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Obai Younis

Bio: Obai Younis is an academic researcher from Salman bin Abdulaziz University. The author has contributed to research in topics: Materials science & Heat transfer. The author has an hindex of 8, co-authored 48 publications receiving 260 citations. Previous affiliations of Obai Younis include Taylors University & Rovira i Virgili University.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: In this article, a numerical investigation of the free convection of the Al2O3/water nanofluid was carried out in a square cavity, where a circular baffle with a radius of R and a temperature of Th was placed in the middle of the cavity.

82 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigate the thermal behavior of nano-encapsulated phase change material (NEPCM) suspensions in a cylindrical cavity and investigate the effect of the fusion temperature of the particle core (θf) on heat transfer.

64 citations

Journal ArticleDOI
TL;DR: In this paper , the authors investigated heat transmission and stable MHD (magneto-hydrodynamic) mixed convective flowing in a ventilated porous enclosed space with a heated elliptic inner cylinder filled with MWCNT (multi-wall carbon nanotube)/CMC (carboxymethylcellulose) nanofluid.

61 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented the results of a study conducted at the University of Skikda, Algeria, in the early 60s, where the Mechanical Engineering Department, College of Engineering, University of Babylon, Babylon City 51002, Hilla, Iraq, and the Department of Mechanical Engineering at Wadi Addwaser, Prince Sattam Bin Abdulaziz University, KSA.
Abstract: 1 Department of Mechanical Engineering, Faculty of Technology, University of 20 Août 1955 – Skikda, Skikda, Algeria 2 Department of Physics, Faculty of Sciences, University 20 août 1955 – Skikda, B.P 26 Route El-Hadaiek, Skikda 21000, Algeria 3 Mechanical Engineering Department, College of Engineering, University of Babylon, Babylon City 51002, Hilla, Iraq 4 Mechanical Engineering Department, College of Engineering, Hail University, 2240, Hail City, Saudi Arabia 5 Laboratoire de Métrologie et des Systèmes Énergétiques, École Nationale d’Ingénieurs, University of Monastir, 5000, Monastir, Tunisia 6 Department of Mechanical Engineering, College of Engineering at Wadi Addwaser, Prince Sattam Bin Abdulaziz University, 18311, KSA 7 Department of Mechanical Engineering, Faculty of Engineering, University of Khartoum, 11115, Sudan

53 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the irreversibility in convective nanofluid flow in the occurrence of a magnetic field (MHD) in a cavity with chamfers is calculated by numerical approach.
Abstract: The irreversibility in convective nanofluid flow in the occurrence of a magnetic field (MHD) in a cavity with chamfers is calculated by numerical approach. The nanofluid flow is considered under the impacts of magnetic field and thermal gradient. The continuity, motion and energy equations are solved by applying COMSOL Multiphysics computer package. The impacts of $$({\text{Ha}})$$ Hartmann number, $$(\gamma )$$ elevation of magnetic field, nanoparticle volume fraction, heat transmission and entropy analysis on the flow of nanofluid are discussed. Results reveal that, the impacts of volume fraction and the magnetic force on different irreversibility are significant. Moreover, results indicate the existence of a critical $$({\text{Ha}}_{{\text{c}}} )$$ Hartmann number this represents the frontier between the domains where the magnetic field dominates via its intrinsic effect and its extrinsic effect.

116 citations

Journal ArticleDOI
13 Jun 2022
TL;DR: In this paper , a review of machine learning techniques employed in the nanofluid-based renewable energy system, as well as new developments in machine learning research, is presented.
Abstract: Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

114 citations

Journal ArticleDOI
TL;DR: In this paper, a numerical study of MHD natural convection in an upright porous cylindrical annulus filled with magnetized nanomaterial is made by using the specificity of nanoliquids to improve the phenomenon of heat transport.

111 citations

Journal ArticleDOI
TL;DR: In this paper, the impact of Cattaneo-Christov model and convective boundary on second-grade nanofluid flow alongside a Riga pattern is investigated.
Abstract: Present communication aims to determine the impact of Cattaneo–Christov model and convective boundary on second-grade nanofluid flow alongside a Riga pattern. Zero mass flux is accounted at the solid surface of Riga pattern such that the fraction of nanoparticles maintains itself on strong retardation. The impact of Lorentz forces generated by Riga pate is also an important aspect of the study. The governing nonlinear problem is converted into ordinary problems via suitably adjusted transformations. Spectral local linearization method has been incorporated to find the solutions of the nonlinear problems. Variation in horizontal movement of the nanofluid, thermal distribution and concentration distribution of the nanoparticles has been noted for various fluid parameters. The results are plotted graphically. Outcomes indicate that the horizontal movement gains enhancement for elevated values of modified Hartman factor. Thermal state of the nanofluid and concentration of nanoparticles receive reduction for incremental values of relaxation time parameters. Numerical results for skin friction and heat flux have been reported in tabular form. The CPU run time and residual error are obtained to check the efficiency of the method used for finding the solution.

109 citations

Journal ArticleDOI
TL;DR: In this paper, a sort of hybrid nanofluid comprising nano-size materials through an ethylene glycol as a regular liquid is modeled to expand the magnetic impact on the mixed convection flow through a shrinking/stretched wedge.
Abstract: Hybrid nanoliquid as an expansion of nanoliquid is acquired by scattering combination of nano-powder or numerous distinct nanomaterials in the regular liquid. Hybrid nanofluids are impeding fluids which furnish better performance of heat transport and thermo-physical properties than convectional heat transport fluids (ethylene glycol, water and oil) and nanofluids with single material. At this juncture, a sort of hybrid nanofluid comprising nano-size materials through an ethylene glycol as a regular liquid is modeled to expand the magnetic impact on the mixed convection flow through a shrinking/stretched wedge. The impacts of Joule heating and viscous dissipation are also revealed. The PDEs which governed the flow problem with heat transport are changed into a dimensionless ODEs system through a similarity technique. Then these equations are numerically exercised by utilizing bvp4c solver. The impact of emerging constraints on the flow field with heat transport is discussed with the aid of plots. Also, the stability analysis is implemented to classify which result is physically reliable and stable.

99 citations