Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
1.8
Journals
Fluids
Special Issues
Machine Learning and Artificial Intelligence in Fluid Mechanics, 2nd Edition
share announcement

Machine Learning and Artificial Intelligence in Fluid Mechanics, 2nd Edition

A special issue of Fluids (ISSN 2311-5521).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3332

Special Issue Editor

Dr. Filippos Sofos

E-Mail Website
Guest Editor
Department of Physics, School of Science, University of Thessaly, 35100 Lamia, Greece
Interests: machine learning; symbolic regression; computational hydraulics; molecular dynamics; smoothed-particle hydrodynamics; multiscale modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fluid mechanics research has evolved over the past few years toward exploiting massive amounts of data generated from knowledge gathered thus far, either from experimental measurements or simulations. The application of novel machine learning (ML) techniques is currently a trend in the field and is almost standardized. Computational boosting, advanced turbulence modeling, scale bridging, hybrid simulation schemes, and flow feature extraction are concepts that scientists and engineers must address.

This Special Issue aims to bring together data science methods and advanced artificial intelligence and machine learning techniques to apply them to popular fluid mechanics problems in an alternative, though effective and accurate, manner, strictly bound to the physical problem.

We encourage authors to submit works addressing topics including, but not limited to, physics-inspired neural networks, intelligent fluid dynamics, scientific machine learning, explainable and trustworthy artificial intelligence, symbolic regression and evolutionary algorithms, and unsupervised machine learning and clustering, with a focus on fluid mechanics applications.

Dr. Filippos Sofos
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine learning
  • data-driven fluid mechanics
  • turbulence modeling
  • reduced-order CFD
  • neural networks
  • symbolic regression

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 7227 KB  
Article
From Laboratory Measurements to AI-Driven Insights: Predicting Shaped Charge Performance with Advanced Machine Learning
by Samuel Nashed, Muhammad Abdullah, Oluchi Ejehu, Badr Mohamed, Norhan Sedki and Rouzbeh Moghanloo
Fluids 2026, 11(3), 64; https://doi.org/10.3390/fluids11030064 - 27 Feb 2026
Viewed by 396
Abstract
The accurate estimation of the perforation length is very vital to improve fluid flow as well as the management of charges. Traditional methods, including empirical correlations, analytical models, and API 19B surface tests, suffer from significant limitations in their scope, require frequent recalibration, [...] Read more.
The accurate estimation of the perforation length is very vital to improve fluid flow as well as the management of charges. Traditional methods, including empirical correlations, analytical models, and API 19B surface tests, suffer from significant limitations in their scope, require frequent recalibration, and fail to capture the complex physics governing shaped charge penetration. This study develops and validates machine learning models for perforation length prediction using a comprehensive dataset of 1648 API 19B standardized tests encompassing diverse gun configurations, explosive properties, and completion parameters. The dataset was partitioned into 1318 tests for model training and hyperparameter optimization, with 330 independent tests reserved for blind validation. Ten regression algorithms were systematically evaluated, with XGBoost demonstrating superior performance, achieving an R2 coefficient of 0.956 on blind validation. Feature importance analysis revealed explosive weight as the dominant predictor, followed by temperature rating. The application of machine learning models offers an accurate, easier, instantaneous during planning and design workflows, and cheaper way of estimation as compared to traditional methods. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics, 2nd Edition)
►▼ Show Figures

Figure 1

19 pages, 3857 KB  
Article
Aerodynamic Analysis and Design of a Sliding Drag Reduction System Using Graph Neural Networks
by Shinji Kajiwara and Cinto Ton
Fluids 2026, 11(2), 59; https://doi.org/10.3390/fluids11020059 - 22 Feb 2026
Viewed by 776
Abstract
To maximize competitive performance in motorsports, balancing high downforce for cornering with low drag for straight–line speed is essential. This paper presents the development and optimization of a sliding Drag Reduction System (DRS) integrated with a ducktail guide for a Student Formula racing [...] Read more.
To maximize competitive performance in motorsports, balancing high downforce for cornering with low drag for straight–line speed is essential. This paper presents the development and optimization of a sliding Drag Reduction System (DRS) integrated with a ducktail guide for a Student Formula racing car. To overcome the computational costs and time constraints of conventional CFD–based iterative design, a Graph Neural Network (GNN) surrogate model was developed to predict aerodynamic coefficients. Unlike traditional models, the GNN directly learns from the geometric graph structure of the multi–element wing, enabling near–instantaneous and highly accurate predictions. CFD results indicated that activating the DRS reduced drag from 82.68 N to 25.51 N, improving the lift–to–drag ratio from 1.67 to 2.67. The GNN surrogate model achieved an R2 value exceeding 0.99, demonstrating exceptional predictive fidelity compared to high–resolution simulations. Physical track testing with a Formula SAE vehicle corroborated these findings, showing a 4.6% improvement in 50 m acceleration and a 5.8% increase in maximum speed. This research establishes that GNN–based surrogate models can significantly accelerate the design and optimization of complex variable aerodynamic systems, providing a robust framework for performance enhancement in racing applications. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics, 2nd Edition)
►▼ Show Figures

