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Related papers: A Time-Accurate Inflow Coupling for Zonal LES

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In this paper, we address the challenge of accurately calculating droplet/bubble properties (e.g., volume, number) in diffuse-interface two-phase flow simulations. Currently, flood-fill algorithms can truncate a significant portion of the…

Computational Physics · Physics 2025-07-10 Pranav J. Nathan , Suhas S. Jain

Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, including oscillations and high-frequency variations. Flow Matching (FM) offers an efficient…

Machine Learning · Computer Science 2026-05-29 Junru Zhang , Lang Feng , Jinbo Wang , Xu Guo , Yucheng Wang , Han Yu , Min Wu , Yabo Dong , Duanqing Xu

Identification of coherent structures is an essential step to describe and model turbulence generation mechanisms in wall-bounded flows. To this end we present a clustering method based on Latent Dirichlet Allocation (LDA), a generative…

Fluid Dynamics · Physics 2021-07-07 Mohamed Frihat , Bérengère Podvin , Lionel Mathelin , Yann Fraigneau , François Yvon

In the present work, large-eddy simulations of free supersonic jet flows are performed to investigate the influence of inflow conditions on the jet flow field and its turbulent properties. A high-order nodal discontinuous Galerkin method is…

Fluid Dynamics · Physics 2026-02-11 Diego F. Abreu , Joao Luiz F. Azevedo , Carlos Junqueira-Junior

The multiscale patch scheme is built from given small micro-scale simulations of complicated physical processes to empower large macro-scale simulations. By coupling small patches of simulations over unsimulated spatial gaps, large savings…

Computational Physics · Physics 2019-12-18 J. E. Bunder , J. Divahar , Ioannis G. Kevrekidis , Trent W. Mattner , A. J. Roberts

Large Eddy Simulations of turbulent flows are powerful tools used in many engineering and geophysical settings. Choosing the right value of the free parameters for their subgrid scale models is a crucial task for which the current methods…

Fluid Dynamics · Physics 2021-02-03 M. Buzzicotti , P. Clark Di Leoni

Interfacial fluctuations in a two-phase binary fluid mixture reveal signatures of underlying physical processes that occur within each phase and on a range of spatial and temporal scales. In this study, we investigate a model binary fluid…

Fluid Dynamics · Physics 2026-03-04 Samuel Z Khiangte , Triparna Sanyal , Sumantra Sarkar , Nairita Pal

Generative models based on dynamical equations such as flows and diffusions offer exceptional sample quality, but require computationally expensive numerical integration during inference. The advent of consistency models has enabled…

Machine Learning · Computer Science 2025-06-04 Nicholas M. Boffi , Michael S. Albergo , Eric Vanden-Eijnden

Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This…

Machine Learning · Computer Science 2026-03-19 Nishant Kumar , Franck Kerhervé , Lionel Agostini , Laurent Cordier

Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. This study performs super-resolution analysis on turbulent…

Performing highly accurate simulations of droplet systems is a challenging problem. This is primarily due to the interface dynamics which is complicated further by the addition of surfactants. This paper presents a boundary integral method…

Numerical Analysis · Mathematics 2019-05-01 Sara Pålsson , Michael Siegel , Anna-Karin Tornberg

Over the last years, supervised learning (SL) has established itself as the state-of-the-art for data-driven turbulence modeling. In the SL paradigm, models are trained based on a dataset, which is typically computed a priori from a…

Fluid Dynamics · Physics 2022-12-23 Marius Kurz , Philipp Offenhäuser , Andrea Beck

We present a predictive master spectrum describing turbulence-like flows in microfluidic systems. Extending Pao's viscous-range closure, the model introduces (i) an adaptive inertial-range slope dependent on measurable dimensionless numbers…

Fluid Dynamics · Physics 2025-12-16 Chit Yau Kuan , Xiaochen Liu , Yi-Ping Ho , Ken-Tye Yong

Motivated by previous work on kinetic energy cascades in the ocean and atmosphere, we develop a spatio-temporal spectral transfer tool that can be used to study scales of variability in generalized dynamical systems. In particular, we use…

Allocating extra computation at inference time has recently improved sample quality in large language models and diffusion-based image generation. In parallel, Flow Matching (FM) has gained traction in language, vision, and scientific…

Machine Learning · Computer Science 2025-10-21 Adam Stecklov , Noah El Rimawi-Fine , Mathieu Blanchette

This article presents the openCFS submodule scattered data reader for coupling multi-physical simulations performed in different simulation programs. For instance, by considering a forward-coupling of a surface vibration simulation…

Computational Engineering, Finance, and Science · Computer Science 2023-08-10 Stefan Schoder

Deep learning (DL) has recently emerged as a candidate for closure modeling of large-eddy simulation (LES) of turbulent flows. High-fidelity training data is typically limited: it is computationally costly (or even impossible) to…

Fluid Dynamics · Physics 2023-03-07 Justin Sirignano , Jonathan F. MacArt

Two-phase outflows refer to situations where the interface formed between two immiscible incompressible fluids passes through open portions of the domain boundary. We present in this paper several new forms of open boundary conditions for…

Fluid Dynamics · Physics 2014-12-31 S. Dong , X. Wang

With increasing emphasis on carbon neutrality, accurate and efficient combustion prediction has become essential for the design and optimization of new generation combustion systems. This study established a computational framework by…

Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or even protein structures. These models are commonly trained…

Machine Learning · Computer Science 2025-05-30 Yuyang Wang , Anurag Ranjan , Josh Susskind , Miguel Angel Bautista