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Arash Karimipour
Researcher at Islamic Azad University
Publications - 313
Citations - 14751
Arash Karimipour is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Nanofluid & Heat transfer. The author has an hindex of 65, co-authored 257 publications receiving 10833 citations. Previous affiliations of Arash Karimipour include Virginia Tech College of Natural Resources and Environment & University of Sistan and Baluchestan.
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Journal Article
Simulation of Fluid Flow and Heat Transfer in Inclined Cavity using Lattice Boltzmann Method
TL;DR: In this article, the effects of the variations of Richardson number and inclination angle on the thermal and flow behavior of the fluid inside the cavity are investigated, and a computer program is developed to simulate this problem using BGK model of lattice Boltzmann method.
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Development of the ANN–KIM composed model to predict the nanofluid energetic thermal conductivity via various types of nano-powders dispersed in oil
Xueqing Mei,Zhixiong Li,Seyed Amin Bagherzadeh,Aliakbar Karimipour,Mehrdad Bahrami,Arash Karimipour +5 more
TL;DR: Artificial neural network/kriging interpolation method optimization method which is evaluated concerned the hybrid nanofluid composed of iron oxide (Fe2O3) and aluminum oxide (Al2O 3) nano-powders to improve the thermal properties of 10w40 engine oil at different amounts of volume fraction and temperature as discussed by the authors.
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Improve the efficiency and heat transfer rate’ trend prediction of a flat-plate solar collector via a solar energy installation by examine the Titanium Dioxide/Silicon Dioxide-water nanofluid
Nidal H. Abu-Hamdeh,Mashhour A. Alazwari,Elias M. Salilih,S. Mohammad Sajadi,S. Mohammad Sajadi,Arash Karimipour +5 more
TL;DR: In this article, the thermal conductivity of the nanofluid (at 1.0 to 4.0 Wt%) was measured via hot-transient technique (at 25 to 50 Wt) and its efficiency enhancement was measured.
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Fluid flow and heat transfer of the two-phase solid/liquid mixture at the equilibration phase structure via MD method: Atomic value effects in a case study of energy consumption and absorbed energy
TL;DR: In this paper, the authors describe the atomic behavior of Ar atoms in the presence of Cu, Fe, and Cu/Fe nanoparticles via the molecular dynamics (MD) approach.
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Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/liquid paraffin mixture properties
Xiaoluan Zhang,Xinni Liu,Xifeng Wang,Shahab S. Band,Seyed Amin Bagherzadeh,Somaye Taherifar,AliAkbar Moussavi Abdollahi,Mehrdad Bahrami,Arash Karimipour,Kwok Wing Chau,Amirhosein Mosavi +10 more
TL;DR: To evaluate the best optimization methods of nanofluid viscosity, Multi-Layer Feed forward (MLF), Radial Basis Function (RBF), and RLSF are compared and discussed and the ExtremeCum model showed the least margin of error and can be employed to predict the data.