Special Issues
Table of Content

Computational Advances in Nanofluids: Modelling, Simulations, and Applications

Submission Deadline: 30 June 2026 View: 1126 Submit to Special Issue

Guest Editors

Assoc. Prof. Dharmendra Tripathi

Email: dtripathi@nituk.ac.in

Affiliation: Department of Mathematics, National Institute of Technology Uttarakhand, Srinagar, 246174, India

Homepage:

Research Interests: biofluid mechanics, mathematical modelling

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Dr. Rajashekhar V. Choudhari

Email: rv.choudhari@manipal.edu

Affiliation: Department of Mathematics, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, 576104, India

Homepage:

Research Interests: fluid mechanics, biomechanics, mathematical modelling, MHD, convection flows, thermal analysis, energy efficiency, numerical analysis, fluid flow and transport phenomenon, nanofluids, microfluidics, computation fluid dynamics, targeting drug delivery

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Summary

Nanofluids are engineered colloidal suspensions of nanoparticles in base fluids, exhibiting enhanced thermal, optical, and rheological properties. They find applications in heat transfer, energy systems, biomedical fields, and nanotechnology.


The special issue aims to advance understanding and showcase recent developments in computational modelling and simulation of nanofluids, focusing on their thermal performance, applications, and innovative techniques. This special issue will bridge computational sciences, nanofluid physics, and engineering applications, and showcase numerical methods, CFD techniques, and machine learning approaches for nanofluid simulations.


The scope encompasses computational aspects, nanofluid characteristics, and diverse applications, focusing on advancing modelling and utilization of nanofluids. The special issue will cover key areas including:
Computational Modelling & Simulations
CFD techniques: Finite volume, finite element methods for nanofluid flow.
Multiscale modelling: Coupling macroscopic fluid dynamics with nanoparticle interactions.
Machine learning: AI/ML for predicting nanofluid properties, optimizing systems.


Nanofluid Properties & Behaviour
· Thermal conductivity: Enhancement mechanisms, models.
· Viscosity & rheology: Non-Newtonian behaviour, shear effects.
· Stability & dispersion: Challenges of nanoparticle agglomeration.


Applications
· Energy systems: Solar collectors, heat exchangers, thermal storage.
· Biomedical: Drug delivery, hyperthermia therapy, imaging.
· Industrial processes: Cooling, lubrication, machining.


Flow & Heat Transfer Phenomena
· Convection: Forced, natural, mixed convection studies.
· MHD (Magnetohydrodynamics): Nanofluids in magnetic fields.
· Phase change: Boiling, condensation aspects.


Hybrid & Advanced Nanofluids
· Hybrid nanofluids: Combinations of nanoparticles for synergistic effects.
· Ternary nanofluids: Multiple nanoparticle systems.


Keywords

CFD (Computational Fluid Dynamics), nanofluids, thermal conductivity, heat transfer; energy systems, nanotechnology

Published Papers


  • Open Access

    ARTICLE

    AI-Assisted Hybrid Solver for Skin Friction and Sherwood Number Prediction in Eyring–Prandtl Nanofluid Flow over a Riga Plate

    Yasir Nawaz, Nabil Kerdid, Muhammad Shoaib Arif, Mairaj Bibi
    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077616
    (This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
    Abstract A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order time derivatives. The proposed scheme attains third-order temporal accuracy and is rigorously validated through stability and convergence analyses for both scalar and coupled systems. Its effectiveness is demonstrated by simulating unsteady Eyring-Prandtl non-Newtonian nanofluid flow over a Riga plate with coupled heat and mass transfer under electromagnetic actuation. The physical model accounts for Brownian motion and thermophoresis, and the nanofluid considered is a Prandtl-type non-Newtonian base fluid… More >

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