E
Ebrahim Al-Hajri
Researcher at Petroleum Institute
Publications - 21
Citations - 634
Ebrahim Al-Hajri is an academic researcher from Petroleum Institute. The author has contributed to research in topics: Heat exchanger & Heat transfer. The author has an hindex of 12, co-authored 20 publications receiving 404 citations. Previous affiliations of Ebrahim Al-Hajri include Khalifa University & University of Maryland, College Park.
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Single phase heat transfer and pressure drop analysis of a dimpled enhanced tube
TL;DR: In this paper, the performance of an enhanced tube was evaluated using experimental and numerical simulation techniques in a pipe-in-pipe heat exchanger, where steady state single phase (LPS) experiments were performed to determine Nusselt number and friction factor.
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Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization
Javad Mohebbi Najm Abad,Rasool Alizadeh,A. Fattahi,Mohammad Hossein Doranehgard,Ebrahim Al-Hajri,Nader Karimi,Nader Karimi +6 more
TL;DR: In this paper, the authors investigated heat and mass transfer in a hybrid nanofluid flow impinging upon a cylindrical bluff-body embedded in porous media and featuring homogenous and heterogeneous chemical reactions.
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Numerical modeling and thermal optimization of a single-phase flow manifold-microchannel plate heat exchanger
TL;DR: In this paper, a manifold-micro-channel plate heat exchanger was optimized to achieve superior heat transfer performance over the traditional chevron plate heat-exchange design, with a lower computational time than the full computational fluid dynamic (CFD) model.
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A Machine Learning Approach to Predicting the Heat Convection and Thermodynamics of an External Flow of Hybrid Nanofluid
Rasool Alizadeh,Javad Mohebbi Najm Abad,A. Fattahi,Mohamad Reza Mohebbi,Mohammad Hossein Doranehgard,Larry K.B. Li,Ebrahim Al-Hajri,Nader Karimi,Nader Karimi +8 more
TL;DR: In this article, an artificial neural network is used for predictive analysis, in which numerical data are generated to train an intelligence algorithm and to optimize the prediction errors, and the simulated and predicted data are subjected to particle swarm optimization to produce correlations for the shear stress and Nusselt number.
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Mass transfer characteristics of gas–liquid absorption during Taylor flow in mini/microchannel reactors
TL;DR: In this paper, a finite-element implementation of the phase field method was used to predict the hydrodynamics of the two-phase flow in mini/micro-channel reactors.