Analysis and Integration of Micropolar Fluid Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 651

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, University of Peloponnese, Koukouli, 263 34 Patras, Greece
Interests: micropolar fluids; ferrofluids; biological flows; magnetohydrodynamics; instabilities; irreversible thermodynamics

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Guest Editor
Fluid Mechanics Laboratory, Department of Mechanical Engineering, University of the Peloponnese, Koukouli, 263 34 Patras, Greece
Interests: computational fluid mechanics; biomagnetic fluid dynamics; applied mathematics; ferrohydrodynamics; magnetohydrodynamics
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Special Issue Information

Dear Colleagues,

Any fluid with internal microstructure can be identified as a micropolar fluid. Such fluids consist of rigid, randomly oriented particles in a mother liquid carrier. Characteristic examples of micropolar fluids are colloidal suspensions, liquid crystals, ferrofluids, and blood. The applications of micropolar fluids in engineering and biomedicine are numerous, varying from friction reduction to heat transfer control. Moreover, external electric and magnetic fields can be applied on micropolar fluid flows, which extends the number of possible applications. This Special Issue on the “Analysis and Integration of Micropolar Fluid Systems” seeks high-quality works focusing on the latest novel advances regarding the modeling, simulation, optimization, and control of all kinds of micropolar fluids and systems. The topics within the scope of the issue include, but are not limited to, following:

  • Mechanics and dynamics of micropolar fluids.
  • Physical properties of micropolar fluids.
  • Heat and mass transfer mechanisms of micropolar fluid flows.
  • Electrohydrodynamics and magnetohydrodynamics of micropolar fluid flows.
  • Instabilities, energy cascade, and turbulent motion.
  • Simulation and experimental studies on all the above mentioned topics.

Dr. Kyriaki-Evangelia Aslani
Prof. Dr. Efstratios Tzirtzilakis
Guest Editors

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Keywords

  • micropolar fluid
  • microrotation
  • microstructure
  • non-Newtonian fluid
  • couple stress
  • complex fluid
  • colloidal fluid
  • ferrofluid
  • blood
  • nanofluid

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Published Papers (1 paper)

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Research

17 pages, 2341 KiB  
Article
A Machine Learning Framework for the Hydraulic Permeability of Fibrous Biomaterials with a Micropolar Bio-Fluid
by Nickolas D. Polychronopoulos, Evangelos Karvelas, Andrew Tsiantis and Thanasis D. Papathanasiou
Processes 2025, 13(6), 1840; https://doi.org/10.3390/pr13061840 - 11 Jun 2025
Viewed by 233
Abstract
Fibrous biomaterials are essential in biomedical engineering, tissue engineering, and filtration due to their specific transport and mechanical properties. Fluid flow through these materials is critical for their function. However, many biological fluids exhibit non-Newtonian behavior, characterized by micro-rotational effects, which traditional models [...] Read more.
Fibrous biomaterials are essential in biomedical engineering, tissue engineering, and filtration due to their specific transport and mechanical properties. Fluid flow through these materials is critical for their function. However, many biological fluids exhibit non-Newtonian behavior, characterized by micro-rotational effects, which traditional models often overlook. The current study presents a machine learning (ML) framework for the prediction and understanding of hydraulic permeability in fibrous biomaterials with a micropolar fluid. A dataset of 1000 numerical simulations was generated by varying the micropolar fluid properties and the fiber volume fraction in a periodic porous structure with nine parallel cylindrical fibers in a square lattice. Six powerful ML algorithms were deployed: Decision Trees (DT), Random Forests (RF), XGBoost, LightGBM, Support Vector Regression (SVR), and k-Nearest Neighbors (kNN). The balance of predictive capacity to unseen data values (tracking R2 values and error metrics) with computational efficiency for all algorithms was assessed. The best-performing ML algorithm was subsequently used to interpret the decisions made by the model using Shapley Additive exPlanations (SHAP) analysis and understand the role of feature importances. The SHAP findings highlight the potential of ML in capturing complex fluid interactions and guiding the design of advanced fibrous biomaterials with optimized hydraulic permeability. Full article
(This article belongs to the Special Issue Analysis and Integration of Micropolar Fluid Systems)
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