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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:

On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach
Mehdi Jamei, Iman Ahmadianfar, Ismail Adewale Olumegbon, et al.
Journal of Energy Storage (2020) Vol. 33, pp. 102067-102067
Closed Access | Times Cited: 94

Showing 1-25 of 94 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
Application of nanofluids for enhanced waste heat recovery: A review
A.G. Olabi, Khaled Elsaid, Enas Taha Sayed, et al.
Nano Energy (2021) Vol. 84, pp. 105871-105871
Closed Access | Times Cited: 125
A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters
Mohammad Najafzadeh, Saeid Niazmardi
Natural Resources Research (2021) Vol. 30, Iss. 5, pp. 3761-3775
Closed Access | Times Cited: 80
Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: Case study of Fontaine Gazelles Dam, Algeria
Issam Rehamnia, Bachir Benlaoukli, Mehdi Jamei, et al.
Measurement (2021) Vol. 176, pp. 109219-109219
Closed Access | Times Cited: 78
Machine learning regression-CFD models for the nanofluid heat transfer of a microchannel heat sink with double synthetic jets
Javad Mohammadpour, Shahid Husain, Fatemeh Salehi, et al.
International Communications in Heat and Mass Transfer (2021) Vol. 130, pp. 105808-105808
Closed Access | Times Cited: 77
An experimental investigation of thermal conductivity and dynamic viscosity of Al2O3-ZnO-Fe3O4 ternary hybrid nanofluid and development of machine learning model
Humphrey Adun, Doğa Kavaz, Mustafa Dagbasi, et al.
Powder Technology (2021) Vol. 394, pp. 1121-1140
Closed Access | Times Cited: 69
Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning techniques
Zafar Said, Prabhakar Sharma, Rajvikram Madurai Elavarasan, et al.
Journal of Energy Storage (2022) Vol. 54, pp. 105230-105230
Closed Access | Times Cited: 64
On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network
Mehdi Jamei, Ismail Adewale Olumegbon, Masoud Karbasi, et al.
International Journal of Heat and Mass Transfer (2021) Vol. 172, pp. 121159-121159
Closed Access | Times Cited: 61
Specific heat capacity of molten salt-based nanofluids in solar thermal applications: A paradigm of two modern ensemble machine learning methods
Mehdi Jamei, Masoud Karbasi, Ismail Adewale Olumegbon, et al.
Journal of Molecular Liquids (2021) Vol. 335, pp. 116434-116434
Closed Access | Times Cited: 59
A critical review of specific heat capacity of hybrid nanofluids for thermal energy applications
Humphrey Adun, Ifeoluwa Wole‐Osho, Eric C. Okonkwo, et al.
Journal of Molecular Liquids (2021) Vol. 340, pp. 116890-116890
Closed Access | Times Cited: 59
On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques
Mehdi Jamei, Iman Ahmadianfar, Ismail Adewale Olumegbon, et al.
International Communications in Heat and Mass Transfer (2021) Vol. 123, pp. 105217-105217
Closed Access | Times Cited: 57
Magneto-bioconvection flow of hybrid nanofluid in the presence of oxytactic bacteria in a lid-driven cavity with a streamlined obstacle
Shafqat Hussain, Abdelraheem M. Aly, Hakan F. Öztop
International Communications in Heat and Mass Transfer (2022) Vol. 134, pp. 106029-106029
Closed Access | Times Cited: 54
Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture
U. K. Singh, Mehdi Jamei, Masoud Karbasi, et al.
Journal of Hydrology (2022) Vol. 607, pp. 127549-127549
Closed Access | Times Cited: 53
Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe 3 O 4 ‐coated MWCNT hybrid nanofluids for potential application in energy systems
Prabhakar Sharma, Zafar Said, Saim Memon, et al.
International Journal of Energy Research (2022) Vol. 46, Iss. 13, pp. 19242-19257
Open Access | Times Cited: 44
Computational methods to simulate molten salt thermophysical properties
Talmage Porter, Michael M. Vaka, Parker Steenblik, et al.
Communications Chemistry (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 40
Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids
Zhongwei Zhang, Mohammed Al‐Bahrani, Behrooz Ruhani, et al.
Chemical Engineering Journal (2023) Vol. 471, pp. 144362-144362
Closed Access | Times Cited: 35
Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms
Majid Emami Javanmard, S.F. Ghaderi
Sustainable Cities and Society (2023) Vol. 95, pp. 104623-104623
Closed Access | Times Cited: 32
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
Measuring the response of clean energy stock price volatility to extreme shocks
Li Zhang, Lu Wang, Lijuan Peng, et al.
Renewable Energy (2023) Vol. 206, pp. 1289-1300
Closed Access | Times Cited: 25
Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model
Masoud Karbasi, Mehdi Jamei, Anurag Malik, et al.
Agricultural Water Management (2023) Vol. 281, pp. 108210-108210
Open Access | Times Cited: 25
Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall
Mohammad Ehteram, Ali Najah Ahmed, Zohreh Sheikh Khozani, et al.
Water Resources Management (2023) Vol. 37, Iss. 9, pp. 3631-3655
Closed Access | Times Cited: 22
Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data
Shivam Dangwal, Yuji Ikeda, Blazej Grabowski, et al.
Chemical Engineering Journal (2024) Vol. 493, pp. 152606-152606
Open Access | Times Cited: 11
Estimating the density of hybrid nanofluids for thermal energy application: Application of non-parametric and evolutionary polynomial regression data-intelligent techniques
Mehdi Jamei, Masoud Karbasi, Mehdi Mosharaf‐Dehkordi, et al.
Measurement (2021) Vol. 189, pp. 110524-110524
Closed Access | Times Cited: 47
Assessment of scouring around spur dike in cohesive sediment mixtures: A comparative study on three rigorous machine learning models
Manish Pandey, Mehdi Jamei, Iman Ahmadianfar, et al.
Journal of Hydrology (2021) Vol. 606, pp. 127330-127330
Closed Access | Times Cited: 47
Prediction of Maximum Scour Depth near Spur Dikes in Uniform Bed Sediment Using Stacked Generalization Ensemble Tree-Based Frameworks
Manish Pandey, Mehdi Jamei, Masoud Karbasi, et al.
Journal of Irrigation and Drainage Engineering (2021) Vol. 147, Iss. 11
Closed Access | Times Cited: 44
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