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Lubricants
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The Tribological Properties and Mathematical Analysis of Nanofluids
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The Tribological Properties and Mathematical Analysis of Nanofluids

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 9708

Special Issue Editors

Dr. Anum Shafiq

E-Mail Website
Guest Editor
1. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
2. Jiangsu International Joint Laboratory on System Modeling and Data Analysis, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: fluid flow with nanoparticles; computational fluid dynamics (CFD); machanical engineering; mathematical and computational methods in statistics; applied mathematics; fluid processing and heat transfer systems; groundwater modeling; heat and mass transfer; non-newtonian fluids; nonlinear analysis; series solutions of nonlinear problems; boundary value problems; differential system of equations; mathematical modeling; homotopy analysis method and its applications; response surface methodology; solutions of nonlinear differential equations; artificial neural network; sensitivity analysis; statistics; distribution theory; bayesian inference
Dr. Andaç Batur Çolak

E-Mail Website
Guest Editor
Mechanical Engineering Department, Niğde Ömer Halisdemir University, Niğde 51240, Turkey
Interests: artificial neural networks; energy; heat transfer; nanofluids; artificial intelligence; pipelines; oil and gas; fluid mechanics; engineering thermodynamics; thermal analysis; thermal properties; differential thermal analysis

Special Issue Information

Dear Colleagues,

Nanoscience is the study of manipulating or engineering matter, particles, and structures on the nanoscale scale, which is the scale of atoms and molecules. Nanotechnology is a technology that is used in nanoscience research to create custom-made materials and products with improved qualities, new types of smart medicines and sensors, new nanoelectronic components and brain structures, and even interfaces between electronic components and biological and molecular components as well as neural networks.

Machine learning methods are one of the engineering tools used in data prediction and prediction. Thanks to their powerful algorithms, they have s higher predictive ability compared to traditional mathematical modeling tools. Machine learning algorithms are widely used in many fields, including energy, medicine, manufacturing, finance, and economics.

In the context of nanoscience and nanotechnology, mathematical modelling, coding, or the simulation of nanomaterials and nanosized neural networks play an important role in the study of various physical, biological, and chemical properties. Thus, the applications of mathematics in nano- and neuroscience are gaining momentum as the mutual benefits of this collaboration become increasingly obvious.

The aim of this Special Issue is to investigate the optimization of nanomaterials, which are applied in many fields, including the field of energy, via modeling and simulations using various machine learning algorithms. The investigation of the usability of machine learning algorithms in various energy applications made with nano-sized materials and the simulation models that have been developed will play a leading role in both scientific research and in industry. We encourage the presentation of numerical and applied studies conducted using machine learning algorithms.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The thermophysical properties of nanofluids;
  • The modeling of single-phase and multi-phase nanofluid flows;
  • Energies of nanomaterials and neural networks;
  • Heat transfer phenomena of nanofluids;
  • The interaction between the biological organisms inside a cell;
  • Mathematical modeling in neural network calculation;
  • Mathematical calculations and neural networks;
  • Computational intelligence and mathematical models;
  • Neural computing, neural engineering, and artificial intelligence;
  • Neural control and neural networks analysis;
  • Modeling of single-phase and multi-phase nanofluid flows.

Dr. Anum Shafiq
Dr. Andaç Batur Çolak
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. Lubricants 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 2600 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

  • nanomaterials
  • neural networks
  • mathematical methods

Published Papers (6 papers)

