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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:

Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization
Mohammad Hojjat
Applied Mathematics and Computation (2019) Vol. 365, pp. 124710-124710
Closed Access | Times Cited: 89

Showing 1-25 of 89 citing articles:

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System
Prabhakar Sharma, Zafar Said, Anurag Kumar, et al.
Energy & Fuels (2022) Vol. 36, Iss. 13, pp. 6626-6658
Closed Access | Times Cited: 242
An updated review on the properties, fabrication and application of hybrid-nanofluids along with their environmental effects
Liu Yang, Weikai Ji, Mao Mao, et al.
Journal of Cleaner Production (2020) Vol. 257, pp. 120408-120408
Closed Access | Times Cited: 221
Recent trends on nanofluid heat transfer machine learning research applied to renewable energy
Ting Ma, Zhixiong Guo, Mei Lin, et al.
Renewable and Sustainable Energy Reviews (2020) Vol. 138, pp. 110494-110494
Open Access | Times Cited: 126
Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid
Ibrahim M. Alarifi, Hoàng M. Nguyẽn, Ali Naderi, et al.
Materials (2019) Vol. 12, Iss. 21, pp. 3628-3628
Open Access | Times Cited: 99
Machine learning algorithms for improving the prediction of air injection effect on the thermohydraulic performance of shell and tube heat exchanger
Emad M.S. El‐Said, Mohamed Abd Elaziz, Ammar H. Elsheikh
Applied Thermal Engineering (2020) Vol. 185, pp. 116471-116471
Closed Access | Times Cited: 94
Optimization of high-performance concrete mix ratio design using machine learning
Bin Chen, Lei Wang, Zongbao Feng, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106047-106047
Closed Access | Times Cited: 33
Enhancing performance of nanofluid mini-channel heat sinks through machine learning and multi-objective optimization of operating parameters
Qifan Wang, Shengqi Zhang, Yu Zhang, et al.
International Journal of Heat and Mass Transfer (2023) Vol. 210, pp. 124204-124204
Closed Access | Times Cited: 29
Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer
Mohammed Mudhsh, Emad M.S. El‐Said, Ahmad O. Aseeri, et al.
Case Studies in Thermal Engineering (2023) Vol. 49, pp. 103294-103294
Open Access | Times Cited: 24
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
Thermal-hydraulic performance of a nanofluid in a shell-and-tube heat exchanger equipped with new trapezoidal inclined baffles: Nanoparticle shape effect
Mehdi Bahiraei, Mohammad Naseri, Ali Monavari
Powder Technology (2021) Vol. 395, pp. 348-359
Closed Access | Times Cited: 52
Research on modeling and parameter sensitivity of flow and heat transfer process in typical rectangular microchannels: From a data-driven perspective
Yinjie Ma, Cheng Liu, E Jiaqiang, et al.
International Journal of Thermal Sciences (2021) Vol. 172, pp. 107356-107356
Closed Access | Times Cited: 41
Multi-objective optimization of a bidirectional-ribbed microchannel based on CFD and NSGA-II genetic algorithm
Guilian Wang, Guifu Ding, Rui Liu, et al.
International Journal of Thermal Sciences (2022) Vol. 181, pp. 107731-107731
Closed Access | Times Cited: 31
Estimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learning
Andaç Batur Çolak, Dogan Akgul, Hatice Mercan, et al.
Case Studies in Thermal Engineering (2023) Vol. 42, pp. 102713-102713
Open Access | Times Cited: 21
A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power
Santosh Kumar Singh, Arun Kumar Tiwari, H.K. Paliwal
Engineering Analysis with Boundary Elements (2023) Vol. 155, pp. 62-86
Closed Access | Times Cited: 16
Turbulent forced convection and entropy production of a nanofluid in a solar collector considering various shapes for nanoparticles
Jalal Alsarraf, Amin Shahsavar, Roohollah Babaei Mahani, et al.
International Communications in Heat and Mass Transfer (2020) Vol. 117, pp. 104804-104804
Closed Access | Times Cited: 39
A CFD study on thermohydraulic characteristics of a nanofluid in a shell-and-tube heat exchanger fitted with new unilateral ladder type helical baffles
Mehdi Bahiraei, Mohammad Naseri, Ali Monavari
International Communications in Heat and Mass Transfer (2021) Vol. 124, pp. 105248-105248
Closed Access | Times Cited: 32
Prediction of heat transfer coefficient, pressure drop, and overall cost of double-pipe heat exchangers using the artificial neural network
Andaç Batur Çolak, Özgen Açıkgöz, Hatice Mercan, et al.
Case Studies in Thermal Engineering (2022) Vol. 39, pp. 102391-102391
Open Access | Times Cited: 26
Dynamic pyrolytic reaction mechanisms, pathways, and products of medical masks and infusion tubes
Weijie Xu, Jingyong Liu, Ziyi Ding, et al.
The Science of The Total Environment (2022) Vol. 842, pp. 156710-156710
Open Access | Times Cited: 22
Investigation of the effect of using the finned tubes on the performance of shell and tube heat exchanger by 3D modeling
Yasin Maghsoudali, Ali Rastegarkoutenaei, Masoud Sahami, et al.
Journal of Energy Storage (2022) Vol. 56, pp. 106031-106031
Closed Access | Times Cited: 22
Machine learning algorithms to study the relative roles of jet convection and natural convection in the presence of surface roughness elements on enhancement of jet impingement heat transfer
K. Nagesha
International Journal of Thermal Sciences (2024) Vol. 198, pp. 108847-108847
Closed Access | Times Cited: 5
Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions
Tri W.B. Riyadi, Safarudin Gazali Herawan, Andy Tirta, et al.
Results in Engineering (2024), pp. 103002-103002
Open Access | Times Cited: 4
Exploring Turbulence and micro-scale mixing mechanisms for enhancing jet impingement heat transfer using micro-roughness elements: A data-driven and numerical analysis
K. Nagesha
International Communications in Heat and Mass Transfer (2025) Vol. 162, pp. 108646-108646
Closed Access
A novel study on time-dependent viscosity model of magneto-hybrid nanofluid flow over a permeable cone: applications in material engineering
Hanifa Hanif, Ilyas Khan, Sharidan Shafie
The European Physical Journal Plus (2020) Vol. 135, Iss. 9
Closed Access | Times Cited: 32
Multi-objective optimization of a microchannel membrane-based absorber with inclined grooves based on CFD and machine learning
Zengguang Sui, Yunren Sui, Wei Wu
Energy (2021) Vol. 240, pp. 122809-122809
Closed Access | Times Cited: 30
Artificial intelligence based structural optimization of solid oxide fuel cell with three-dimensional reticulated trapezoidal flow field
Lei Xia, Ali Khosravi, Minfang Han, et al.
International Journal of Hydrogen Energy (2023) Vol. 48, Iss. 72, pp. 28131-28149
Closed Access | Times Cited: 12
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