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Mathematics
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Artificial Intelligence for Fluid Mechanics
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Artificial Intelligence for Fluid Mechanics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1267

Special Issue Editors

Dr. Andreas Lintermann

E-Mail Website
Guest Editor
Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Interests: computational fluid dynamics; multi-physics simulations; code coupling; high-performance computing; modular supercomputing architectures; artificial intelligence; deep learning; hyperparameter optimization; quantum computing
Dr. Guillaume Houzeaux

E-Mail Website
Guest Editor
CASE, Barcelona Supercomputing Center, Barcelona, Spain
Interests: computational mechanics; numerical methods; multi-physics simulations; multi-scale simulations; code coupling; high-performance computing; heterogeneous computing; artificial intelligence

Special Issue Information

Dear Colleagues,

AI methods continuously penetrate into various fields of research and industry. Huge amounts of data in fluid mechanics research are nowadays produced by simulations or experiments. They provide the opportunity to train novel AI technologies resulting in predictive models competing with conventional physical and numerical models. In a rapidly evolving supercomputing context, the AI training process relies more and more on HPC to produce and process data and thus poses implementation and performance challenges.

This Special Issue will present cutting-edge work on novel AI technologies that advance our understanding of fluid mechanics problems. Manuscripts developing AI models, dealing with training on computational or experimental fluid mechanics data, making use of HPC systems in this context, and employing the final models, e.g., in inferencing at simulation runtime to model unresolved small-scale phenomena or in experiments in processing and mining data, are welcome. This includes coupling methods employing state-of-the-art computing hardware to bring traditional fluid mechanics workflows and AI workflows together.

Topics of interest for this Special Issues include, but are not limited to, the following: CFD, multi-physics simulations, experimental fluid mechanics, machine learning, physics-informed neural networks, deep learning, and AI-based subgrid-scale or surrogate modeling. Manuscripts describing original theoretical and applied research are welcome for submission.

Dr. Andreas Lintermann
Dr. Guillaume Houzeaux
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • artificial intelligence
  • machine learning
  • deep learning
  • physics-informed neural networks
  • unsupervised learning
  • fluid mechanics
  • computational fluid dynamics
  • simulation
  • experimental fluid mechanics
  • high-performance computing

Published Papers (2 papers)

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Research

21 pages, 1014 KiB  
Article
Hybrid AI-Analytical Modeling of Droplet Dynamics on Inclined Heterogeneous Surfaces
by Andreas D. Demou and Nikos Savva
Mathematics 2024, 12(8), 1188; https://doi.org/10.3390/math12081188 - 15 Apr 2024
Viewed by 361
Abstract
This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model [...] Read more.
This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fluid Mechanics)
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23 pages, 15847 KiB  
Article
Toward the Usage of Deep Learning Surrogate Models in Ground Vehicle Aerodynamics
by Benet Eiximeno, Arnau Miró, Ivette Rodríguez and Oriol Lehmkuhl
Mathematics 2024, 12(7), 998; https://doi.org/10.3390/math12070998 - 27 Mar 2024
Viewed by 496
Abstract
This study introduces a deep learning surrogate model designed to predict the evolution of the mean pressure coefficient on the back face of a Windsor body across a range of yaw angles from 2.5∘ to 10∘. Utilizing a [...] Read more.
This study introduces a deep learning surrogate model designed to predict the evolution of the mean pressure coefficient on the back face of a Windsor body across a range of yaw angles from 2.5∘ to 10∘. Utilizing a variational autoencoder (VAE), the model effectively compresses snapshots of back pressure taken at yaw angles of 2.5∘, 5∘, and 10∘ into two latent vectors. These snapshots are derived from wall-modeled large eddy simulations (WMLESs) conducted at a Reynolds number of ReL=2.9×106. The frequencies that dominate the latent vectors correspond closely with those observed in both the drag’s temporal evolution and the dynamic mode decomposition. The projection of the mean pressure coefficient to the latent space yields an increasing linear evolution of the two latent variables with the yaw angle. The mean pressure coefficient distribution at a yaw angle of 7.5∘ is predicted with a mean error of e¯=3.13% when compared to the WMLESs results after obtaining the values of the latent space with linear interpolation. Full article
(This article belongs to the Special Issue Artificial Intelligence for Fluid Mechanics)
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