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Research

15 pages, 4927 KiB  
Article
Significance of Temperature-Dependent Density on Dissipative and Reactive Flows of Nanofluid along Magnetically Driven Sheet and Applications in Machining and Lubrications
by Zia Ullah, Ahmad Hussain, Musaad S. Aldhabani, Nifeen H. Altaweel and Sana Shahab
Lubricants 2023, 11(9), 410; https://doi.org/10.3390/lubricants11090410 - 18 Sep 2023
Cited by 2 | Viewed by 947
Abstract
Nanofluid lubrication and machining are challenging and significant tasks in manufacturing industries that are used to control the removal of a material from a surface by using a cutting tool. The introduction of a nanofluid to the cutting zone provides cooling, lubricating, and [...] Read more.
Nanofluid lubrication and machining are challenging and significant tasks in manufacturing industries that are used to control the removal of a material from a surface by using a cutting tool. The introduction of a nanofluid to the cutting zone provides cooling, lubricating, and chip-cleaning benefits that improve machining productivity. A nanofluid is a cutting fluid that is able to remove excessive friction and heat generation. Chemical reactions and temperature-dependent density are essential in the thermal behavior of a nanofluid. The present study presents a careful inspection of the chemical reactions, temperature-dependent density, viscous dissipation, and thermophoresis during the heat and mass transfer of a nanofluid along a magnetically driven sheet. The physical attitude of viscous dissipation and the chemical reaction improvement rate in magneto-nanofluid flow is the primary focus of the present research. By applying the proper transformation, nonlinear partial differential expressions are introduced to the structure of the ordinary differential framework. The flow equations are simplified into nonlinear differential equations, and these equations are then computationally resolved via an efficient computational technique known as the Keller box technique. Flow factors like the Eckert number, reaction rate, density parameter, magnetic force parameter, thermophoretic number, buoyancy number, and Prandtl parameter governing the velocity, temperature distribution, and concentration distribution are evaluated prominently via tables and graphs. The novelty of the current study is in computing a heat transfer assessment of the magneto-nanofluid flow with chemical reactions and temperature-dependent density to remove excessive friction and heating in cutting zones. Nanofluids play significant roles in minimum quantity lubrication (MQL), enhanced oil recovery (EOR), drilling, brake oil, engine oil, water-miscible cutting fluids, cryogenic cutting fluids, controlled friction between tools and chips and tools and work, and conventional flood cooling during machining processes. Full article
(This article belongs to the Special Issue The Tribological Properties and Mathematical Analysis of Nanofluids)
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15 pages, 6262 KiB  
Article
Mathematical Analysis of Transverse Wall-Shearing Motion via Cross Flow of Nanofluid
by Faisal Z. Duraihem, Arif Ullah Khan, Salman Saleem and Shawana
Lubricants 2023, 11(3), 138; https://doi.org/10.3390/lubricants11030138 - 14 Mar 2023
Viewed by 855
Abstract
The investigation of nanofluid’s cross flow, which is caused by a nonlinear stretching sheet within the boundary layer, is presented. The proper mathematical detail is provided for three distinct cross flow instances with the streamwise flow. A uniform transverse stream located far above [...] Read more.
The investigation of nanofluid’s cross flow, which is caused by a nonlinear stretching sheet within the boundary layer, is presented. The proper mathematical detail is provided for three distinct cross flow instances with the streamwise flow. A uniform transverse stream located far above the stretched plate, in one instance, creates the cross flow. Two further situations deal with cross flows caused by surface transverse shearing motions. Weidman’s work was used to find a similarity solution by making the necessary changes. It has been found that two parameters, namely nanoparticle volume frictions ϕ and a nonlinear stretching parameter β, have a significant impact on the flow of fluids in cross flow scenarios. Graphical representations of transverse and streamwise shear stresses and velocity profiles are provided. From this study, we found that nanoparticle volume fraction ϕ reduces the momentum boundary layer in both streamwise and cross flow scenarios while increasing the temperature of the fluid and, hence, increasing thermal boundary layer thickness. The same is observed for the nonlinear stretching parameter β. Full article
(This article belongs to the Special Issue The Tribological Properties and Mathematical Analysis of Nanofluids)
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15 pages, 4217 KiB  
Article
Significance of Melting Heat Transfer and Brownian Motion on Flow of Powell–Eyring Fluid Conveying Nano-Sized Particles with Improved Energy Systems
by Hong Yang, Aaqib Majeed, Kamel Al-Khaled, Tasawar Abbas, Muhammad Naeem, Sami Ullah Khan and Munazza Saeed
Lubricants 2023, 11(1), 32; https://doi.org/10.3390/lubricants11010032 - 13 Jan 2023
Cited by 4 | Viewed by 1574
Abstract
The present study explores the characteristics of 2D MHD melting with reference to mass and heat transportation upon stagnation point Powell–Eyring nanofluid flow on an extensible surface. Melting is an important phenomenon that is involved in many procedures such as permafrost melting, solidification [...] Read more.
The present study explores the characteristics of 2D MHD melting with reference to mass and heat transportation upon stagnation point Powell–Eyring nanofluid flow on an extensible surface. Melting is an important phenomenon that is involved in many procedures such as permafrost melting, solidification of slag, defrosting frozen ground etc., all of which are examples of soil freezing and melting that involve heat trafficking through a coil in a grounded pump. A mathematical model is developed for the boundary layer flow. The differential equations are solved through a numerical algorithm which makes use of the boundary value problem solver bvp4c, applying MATLAB software. The numerical variations of embedded parameters on velocity lineation, temperature figuration, and concentration delineation are represented graphically, as are the width of the boundary layer value and the delineation rate for the increasing velocity parameter. The velocity function shows a decremental response for M while the opposite behavior is seen against the concentration field. Full article
(This article belongs to the Special Issue The Tribological Properties and Mathematical Analysis of Nanofluids)
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19 pages, 4563 KiB  
