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Fluids
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Machine Learning and Artificial Intelligence in Fluid Mechanics
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Machine Learning and Artificial Intelligence in Fluid Mechanics

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 16153

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 during the past few years, towards the direction of exploiting massive amounts of data generated from knowledge gathered insofar, either from experimental measurements or simulations. The application of novel machine learning (ML) techniques is currently the latest trend in the field, and has almost reached standardization. Computational boosting, advanced turbulence modeling, bridging among scales, hybrid simulation schemes, and flow feature extraction are concepts that scientists and engineers must deal with.

This Special Issue aims to join together data science methods and advanced artificial intelligence and machine learning techniques, in order 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, 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 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

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

Published Papers (6 papers)

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Research

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14 pages, 12236 KiB  
Article
Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids
by Abulhassan Ali, Nawal Noshad, Abhishek Kumar, Suhaib Umer Ilyas, Patrick E. Phelan, Mustafa Alsaady, Rizwan Nasir and Yuying Yan
Fluids 2024, 9(1), 20; https://doi.org/10.3390/fluids9010020 - 09 Jan 2024
Viewed by 1633
Abstract
The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this [...] Read more.
The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s−1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics)
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20 pages, 3810 KiB  
Article
Physics-Informed Super-Resolution of Turbulent Channel Flows via Three-Dimensional Generative Adversarial Networks
by Nicholas J. Ward
Fluids 2023, 8(7), 195; https://doi.org/10.3390/fluids8070195 - 29 Jun 2023
Cited by 1 | Viewed by 1254
Abstract
For a few decades, machine learning has been extensively utilized for turbulence research. The goal of this work is to investigate the reconstruction of turbulence from minimal or lower-resolution datasets as inputs using reduced-order models. This work seeks to effectively reconstruct high-resolution 3D [...] Read more.
For a few decades, machine learning has been extensively utilized for turbulence research. The goal of this work is to investigate the reconstruction of turbulence from minimal or lower-resolution datasets as inputs using reduced-order models. This work seeks to effectively reconstruct high-resolution 3D turbulent flow fields using unsupervised physics-informed deep learning. The first objective of this study is to reconstruct turbulent channel flow fields and verify these with respect to the statistics. The second objective is to compare the turbulent flow structures generated from a GAN with a DNS. The proposed deep learning algorithm effectively replicated the first- and second-order statistics of turbulent channel flows of Reτ= 180 within a 2% and 5% error, respectively. Additionally, by incorporating physics-based corrections to the loss functions, the proposed algorithm was also able to reconstruct λ2 structures. The results suggest that the proposed algorithm can be useful for reconstructing a range of 3D turbulent flows given computational and experimental efforts. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics)
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15 pages, 10777 KiB  
Article
DropletAI: Deep Learning-Based Classification of Fluids with Different Ohnesorge Numbers during Non-Contact Dispensing
by Pranshul Sardana, Mohammadreza Zolfaghari, Guilherme Miotto, Roland Zengerle, Thomas Brox, Peter Koltay and Sabrina Kartmann
Fluids 2023, 8(6), 183; https://doi.org/10.3390/fluids8060183 - 17 Jun 2023
Viewed by 1201
Abstract
The reliable non-contact dispensing of droplets in the pico- to microliter range is a challenging task. The dispensed drop volume depends on various factors such as the rheological properties of the liquids, the actuation parameters, the geometry of the dispenser, and the ambient [...] Read more.
The reliable non-contact dispensing of droplets in the pico- to microliter range is a challenging task. The dispensed drop volume depends on various factors such as the rheological properties of the liquids, the actuation parameters, the geometry of the dispenser, and the ambient conditions. Conventionally, the rheological properties are characterized via a rheometer, but this adds a large liquid overhead. Fluids with different Ohnesorge number values produce different spatiotemporal motion patterns during dispensing. Once the Ohnesorge number is known, the ratio of viscosity and surface tension of the liquid can be known. However, there exists no mathematical formulation to extract the Ohnesorge number values from these motion patterns. Convolutional neural networks (CNNs) are great tools for extracting information from spatial and spatiotemporal data. The current study compares seven different CNN architectures to classify five liquids with different Ohnesorge numbers. Next, this work compares the results of various data cleaning conditions, sampling strategies, and the amount of data used for training. The best-performing model was based on the ECOmini-18 architecture. It reached a test accuracy of 94.2% after training on two acquisition batches (a total of 12,000 data points). Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics)
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17 pages, 7474 KiB  
Article
Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow
by Fabian Pioch, Jan Hauke Harmening, Andreas Maximilian Müller, Franz-Josef Peitzmann, Dieter Schramm and Ould el Moctar
Fluids 2023, 8(2), 43; https://doi.org/10.3390/fluids8020043 - 26 Jan 2023
Cited by 9 | Viewed by 3807
Abstract
Physics-informed neural networks (PINN) can be used to predict flow fields with a minimum of simulated or measured training data. As most technical flows are turbulent, PINNs based on the Reynolds-averaged Navier–Stokes (RANS) equations incorporating a turbulence model are needed. Several studies demonstrated [...] Read more.
Physics-informed neural networks (PINN) can be used to predict flow fields with a minimum of simulated or measured training data. As most technical flows are turbulent, PINNs based on the Reynolds-averaged Navier–Stokes (RANS) equations incorporating a turbulence model are needed. Several studies demonstrated the capability of PINNs to solve the Naver–Stokes equations for laminar flows. However, little work has been published concerning the application of PINNs to solve the RANS equations for turbulent flows. This study applied a RANS-based PINN approach to a backward-facing step flow at a Reynolds number of 5100. The standard k-ω model, the mixing length model, an equation-free νt and an equation-free pseudo-Reynolds stress model were applied. The results compared favorably to DNS data when provided with three vertical lines of labeled training data. For five lines of training data, all models predicted the separated shear layer and the associated vortex more accurately. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics)
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15 pages, 2162 KiB  
Article
The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches
by Theodoros E. Karakasidis, Filippos Sofos and Christos Tsonos
Fluids 2022, 7(10), 321; https://doi.org/10.3390/fluids7100321 - 05 Oct 2022
Cited by 11 | Viewed by 2586
Abstract
In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as [...] Read more.
In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics)
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Review

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16 pages, 1167 KiB  
Review
Can Artificial Intelligence Accelerate Fluid Mechanics Research?
by Dimitris Drikakis and Filippos Sofos
Fluids 2023, 8(7), 212; https://doi.org/10.3390/fluids8070212 - 19 Jul 2023
Cited by 11 | Viewed by 4221
Abstract
The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive [...] Read more.
The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Fluid Mechanics)
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