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Heat Transfer Research

Publication de 18  numéros par an

ISSN Imprimer: 1064-2285

ISSN En ligne: 2162-6561

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.4 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00072 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.43 SJR: 0.318 SNIP: 0.568 CiteScore™:: 3.5 H-Index: 28

Indexed in

MIXED CONVECTION FLUID FLOW AND HEAT TRANSFER OF THE Al2O3−WATER NANOFLUID WITH VARIABLE PROPERTIES IN A CAVITY WITH AN INSIDE QUADRILATERAL OBSTACLE

Volume 46, Numéro 5, 2015, pp. 465-482
DOI: 10.1615/HeatTransRes.2015007328
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RÉSUMÉ

This investigation is focused on mixed convection fluid flow and heat transfer of Al2O3−water inside a square enclosure containing a hot rectangular obstacle at its bottom wall. The governing equations have been solved using the finite volume method. The SIMPLER algorithm was employed to couple the velocity and pressure fields. Utilizing the developed code, a parametric study was conducted and the impact of important parameters such as the solid volume fraction, Richardson number, size of the hot obstacle on the fluid flow and thermal fields and heat transfer inside the enclosure were investigated. The results show that for all Richardson numbers, the average Nusselt number increases with increase in the volume fraction of nanoparticles. Moreover, at all values of the Richardson number, heat transfer decreases when the height of the heated obstacle increases.

CITÉ PAR
  1. Rahmati Ahmad Reza, Rayat Roknabadi Ali, Abbaszadeh Mahmoud, Numerical simulation of mixed convection heat transfer of nanofluid in a double lid-driven cavity using lattice Boltzmann method, Alexandria Engineering Journal, 55, 4, 2016. Crossref

  2. Afrand Masoud, Nazari Najafabadi Karim, Sina Nima, Safaei Mohammad Reza, Kherbeet A.Sh., Wongwises Somchai, Dahari Mahidzal, Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network, International Communications in Heat and Mass Transfer, 76, 2016. Crossref

  3. Hemmat Esfe Mohammad, Afrand Masoud, Gharehkhani Samira, Rostamian Hadi, Toghraie Davood, Dahari Mahidzal, An experimental study on viscosity of alumina-engine oil: Effects of temperature and nanoparticles concentration, International Communications in Heat and Mass Transfer, 76, 2016. Crossref

  4. Esfandiary Meissam, Mehmandoust Babak, Karimipour Arash, Pakravan Hossein Ali, Natural convection of Al2O3–water nanofluid in an inclined enclosure with the effects of slip velocity mechanisms: Brownian motion and thermophoresis phenomenon, International Journal of Thermal Sciences, 105, 2016. Crossref

  5. Arabpour Abedin, Karimipour Arash, Toghraie Davood, The study of heat transfer and laminar flow of kerosene/multi-walled carbon nanotubes (MWCNTs) nanofluid in the microchannel heat sink with slip boundary condition, Journal of Thermal Analysis and Calorimetry, 131, 2, 2018. Crossref

  6. Sajeeb Ayamannil, Rajendrakumar Perikinalil Krishnan, Assessment of Viscosity of Coconut-Oil-Based CeO2/CuO Hybrid Nano-lubricant Using Artificial Neural Network, in Advanced Engineering Optimization Through Intelligent Techniques, 949, 2020. Crossref

  7. Khan Z. H., Khan W. A., Qasim M., Alharbi S. O., Hamid M., Du M., Hybrid nanofluid flow around a triangular-shaped obstacle inside a split lid-driven trapezoidal cavity, The European Physical Journal Special Topics, 231, 13-14, 2022. Crossref

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