Article
Significance of Thermophoretic Particle Deposition, Arrhenius Activation Energy and Chemical Reaction on the Dynamics of Wall Jet Nanofluid Flow Subject to Lorentz Forces
by Umair Khan, Aurang Zaib, Anuar Ishak, Iskandar Waini, Zehba Raizah, Nattakan Boonsatit, Anuwat Jirawattanapanit and Ahmed M. Galal
Lubricants 2022, 10(10), 228; https://doi.org/10.3390/lubricants10100228 - 20 Sep 2022
Cited by 6 | Viewed by 1669
Abstract
The need for effective heating and cooling systems in the automotive, chemical, and aerospace industries is driving a rapid proliferation of heat-transfer technology. In recent times, GO (Graphene Oxide) has been emerging as one of the most promising nanoparticles because of its uninterrupted [...] Read more.
The need for effective heating and cooling systems in the automotive, chemical, and aerospace industries is driving a rapid proliferation of heat-transfer technology. In recent times, GO (Graphene Oxide) has been emerging as one of the most promising nanoparticles because of its uninterrupted behavior of electrical conductivity even at a minimum carrier concentration. Due to this incentive, the behavior of jet flow with heat and mass transfer features of electrically conducting based kerosene oil (KO) fluid dispensed by graphene nanoparticles was studied. In addition, the activation energy, irregular heat source/sink, thermophoretic particle deposition, and chemical reaction are also provoked. In order to provide numerical results, the boundary value problem of fourth-order (bvp4c) solver was used. The graphs were used to illustrate the effects of relevant parameters on the fluid flow, heat, and mass transfer rates. The incorporation of graphene nanoparticles significantly improves heat conductivity. Additionally, the nanoparticle volume fraction augments the temperature and concentration profile while the velocity profile declines. Moreover, the temperature enhances due to the heat source, whilst the contrary behavior is observed in the presence of the heat sink. Furthermore, the shear stress increases up to 12.3%, the Nusselt number increases up to 0.119%, and the Sherwood number increases up to 0.006% due to the presence of nanofluid. Finally, we can conclude that the latest work will be useful for thermal cooling systems, including cooling for engines and generators, nuclear systems, aviation refrigeration systems, and other systems. Full article
(This article belongs to the Special Issue The Tribological Properties and Mathematical Analysis of Nanofluids)
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16 pages, 1519 KiB  
Article
Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks
by Anum Shafiq, Andaç Batur Çolak and Tabassum Naz Sindhu
Lubricants 2022, 10(9), 209; https://doi.org/10.3390/lubricants10090209 - 30 Aug 2022
Cited by 12 | Viewed by 1547
Abstract
Nanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to [...] Read more.
Nanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to address this problem. The article deals with the bio-convective Walter’s B nanofluid with thermophoresis and Brownian diffusion through a cylindrical disk under artificial neural networks (ANNs). In addition, the thermal conductivity, radiation, and motile density of microorganisms are taken into consideration. The Buongiorno model is utilized to investigate the properties of nanofluids in motile microorganisms. By using appropriate similarity variables, a dimensionless system of a differential system is attained. The non-linear simplified system of equations has been numerically calculated via the Runge–Kutta fourth-order shooting process. The consequences of flow parameters on the velocity field, temperature distribution, species volumetric concentration, and microorganism fields are all addressed. Two distinct artificial neural network models were produced using numerical data, and their prediction performance was thoroughly examined. It is noted that according to the error histograms, the ANN model’s training phase has very little error. Furthermore, mean square error values calculated for local Nusselt number, local Sherwood number, and local motile density number, parameters were obtained as 3.58×10−3, 1.24×10−3, and 3.55×10−5, respectively. Both artificial neural network models can predict with high accuracy, according to the findings of the calculated performance parameters. Full article
(This article belongs to the Special Issue The Tribological Properties and Mathematical Analysis of Nanofluids)
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18 pages, 7565 KiB  
Article
Ramification of Hall and Mixed Convective Radiative Flow towards a Stagnation Point into the Motion of Water Conveying Alumina Nanoparticles Past a Flat Vertical Plate with a Convective Boundary Condition: The Case of Non-Newtonian Williamson Fluid
by Umair Khan, Aurang Zaib, Anuar Ishak, Iskandar Waini, El-Sayed M. Sherif, Nattakan Boonsatit, Ioan Pop and Anuwat Jirawattanapanit
Lubricants 2022, 10(8), 192; https://doi.org/10.3390/lubricants10080192 - 19 Aug 2022
Cited by 2 | Viewed by 1564
Abstract
Heat transfer technologies are experiencing rapid expansion as a result of the demand for efficient heating and cooling systems in the automotive, chemical, and aerospace industries. Therefore, the current study peruses an inspection of mixed convective radiative Williamson flow close to a stagnation [...] Read more.
Heat transfer technologies are experiencing rapid expansion as a result of the demand for efficient heating and cooling systems in the automotive, chemical, and aerospace industries. Therefore, the current study peruses an inspection of mixed convective radiative Williamson flow close to a stagnation point aggravated by a single nanoparticle (alumina) from a vertical flat plate with the impact of Hall. The convective heating of water conveying alumina (Al2O3) nanoparticles, as appropriate in engineering or industry, is investigated. Using pertinent similarity variables, the dominating equations are non-dimensionalized, and after that, via the bvp4c solver, they are numerically solved. We extensively explore the effects of many relevant parameters on axial velocity, transverse velocity, temperature profile, heat transfer, and drag force. In the opposing flow, there are two solutions seen; in the aiding flow, just one solution is found. In addition, the results designate that, due to nanofluid, the thickness of the velocity boundary layer decreases, and the thermal boundary layer width upsurges. The gradients for the branch of stable outcome escalate due to a higher Weissenberg parameter, while they decline for the branch of lower outcomes. Moreover, a magnetic field can be used to influence the flow and the properties of heat transfer. Full article
(This article belongs to the Special Issue The Tribological Properties and Mathematical Analysis of Nanofluids)
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