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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A unique thermal conductivity model (ANN) for nanofluid based on experimental study
Ashutosh Pare, Subrata Kumar Ghosh
Powder Technology (2020) Vol. 377, pp. 429-438
Closed Access | Times Cited: 62

Showing 26-50 of 62 citing articles:

Surface qualitative analysis and ANN modelling for pool boiling heat transfer using Al2O3-water based nanofluids
Ashutosh Pare, Subrata Kumar Ghosh
Colloids and Surfaces A Physicochemical and Engineering Aspects (2020) Vol. 610, pp. 125926-125926
Closed Access | Times Cited: 36
Knacks of neuro-computing to study the unsteady squeezed flow of MHD carbon nanotube with entropy generation
Muhammad Shoaib, Kottakkaran Sooppy Nisar, Muhammad Asif Zahoor Raja, et al.
International Communications in Heat and Mass Transfer (2022) Vol. 135, pp. 106140-106140
Closed Access | Times Cited: 21
Determining the optimal structure for accurate estimation of the dynamic viscosity of oil-based hybrid nanofluid containing MgO and MWCNTs nanoparticles using multilayer perceptron neural networks with Levenberg-Marquardt Algorithm
Mohammad Hemmat Esfe, Fatemeh Amoozadkhalili, Davood Toghraie
Powder Technology (2022) Vol. 415, pp. 118085-118085
Closed Access | Times Cited: 20
Statistical and artificial neural network technique for prediction of performance in AlSi10Mg-MWCNT based composite materials
Santosh Kumar, Priyadarshan, Subrata Kumar Ghosh
Materials Chemistry and Physics (2021) Vol. 273, pp. 125136-125136
Closed Access | Times Cited: 23
Study of buoyancy effects in unsteady stagnation point flow of Maxwell nanofluid over a vertical stretching sheet in the presence of Joule heating
Zahoor Iqbal, Masood Khan, Muhammad Shoaib, et al.
Waves in Random and Complex Media (2022), pp. 1-15
Closed Access | Times Cited: 16
Estimation of forced heat convection in a rectangular channel with curved-winglet vortex generator: A machine learning approach
Adnan Berber, Mehmet Gürdal
Thermal Science and Engineering Progress (2022) Vol. 37, pp. 101563-101563
Closed Access | Times Cited: 16
Machine learning‐based prediction of mechanical and thermal properties of nickel/cobalt/ferrous and dried leaves fiber‐reinforced polymer hybrid composites
H. Mohit, Sanjay Mavinkere Rangappa, Suchart Siengchin, et al.
Polymer Composites (2023) Vol. 45, Iss. 1, pp. 489-506
Closed Access | Times Cited: 9
Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling
Adeola Borode, Thato Tshephe, Peter Apata Olubambi, et al.
Journal of Thermal Analysis and Calorimetry (2024) Vol. 149, Iss. 10, pp. 5059-5083
Open Access | Times Cited: 3
Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles
Sadegh Motahar, Saeb Sadri
International Journal of Energy Research (2021) Vol. 45, Iss. 10, pp. 15092-15109
Open Access | Times Cited: 20
Development of a unique multi-layer perceptron neural architecture and mathematical model for predicting thermal conductivity of distilled water based nanofluids using experimental data
Shiva Singh, Sumit Kumar, Subrata Kumar Ghosh
Colloids and Surfaces A Physicochemical and Engineering Aspects (2021) Vol. 627, pp. 127184-127184
Closed Access | Times Cited: 20
Thermal conductivity prediction of WO3-CuO-Ag (35:40:25)/water hybrid ternary nanofluid with Artificial Neural Network and back-propagation algorithm
Chunlei Lin, Junhui Zhou, Qianqian Lu, et al.
Materials Today Communications (2023) Vol. 36, pp. 106807-106807
Closed Access | Times Cited: 8
The empirical characteristics on transient nature of Al2O3-water nanofluid pool boiling
Ashutosh Pare, Subrata Kumar Ghosh
Applied Thermal Engineering (2021) Vol. 199, pp. 117617-117617
Closed Access | Times Cited: 19
