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Combining machine learning (ML) with computational fluid dynamics (CFD) opens many possibilities for improving simulations of technical and natural systems. However, CFD+ML algorithms require exchange of data, synchronization, and…

Machine Learning · Computer Science 2024-06-25 Tomislav Maric , Mohammed Elwardi Fadeli , Alessandro Rigazzi , Andrew Shao , Andre Weiner

The separation between molecular and mesoscopic length and time scales poses a severe limit to molecular simulations of mesoscale phenomena. We describe a hybrid multiscale computational technique which address this problem by keeping the…

Fluid Dynamics · Physics 2009-11-13 G. De Fabritiis , R. Delgado-Buscalioni , P. V. Coveney

We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient prediction of multiscale fluidic systems. By using a Gaussian…

Fluid Dynamics · Physics 2016-03-16 David Stephenson , James R Kermode , Duncan A Lockerby

A comparative study on mesh-based and mesh-less Computational Fluid Dynamics (CFD) approaches coupled with the Discrete Element Method (DEM) is presented. As the mesh-based CFD approach a Finite Volume Method (FVM) is used. A Smoothed…

Fluid Dynamics · Physics 2016-09-28 D. Markauskas , H. Kruggel-Emden , R. Sivanesapillai , H. Steeb

The article reviews fluid flow models implemented in the leading CFD software tools and designed for simulation of multi-component and multi-phase flows, compressible flows, flows with heat transfer, cavitation and other phenomena. The…

Fluid Dynamics · Physics 2007-05-23 Alexey N. Kochevsky

A part of non-Newtonian fluids are yield stress fluids. They require a minimum stress to flow. Below this minimum value, yield stress fluids remain solid. To date, 1D and 2D numerical models have been used predominantly to study free…

Fluid Dynamics · Physics 2018-08-03 N Schaer , J. Vazquez , M. Dufresne , G Isenmann , J. Wertel

The never-ending computational demand from simulations of turbulence makes computational fluid dynamics (CFD) a prime application use case for current and future exascale systems. High-order finite element methods, such as the spectral…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-05-10 Martin Karp , Estela Suarez , Jan H. Meinke , Måns I. Andersson , Philipp Schlatter , Stefano Markidis , Niclas Jansson

Detailed studies of the intriguing field-dependent dynamics and transport properties of confined flowing ferrofluids require efficient mesoscopic simulation methods that account for fluctuating ferrohydrodynamics. Here, we propose such a…

Soft Condensed Matter · Physics 2024-06-19 Patrick Ilg

Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations…

Computational fluid dynamics (CFD) studies of left atrial flows have reached a sophisticated level, e.g., revealing plausible relationships between hemodynamics and stresses with atrial fibrillation. However, little focus has been on…

This paper presents the first application of quantum generative models to learned latent space representations of computational fluid dynamics (CFD) data. While recent work has explored quantum models for learning statistical properties of…

Machine Learning · Computer Science 2025-12-30 Achraf Hsain , Fouad Mohammed Abbou

A new Micro-Macro-Surrogate (MMS) hybrid method is presented that couples the Direct Simulation Monte Carlo (DSMC) method with Computational Fluid Dynamics (CFD) to simulate low-speed rarefied gas flows. The proposed MMS method incorporates…

The coarse-grained molecular dynamics (MD) or Brownian dynamics (BD) simulation is a particle-based approach that has been applied to a wide range of biological problems that involve interactions with surrounding fluid molecules or the…

Soft Condensed Matter · Physics 2014-11-20 Szu-Pei Fu , Yuan-Nan Young , Shidong Jiang

Developing efficient path integral (PI) methods for atomistic simulations of vibrational spectra in heterogeneous condensed phases and interfaces has long been a challenging task. Here, we present the h-CMD method, short for hybrid centroid…

Chemical Physics · Physics 2025-05-08 Dil K. Limbu , Nathan London , Md Omar Faruque , Mohammad R. Momeni

Various experiments have found a boundary slip in hydrophobic microchannel flows, but a consistent understanding of the results is still lacking. While Molecular Dynamics (MD) simulations cannot reach the low shear rates and large system…

Fluid Dynamics · Physics 2007-05-23 Jens Harting , Christian Kunert , Hans J. Herrmann

CFD-DEM is used to simulate solid-fluid systems. DEM models the motion of discrete particles while CFD models the fluid phase. Coupling both necessitates the calculation of the void fraction and the solid-fluid forces resulting in a…

Fluid Dynamics · Physics 2023-01-06 Toni El Geitani , Shahab Golshan , Bruno Blais

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity…

Computational Physics · Physics 2019-10-18 Han Bao , Jinyong Feng , Nam Dinh , Hongbin Zhang

Computational fluid dynamics (CFD) is an important tool for the simulation of the cardiovascular function and dysfunction. Due to the complexity of the anatomy, the transitional regime of blood flow in the heart, and the strong mutual…

In this paper, a hybrid quasi-static atomistic simulation method at finite temperature is developed, which combines the advantages of MD for thermal equilibrium and atomic-scale finite element method (AFEM) for efficient equilibration. Some…

Atomic Physics · Physics 2015-06-17 Ran Xu , Bin Liu

Various biological system models have been proposed in systems biology, which are based on the complex biological reactions kinetic of various components. These models are not practical because we lack of kinetic information. In this paper,…

Quantitative Methods · Quantitative Biology 2011-02-19 Weidong Huang , Chundu Wu , Bingjia Xiao , Weidong Xia