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Fluids
Special Issues
Feature Reviews for Fluids 2025–2026
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Feature Reviews for Fluids 2025–2026

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4051

Special Issue Editors

Prof. Dr. Giuliano De Stefano

E-Mail Website
Guest Editor
Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa, Italy
Interests: computational fluid dynamics; turbulence modelling and simulation; large-eddy simulation; wavelets and fluids
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. D. Andrew S. Rees

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK
Interests: convection; porous media; instability; numerical simulation; asymptotic analysis; non-Newtonian fluids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue, entitled “Feature Reviews for Fluids 2025–2026”. This Special Issue aims to present a collection of high-quality review papers from our Editorial Board Members or outside leading authors, discussing a diverse set of topics related to all aspects of fluids, including original theoretical, computational, and experimental contributions to understanding of the dynamics of gases, liquids, and complex or multiphase fluids.

We consider this Special Issue to be the best forum to disseminate important research findings and share innovative ideas in the field.

Prof. Dr. Giuliano De Stefano
Prof. Dr. D. Andrew S. Rees
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

  • artificial intelligence in fluid mechanics
  • biofluid mechanics
  • coherent vortical structures in fluids
  • marine hydrodynamics
  • multiphase flows
  • shock waves
  • turbulence modelling and simulation
  • wind turbine aerodynamics
  • stability theory in fluid mechanics
  • geophysical fluid dynamics
  • granular/suspension flows
  • heat and mass transfer
  • magneto-hydrodynamics (MHD)
  • nanofluids and microfluids
  • Newtonian and non-Newtonian fluids
  • polymers
  • rheology
  • tribology/lubrication
  • computational fluid dynamics (CFD)
  • biomedical flows
  • experiments in fluids
  • fluid–structure interaction
  • data-driven fluids research
  • culinary fluid mechanics

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.

Published Papers (3 papers)

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Review

28 pages, 10432 KiB  
Review
Rapid CFD Prediction Based on Machine Learning Surrogate Model in Built Environment: A Review
by Rui Mao, Yuer Lan, Linfeng Liang, Tao Yu, Minhao Mu, Wenjun Leng and Zhengwei Long
Fluids 2025, 10(8), 193; https://doi.org/10.3390/fluids10080193 - 28 Jul 2025
Viewed by 818
Abstract
Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. [...] Read more.
Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article
(This article belongs to the Special Issue Feature Reviews for Fluids 2025–2026)
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17 pages, 327 KiB  
Review
Renormalization Group and Effective Field Theories in Magnetohydrodynamics
by Amir Jafari
Fluids 2025, 10(8), 188; https://doi.org/10.3390/fluids10080188 - 23 Jul 2025
Viewed by 298
Abstract
We briefly review the recent developments in magnetohydrodynamics, which in particular deal with the evolution of magnetic fields in turbulent plasmas. We especially emphasize (i) the necessity and utility of renormalizing equations of motion in turbulence where velocity and magnetic fields become Hölder [...] Read more.
We briefly review the recent developments in magnetohydrodynamics, which in particular deal with the evolution of magnetic fields in turbulent plasmas. We especially emphasize (i) the necessity and utility of renormalizing equations of motion in turbulence where velocity and magnetic fields become Hölder singular; (ii) the breakdown of Laplacian determinism of classical physics (spontaneous stochasticity or super chaos) in turbulence; and (iii) the possibility of eliminating the notion of magnetic field lines in magnetized plasmas, using instead magnetic path lines as trajectories of Alfvénic wave packets. These methodologies are then exemplified with their application to the problem of magnetic reconnection—rapid change in magnetic field pattern that accelerates plasma—a ubiquitous phenomenon in astrophysics and laboratory plasmas. Renormalizing rough velocity and magnetic fields on any finite scale l in turbulence inertial range, to remove singularities, implies that magnetohydrodynamic equations should be regarded as effective field theories with running parameters depending upon the scale l. A high wave-number cut-off should also be introduced in fluctuating equations of motion, e.g., Navier–Stokes, which makes them effective, low-wave-number field theories rather than stochastic differential equations. Full article
(This article belongs to the Special Issue Feature Reviews for Fluids 2025–2026)
19 pages, 3372 KiB  
Review
A Comprehensive Review of Biomass Gasification Characteristics in Fluidized Bed Reactors: Progress, Challenges, and Future Directions
by Lu Wang, Tuo Zhou, Bo Hou, Hairui Yang, Nan Hu and Man Zhang
Fluids 2025, 10(6), 147; https://doi.org/10.3390/fluids10060147 - 1 Jun 2025
Cited by 2 | Viewed by 2514
Abstract
Biomass fluidized bed gasification technology has attracted significant attention due to its high efficiency and clean energy conversion capabilities. However, its industrial application has been limited by insufficient technological maturity. This paper systematically reviews the research progress on biomass fluidized bed gasification characteristics; [...] Read more.
Biomass fluidized bed gasification technology has attracted significant attention due to its high efficiency and clean energy conversion capabilities. However, its industrial application has been limited by insufficient technological maturity. This paper systematically reviews the research progress on biomass fluidized bed gasification characteristics; compares the applicability of bubbling fluidized beds (BFBs), circulating fluidized beds (CFBs), and dual fluidized beds (DFBs); and highlights the comprehensive advantages of CFBs in large-scale production and tar control. The gas–solid flow characteristics within CFB reactors are highly complex, with factors such as fluidization velocity, gas–solid mixing homogeneity, gas residence time, and particle size distribution directly affecting syngas composition. However, experimental studies have predominantly focused on small-scale setups, failing to characterize the impact of flow dynamics on gasification reactions. Therefore, numerical simulation has become essential for in-depth exploration. Additionally, this study analyzes the influence of different gasification agents (air, oxygen-enriched, oxygen–steam, etc.) on syngas quality. The results demonstrate that oxygen–steam gasification eliminates nitrogen dilution, optimizes reaction kinetics, and significantly enhances syngas quality and hydrogen yield, providing favorable conditions for downstream processes such as green methanol synthesis. Based on the current research landscape, this paper employs numerical simulation to investigate oxygen–steam CFB gasification at a pilot scale (500 kg/h biomass throughput). The results reveal that under conditions of O2/H2O = 0.25 and 800 °C, the syngas H2 volume fraction reaches 43.7%, with a carbon conversion rate exceeding 90%. These findings provide theoretical support for the industrial application of oxygen–steam CFB gasification technology. Full article
(This article belongs to the Special Issue Feature Reviews for Fluids 2025–2026)
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