorinsky model, mixed velocity gradient scale similarity approximate deconvolution and iterative (DIAD) model. In a priori study, correlation coefficient SGS stress given by DIAD is significantly higher than those other structural relative error lowest. posteriori validation, models outperform functional predicting energy spectra, enstrophy probability density functions (PDFs) vorticity, strain-rate tensor, flux, production term. These results confirm feasibility VTE-based LES. They also indicate that classic modeling approaches for terms filtered Navier–Stokes (NSE) are applicable to counterparts VTE.

Language: Английский

Citations

1

Numerically Consistent Data-Driven Subgrid-Scale Model via Data Assimilation and Machine Learning DOI
Yuenong Ling, Adrian Lozano-Durán

AIAA SCITECH 2022 Forum, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0

Correcting Unsteady Low Order Discontinuous Galerkin Simulations DOI

Anna Kiener,

Philipp Bekemeyer

AIAA SCITECH 2022 Forum, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0

Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers DOI Creative Commons
David Huergo, Martín de Frutos, Eduardo Jané

et al.

Journal of Computational Physics, Journal Year: 2025, Volume and Issue: unknown, P. 114080 - 114080

Published: May 1, 2025

Language: Английский

Citations

0

Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation DOI Creative Commons
Syver Døving Agdestein, Benjamin Sanderse

Journal of Computational Physics, Journal Year: 2024, Volume and Issue: 522, P. 113577 - 113577

Published: Nov. 15, 2024

We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations on paradigm "discretize first, filter and close next".This leads to full model-data consistency allows employing closure models in same environment as where they have been trained.Since LES discretization error is included learning process, can learn account discretization.Furthermore, we employ divergence-consistent discrete defined through face-averaging provide novel theoretical numerical analysis.This preserves divergence-free constraint by construction, unlike general filters such volume-averaging filters.We show that using formulation coupled with convolutional model produces stable accurate results both a-priori a-posteriori training, while (divergence-inconsistent) requires training or other stabilityenforcing measures.

Language: Английский

Citations

3

A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers DOI Creative Commons
David Huergo, Laura Alonso Alemany, Saumitra Joshi

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102949 - 102949

Published: Sept. 1, 2024

Language: Английский

Citations

2