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Journals
Processes
Special Issues
Recent Computational Aspect of Nanofluids and Heat Transfer
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Recent Computational Aspect of Nanofluids and Heat Transfer

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 2001

Special Issue Editors

Dr. Mohamed F. El-Amin

E-Mail Website
Guest Editor
Mathematical and Natural Sciences Department, College of Engineering, Effat University, Jeddah 21478, Saudi Arabia
Interests: nanofluids; nanoparticles; heat and mass transfer; fluid flow; magnetic field; modeling and simulation; porous media; reservoir simulation; machine learning; computational fluid dynamics; multiphase flow; lattice Boltzmann methods; hydrogen energy safety; turbulent flow; heat storage; boundary layer theory; magnetohydrodynamics; micropolar fluids; non-Newtonian fluids; harvesting atmospheric water
Special Issues, Collections and Topics in MDPI journals
Dr. Osama Eljamal

E-Mail Website
Guest Editor
Environmental Engineering Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 819-0935, Japan
Interests: environmental engineering; nanotechnology; microbial fuel cells; renewable energy; water and wastewater treatment

Special Issue Information

Dear Colleagues,

The previous two decades have witnessed a significant scientific and industrial revolution in the field of nanotechnology, which has unquestionably impacted all scientific disciplines and applications. The current Special Issue focuses on developing the computational aspects of nanofluid flow associated with heat transfer. It targets problem modeling, numerical analysis, algorithms and techniques, and conducts an analysis of the properties of the heat-transfer process in nanofluids. Moreover, it asseses the use of machine and deep learning methods in the heat-transfer process in nanofluids.

This Special Issue on the “Recent Computational Aspect of Nanofluids and Heat Transfer” invites submissions of advanced research that focuses on the latest novel advances using both numerical and machine learning techniques in relation to nanofluid flow.

The topics include, but are not limited to, the following areas:

  • Modeling and simulation of nanofluid flow with heat transfer;
  • New numerical algorithms for nanofluid flow with heat transfer;
  • Heat and mass transfer in nanofluid dynamics;
  • Stability analysis of heat transfer in nanofluids;
  • Error analysis and solution properties of heat transfer in nanofluids;
  • Interaction of nanofluids with magnetic field;
  • Thermodynamics aspect of heat transfer in nanofluids;
  • Machine/deep learning techniques for nanofluid dynamics;
  • Physical/mathematical aspects of nanofluid flow and heat transfer;
  • Multiscale modeling of nanofluid dynamics.

Dr. Mohamed F. El-Amin
Dr. Osama Eljamal
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. Processes 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 2400 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

  • nanofluids
  • nanoparticles
  • heat and mass transfer
  • porous media
  • modeling and simulation
  • reservoir simulation
  • machine learning
  • deep learning
  • numerical analysis
  • computational fluid dynamics
  • multiphase flow
  • lattice Boltzmann methods
  • turbulent flow
  • heat storage
  • boundary layer theory
  • magnetohydrodynamics
  • micropolar fluids
  • non-Newtonian fluids
  • harvesting atmospheric water

Published Papers (2 papers)

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Research

20 pages, 3779 KiB  
Article
An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence
by Amani S. Baazeem, Muhammad Shoaib Arif and Kamaleldin Abodayeh
Processes 2023, 11(9), 2736; https://doi.org/10.3390/pr11092736 - 13 Sep 2023
Cited by 3 | Viewed by 833
Abstract
Engineering and technological research groups are becoming interested in neural network techniques to improve productivity, business strategies, and societal development. In this paper, an explicit numerical scheme is given for both linear and nonlinear differential equations. The scheme is correct to second order. [...] Read more.
Engineering and technological research groups are becoming interested in neural network techniques to improve productivity, business strategies, and societal development. In this paper, an explicit numerical scheme is given for both linear and nonlinear differential equations. The scheme is correct to second order. Additionally, the scheme’s consistency and stability are guaranteed. Backpropagation of Levenberg–Marquardt, the effect of including an induced magnetic field in a mathematical model for electrical boundary layer nanofluid flow on a flat plate, is quantitatively investigated using artificial neural networks. Later, the model is reduced into a set of boundary value problems, which are then resolved using the suggested scheme and a shooting strategy. The outcomes are also contrasted with earlier studies and the MATLAB solver bvp4c for validation purposes. In addition, neural networking is also employed for mapping input to outputs for velocity, temperature, and concentration profiles. These results prove that artificial neural networks can make precise forecasts and optimizations. Using a neural network to optimize the fluid flow in an electrical boundary layer while subjected to an induced magnetic field is a promising application of the suggested computational scheme. Fluid dynamics benefits greatly from combining numerical methods and artificial neural networks, which could lead to new developments in various fields. Results from this study may aid in optimizing fluid systems, leading to greater productivity and effectiveness in numerous technical fields. Full article
(This article belongs to the Special Issue Recent Computational Aspect of Nanofluids and Heat Transfer)
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14 pages, 1971 KiB  
Communication
Multi-Point Flux MFE Decoupled Method for Compressible Miscible Displacement Problem
by Wenwen Xu, Hong Guo, Xindong Li and Yongqiang Ren
Processes 2023, 11(4), 1244; https://doi.org/10.3390/pr11041244 - 18 Apr 2023
Viewed by 725
Abstract
In this paper, a multi-point flux mixed-finite-element decoupled method was considered for the compressible miscible displacement problem. For this compressible problem, a fully discrete backward Euler scheme was proposed, in which the velocity and pressure equations were decoupled by a multi-point flux MFE [...] Read more.
In this paper, a multi-point flux mixed-finite-element decoupled method was considered for the compressible miscible displacement problem. For this compressible problem, a fully discrete backward Euler scheme was proposed, in which the velocity and pressure equations were decoupled by a multi-point flux MFE method using BDM1 elements combined with a trapezoidal quadrature rule. The concentration equation was handled by a standard FE method. The error analysis for velocity, pressure, and concentration were rigorously derived. Numerical experiments to verify the convergence rates and simulate the miscible displacement problem of a water–oil system were presented. Full article
(This article belongs to the Special Issue Recent Computational Aspect of Nanofluids and Heat Transfer)
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