Figure 1

35 pages, 10631 KB  
Article
Advancing CFD Simulations Through Machine-Learning-Enabled Mesh Refinement Analysis
by Charles Patrick Bounds and Mesbah Uddin
Fluids 2026, 11(2), 43; https://doi.org/10.3390/fluids11020043 - 30 Jan 2026
Viewed by 874
Abstract
As computational fluid dynamics (CFD) has become more mainstream in production engineering workflows, new demands have been introduced that require high-quality meshes to accurately capture the complex geometries. This evolution has created the need for mesh generation frameworks that help engineers design optimized [...] Read more.
As computational fluid dynamics (CFD) has become more mainstream in production engineering workflows, new demands have been introduced that require high-quality meshes to accurately capture the complex geometries. This evolution has created the need for mesh generation frameworks that help engineers design optimized meshing structures for each new geometry. However, many simulation workflows rely on the experience and intuition of senior engineers rather than systematic frameworks. In this paper, a novel technique for determining mesh convergence is created using machine learning (ML). This method seeks to provide process engineers with a visual feedback mechanism of flow regions that require mesh refinement. The work was accomplished by creating three grid sensitivity studies on various geometries: zero-pressure-gradient flat plate, bump in channel, and axisymmetric free jet. The cases were then simulated using the Reynolds Averaged Navier-Stokes (RANS) models in OpenFOAM (v2306) and had the ML method applied post-hoc using Python (v3.12.6). To apply the method to each case, the flow field was regionalized and clustered using an unsupervised ML model. The ML clustering results were then converted into a similarity score, which compares two grid levels to inform the user whether the region of the flow had converged. To prove this framework, the similarity scores were compared to flow field probes used to determine mesh convergence at key points in the flow. The method was found to be in agreement with the flow field probes on the level of mesh refinement that created convergence. The approach was also seen to provide refinement region recommendations in regions of the flow that align with human intuition of the physics of the flow. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics, 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 3203 KB  
Article
Machine Learning and Physics-Informed Neural Networks for Thermal Behavior Prediction in Porous TPMS Metals
by Mohammed Yahya and Mohamad Ziad Saghir
Fluids 2026, 11(2), 29; https://doi.org/10.3390/fluids11020029 - 23 Jan 2026
Cited by 1 | Viewed by 795
Abstract
Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experimentation is costly and full-scale simulations require extremely fine meshes to resolve the [...] Read more.
Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experimentation is costly and full-scale simulations require extremely fine meshes to resolve the complex geometry. This study develops a physics-informed neural network (PINN) that reconstructs steady-state temperature fields in TPMS Gyroid structures using only two experimentally measured materials, Aluminum and Silver, which were tested under identical heat flux and flow conditions. The model incorporates conductivity ratio physics, Fourier-based thermal scaling, and complete spatial temperature profiles directly into the learning process to maintain physical consistency. Validation using the complete Aluminum and Silver datasets confirms excellent agreement for Aluminum and strong accuracy for Silver despite its larger temperature gradients. Once trained, the PINN can generalize the learned behavior to nine additional metals using only their conductivity ratios, without requiring new experiments or numerical simulations. A detailed heat transfer analysis is also performed for Magnesium, a lightweight material that is increasingly considered for thermal management applications. Since no published TPMS measurements for Magnesium currently exist, the predicted Nusselt numbers obtained from the PINN-generated temperature fields represent the first model-based evaluation of its convective performance. The results demonstrate that the proposed PINN provides an efficient, accurate, and scalable surrogate model for predicting thermal behavior across multiple metallic TPMS structures and supports the design and selection of materials for advanced porous heat technologies. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop