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Fluids
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Advances in Multiphase Flow Simulation with Machine Learning
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Advances in Multiphase Flow Simulation with Machine Learning

A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Flow of Multi-Phase Fluids and Granular Materials".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 5599

Special Issue Editors

Dr. Chaouki Habchi

E-Mail Website1 Website2
Guest Editor
IFP Energies Nouvelles, Institut Carnot Transports Energies, 1 et 4 Avenue de Bois-Préau, 92852 Rueil-Malmaison, France
Interests: CFD; simulation; multiphase flow; thermodynamics; eFuels; biofuels; artificial neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Michele Battistoni

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, 06123 Perugia, Italy
Interests: computational fluid dynamics (CFD); sprays; multiphase flows; internal combustion engines; fuels
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

E-fuels (such as hydrogen, ammonia and methanol) have been identified to replace fossil fuels and promote carbon-free transport and decentralized power generation with gas turbines, for example. This is why multiphase computational fluid dynamics (CFD) simulation is a hot topic in many research laboratories and industry today. These developments will require precise answers to several open questions of a fundamental scientific nature. For this, fast, robust and accurate CFD models are needed to go beyond academic simulations and reduce the time and cost of developing cutting-edge technologies capable of combating global warming. However, multi-dimensional numerical simulation is generally computationally expensive and can require significant memory capacity, for example, to store the physical properties of the e-fuels over a wide range of pressures, temperatures and compositions. The aim of this Special Issue is to demonstrate the efficiency and robustness of CFD simulation when the numerical solver is coupled with an artificial intelligence method, such as deep learning.

Dr. Chaouki Habchi
Dr. Michele Battistoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiphase flow
  • simulation
  • machine learning
  • inference
  • optimization
  • acceleration
  • CPU/GPU

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Published Papers (3 papers)

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Research

25 pages, 4476 KiB  
Article
A Machine Learning Approach to Volume Tracking in Multiphase Flow Simulations
by Aaron Mak and Mehdi Raessi
Fluids 2025, 10(2), 39; https://doi.org/10.3390/fluids10020039 - 2 Feb 2025
Viewed by 640
Abstract
This work presents a machine learning (ML) approach to volume-tracking for computational simulations of multiphase flow. It is an alternative to a commonly used procedure in the volume-of-fluid (VOF) method for volume tracking, in which the phase interfaces are reconstructed for flux calculation [...] Read more.
This work presents a machine learning (ML) approach to volume-tracking for computational simulations of multiphase flow. It is an alternative to a commonly used procedure in the volume-of-fluid (VOF) method for volume tracking, in which the phase interfaces are reconstructed for flux calculation followed by volume advection. Bypassing the computationally expensive steps of interface reconstruction and flux calculation, the proposed ML approach performs volume advection in a single step, directly predicting the volume fractions at the next time step. The proposed ML function is two-dimensional and has eleven inputs. It was trained using MATLAB’s (R2021a) Deep Learning Toolbox with a grid search method to find an optimal neural network configuration. The performance of the ML function is assessed using canonical test cases, including translation, rotation, and vortex tests. The errors in the volume fraction fields obtained by the ML function are compared with those of the VOF method. In ideal conditions, the ML function speeds up the computations four times compared to the VOF method. However, in terms of overall robustness and accuracy, the VOF method remains superior. This study demonstrates the potential of applying ML methods to multiphase flow simulations while highlighting areas for further improvement. Full article
(This article belongs to the Special Issue Advances in Multiphase Flow Simulation with Machine Learning)
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24 pages, 6157 KiB  
Article
Machine Learning Model for Gas–Liquid Interface Reconstruction in CFD Numerical Simulations
by Tamon Nakano, Michele Alessandro Bucci, Jean-Marc Gratien and Thibault Faney
Fluids 2025, 10(1), 20; https://doi.org/10.3390/fluids10010020 - 20 Jan 2025
Cited by 1 | Viewed by 932
Abstract
The volume of fluid (VoF) method is widely used in multiphase flow simulations to track and locate the interface between two immiscible fluids. The relative volume fraction in each cell is used to recover the interface properties (i.e., normal, location, and curvature). Accurate [...] Read more.
The volume of fluid (VoF) method is widely used in multiphase flow simulations to track and locate the interface between two immiscible fluids. The relative volume fraction in each cell is used to recover the interface properties (i.e., normal, location, and curvature). Accurate computation of the local interface curvature is essential for evaluation of the surface tension force at the interface. However, this interface reconstruction step is a major bottleneck of the VoF method due to its high computational cost and low accuracy on unstructured grids. Recent attempts to apply data-driven approaches to this problem have outperformed conventional methods in many test cases. However, these machine learning-based methods are restricted to computations on structured grids. In this work, we propose a machine learning-enhanced VoF method based on graph neural networks (GNNs) to accelerate interface reconstruction on general unstructured meshes. We first develop a methodology for generating a synthetic dataset based on paraboloid surfaces discretized on unstructured meshes to obtain a dataset akin to the configurations encountered in industrial settings. We then train an optimized GNN architecture on this dataset. Our approach is validated using analytical solutions and comparisons with conventional methods in the OpenFOAM framework on a canonical test. We present promising results for the efficiency of GNN-based approaches for interface reconstruction in multiphase flow simulations in the industrial context. Full article
(This article belongs to the Special Issue Advances in Multiphase Flow Simulation with Machine Learning)
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19 pages, 8707 KiB  
Article
Computation of Real-Fluid Thermophysical Properties Using a Neural Network Approach Implemented in OpenFOAM
by Nasrin Sahranavardfard, Damien Aubagnac-Karkar, Gabriele Costante, Faniry N. Z. Rahantamialisoa, Chaouki Habchi and Michele Battistoni
Fluids 2024, 9(3), 56; https://doi.org/10.3390/fluids9030056 - 23 Feb 2024
Cited by 2 | Viewed by 2719
Abstract
Machine learning based on neural networks facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this [...] Read more.
Machine learning based on neural networks facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver called realFluidReactingNNFoam, under development at the University of Perugia, in OpenFOAM with a neural network algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained neural network models. Currently, neural network models are trained against data generated using the Peng–Robinson equation of state assuming a mixture’s frozen temperature. The OpenFOAM solver, where needed, calls the neural network models in each grid cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for thermophysical properties is achieved via the “Neural Network Inference in C made Easy (NNICE)” library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the Peng–Robinson equation of state and the Chung transport model. First, the development of a real-fluid model with an artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational efficiency. The results of the overall implementation indicate that the combined OpenFOAM and machine learning approach provides a speed-up factor higher than seven, while preserving the original solver accuracy. Full article
(This article belongs to the Special Issue Advances in Multiphase Flow Simulation with Machine Learning)
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