Items where Author is "Hekmatifar, M."

Article

An, Qing and Basem, Ali and Alizadeh, As'ad and Kamoon, Saeed S. and AL-Yasiri, Mortatha and Zhang, Mengyan and Li, Z. and Salahshour, Soheil and Hekmatifar, M. (2024) The Pool Boiling Heat Transfer of Ammonia/Fe3O4 nano-Refrigerant in the Presence of External Magnetic Field and Heat flux: A Molecular Dynamics Approach. International Journal of Heat and Mass Transfer, 227. p. 125589. ISSN 00179310

Wang, Ke and Jasim, Dheyaa J. and Alizadeh, As'ad and Al-Rubaye, Ameer H. and Nasajpour-Esfahani, Navid and Salahshour, Soheil and Esmaeili, Shadi and Hekmatifar, M. (2023) Investigating the Effect of External Heat Flux on the Thermal Behaviour of Hybrid Paraffin-air Heat sink: A Molecular Dynamics Approach. Case Studies in Thermal Engineering, 52. ISSN 2214157X

Daneshmand, Saeed and Vini, Mohammad Heydari and Sajadi, S. Mohammad and Mouthanna, Ahmed and Jasim, Dheyaa J. and Hammoodi, Karrar A. and Hekmatifar, M. and Nasajpour-Esfahani, Navid (2023) Numerical and Experimental Investigations of Mechanical, Tribological, and Electrical Properties of Laminated Bi-Metal Al/Sic/Ni Composites. Materials Today Communications, 37: 107355. ISSN 23524928

Heydari Vini, Mohammad and Jasim, Dheyaa J. and Sajadi, S. Mohammad and Daneshmand, Saeed and Al-Shati, Ahmed Salah and Nasajpour-Esfahani, Navid and Hekmatifar, M. (2023) Tribological Characterization of Laminated Hybrid AA1050/Tic/Graphite Composite Bars. Journal of Materials Research and Technology, 26. pp. 7594-7604. ISSN 22387854

Daneshmand, Saeed and Vini, Mohammad Heydari and Sajadi, S. Mohammad and Jasim, Dheyaa J. and Nasajpour-Esfahani, Navid and Al-Shati, Ahmed Salah and Hekmatifar, M. (2023) Effect of Interlayer Particles on the Bonding Improvement of Aluminum Laminated Composites. Materials Today Communications, 36. p. 106754. ISSN 23524928

Milyani, Ahmad H. and Karimi, Maryam and Alizadeh, As'ad and Nasajpour-Esfahani, Navid and Abu-Hamdeh, Nidal H. and Hekmatifar, M. and Shamsborhan, Mahmoud (2023) Utilizing Different Machine Learning Methods to Accurately Predict Density, Temperature, Velocity, and Thermal Conductivity of Hydrophilic, Hydrophobic, and Compound materials. Journal of Molecular Liquids, 387. p. 122625. ISSN 01677322