Investigation of different training function efficiency in modeling thermal conductivity of TiO2/Water nanofluid using artificial neural network
Mohammad Hemmat Esfe, Saeed Esfandeh, Davood Toghraie
Colloids and Surfaces A Physicochemical and Engineering Aspects (2022) Vol. 653, pp. 129811-129811
Closed Access | Times Cited: 13
Machine Learning Approach for Thermal Characteristics and Improvement of Heat Transfer of Nanofluids—A Review
Harishchandra Patel, Dwesh K. Singh, Om Prakash, et al.
Lecture notes in networks and systems (2024), pp. 227-233
Closed Access | Times Cited: 2
Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions
Md Munirul Hasan, M.M. Rahman, Mohammad Saiful Islam, et al.
Frontiers in Heat and Mass Transfer (2024) Vol. 22, Iss. 2, pp. 537-556
Open Access | Times Cited: 2
Addition of MWCNT-Al2O3 nanopowders to water- ethylene glycol (EG) base fluid for enhancing the thermal characteristics: Design an optimum feed-forward neural network
Shi Fu-xi, Sajad Hamedi, Mehdi Hajian, et al.
Case Studies in Thermal Engineering (2021) Vol. 27, pp. 101293-101293
Open Access | Times Cited: 17
Preparation, characterization and experimental investigation of thermophysical properties of stable TiN nanofluid for solar thermal application
Kishor Deshmukh, Suhas Karmare, Dipak Raut
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2022) Vol. 44, Iss. 10
Closed Access | Times Cited: 12
Modelling the effects of mixing ratio and temperature on the thermal conductivity of GNP-Alumina hybrid nanofluids: A comparison of ANN, RSM, and linear regression methods
Adeola Borode, Peter Apata Olubambi
Heliyon (2023) Vol. 9, Iss. 8, pp. e19228-e19228
Open Access | Times Cited: 6
Viscosity and thermal conductivity correlations for various nanofluids based on different temperature and nanoparticle diameter
Hossein Asadi Moghaddam, Ashkan Ghafouri, Reza Faridi Khouzestani
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2021) Vol. 43, Iss. 6
Closed Access | Times Cited: 15
Using radial basis function network to model the heat transfer and pressure drop of water based nanofluids containing MgO nanoparticles
Mohammad Hemmat Esfe, Mohammad Hassan Kamyab, Ali Alirezaie, et al.
Case Studies in Thermal Engineering (2021) Vol. 28, pp. 101475-101475
Open Access | Times Cited: 12
Experimental investigation and intelligent modeling of thermal conductivity of R141b based nanorefrigerants containing metallic oxide nanoparticles
Songyuan Zhang, Yuexiwei Li, Z.R. Xu, et al.
Powder Technology (2021) Vol. 395, pp. 850-871
Closed Access | Times Cited: 12
An Updated Review on Improving Radiator Efficiency Using Nanofluid Coolants
Baqir Sabah Nuri, Hasan I. Dawood, Suzanne Alsamaq
Russian Journal of Applied Chemistry (2024) Vol. 97, Iss. 1, pp. 169-182
Closed Access | Times Cited: 1
Detailed experimentation and prediction of thermophysical properties in lauric acid-based nanocomposite phase change material using artificial neural network
Elangovan Thangapandian, Ponnusamy Palanisamy, Senthil Kumaran Selvaraj, et al.
Journal of Energy Storage (2023) Vol. 74, pp. 109345-109345
Open Access | Times Cited: 4
An analytical and statistical review of selected researches in the field of estimation of rheological behavior of nanofluids
Roozbeh Moshfeghi, Davood Toghraie
Powder Technology (2021) Vol. 398, pp. 117076-117076
Closed Access | Times Cited: 9
A novel hybrid nanofluid including MWCNT and ZrO2 nanoparticles: implementation of response surface methodology and artificial neural network
Jawed Mustafa, Saeed Alqaed, M.M. Abdullah, et al.
Journal of Thermal Analysis and Calorimetry (2023) Vol. 148, Iss. 18, pp. 9619-9632
Closed Access | Times Cited: 3

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