Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 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

Showing 1-25 of 61 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: 243
Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making
Tao Zhang, Anahita Manafi Khajeh Pasha, S. Mohammad Sajadi, et al.
Chemical Engineering Journal (2024) Vol. 485, pp. 150059-150059
Closed Access | Times Cited: 23
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
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
Research and optimization of thermophysical properties of sic oil-based nanofluids for data center immersion cooling
Qingyi Luo, Changhong Wang, Haiping Wen, et al.
International Communications in Heat and Mass Transfer (2022) Vol. 131, pp. 105863-105863
Closed Access | Times Cited: 38
Sonication impact on thermal conductivity of f-MWCNT nanofluids using XGBoost and Gaussian process regression
Zafar Said, Prabhakar Sharma, Bhaskor Jyoti Bora, et al.
Journal of the Taiwan Institute of Chemical Engineers (2023) Vol. 145, pp. 104818-104818
Closed Access | Times Cited: 38
Thermal conductivity of hydraulic oil-GO/Fe3O4/TiO2 ternary hybrid nanofluid: Experimental study, RSM analysis, and development of optimized GPR model
Amin Shahsavar, Mojtaba Sepehrnia, Hamid Maleki, et al.
Journal of Molecular Liquids (2023) Vol. 385, pp. 122338-122338
Closed Access | Times Cited: 34
Enhancing Solar Energy Conversion Efficiency: Thermophysical Property Predicting of MXene/Graphene Hybrid Nanofluids via Bayesian-Optimized Artificial Neural Networks
Dheyaa J. Jasim, Husam Rajab, As’ad Alizadeh, et al.
Results in Engineering (2024) Vol. 24, pp. 102858-102858
Open Access | Times Cited: 12
Artificial neural network hyperparameters optimization for predicting the thermal conductivity of MXene/graphene nanofluids
Yunyan Shang, Karrar A. Hammoodi, As’ad Alizadeh, et al.
Journal of the Taiwan Institute of Chemical Engineers (2024) Vol. 164, pp. 105673-105673
Closed Access | Times Cited: 10
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
Thermal conductivity prediction of nano enhanced phase change materials: A comparative machine learning approach
Farzad Jaliliantabar
Journal of Energy Storage (2021) Vol. 46, pp. 103633-103633
Closed Access | Times Cited: 34
Investigation on two-phase fluid mixture flow, heat transfer and entropy generation of a non-Newtonian water-CMC/CuO nanofluid inside a twisted tube with variable twist pitch: Numerical and evolutionary machine learning simulation
Amin Shahsavar, Sajad Entezari, Ighball Baniasad Askari, et al.
Engineering Analysis with Boundary Elements (2022) Vol. 140, pp. 322-337
Closed Access | Times Cited: 24
Optimization and design of ANN with Levenberg-Marquardt algorithm to increase the accuracy in predicting the viscosity of SAE40 oil-based hybrid nano-lubricant
Mohammad Hemmat Esfe, Davood Toghraie, Fatemeh Amoozadkhalili
Powder Technology (2022) Vol. 415, pp. 118097-118097
Closed Access | Times Cited: 23
The Applications and Challenges of Nanofluids as Coolants in Data Centers: A Review
Le Sun, Jiafeng Geng, Kaijun Dong, et al.
Energies (2024) Vol. 17, Iss. 13, pp. 3151-3151
Open Access | Times Cited: 5
Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods
Huaguang Li, Ali B. M. Ali, R. Hussein, et al.
Case Studies in Thermal Engineering (2025), pp. 106038-106038
Open Access
Experimental evaluation and development of predictive models for rheological behavior of aqueous Fe3O4 ferrofluid in the presence of an external magnetic field by introducing a novel grid optimization based-Kernel ridge regression supported by sensitivity analysis
Amin Shahsavar, Mehdi Jamei, Masoud Karbasi
Powder Technology (2021) Vol. 393, pp. 1-11
Closed Access | Times Cited: 32
Experimental investigation of thermo-convection behaviour of aqueous binary nanofluids of MgO-ZnO in a square cavity
Collins N. Nwaokocha, M. Momin, Solomon O. Giwa, et al.
Thermal Science and Engineering Progress (2021) Vol. 28, pp. 101057-101057
Closed Access | Times Cited: 30
Thermal performance of hybrid fly ash and copper nanofluid in various mixture ratios: Experimental investigation and application of a modern ensemble machine learning approach
Praveen Kumar Kanti, K.V. Sharma, Mehdi Jamei, et al.
International Communications in Heat and Mass Transfer (2021) Vol. 129, pp. 105731-105731
Closed Access | Times Cited: 30
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
Turbulent forced convective flow in a conical diffuser: Hybrid and single nanofluids
Farida Iachachene, Zoubida Haddad, Müslüm Arıcı, et al.
Engineering Analysis with Boundary Elements (2023) Vol. 148, pp. 205-219
Closed Access | Times Cited: 12
Machine Learning for Thermal Conductivity Prediction in Graphene/Hexagonal Boron Nitride van der Waals Heterostructures
Youzhe Yang, Chunhui Yang, Jie Yang, et al.
The Journal of Physical Chemistry C (2025)
Closed Access
Application of the adaptive method to determine the process noise in the extended Kalman filter to estimate the parameters of the two dimensional inverse heat transfer problem
Ramin Sajedi, Farshad Kowsary, Ahmad Kahrbaeiyan, et al.
International Journal of Thermal Sciences (2024) Vol. 201, pp. 109027-109027
Closed Access | Times Cited: 3
Estimation of thermal parameters of a locomotive brake disc using an adaptive type 1 and type 2 fuzzy Kalman filter
Ramin Sajedi, Javad Faraji, Farshad Kowsary, et al.
International Communications in Heat and Mass Transfer (2024) Vol. 157, pp. 107825-107825
Closed Access | Times Cited: 3
Dynamic viscosity analysis of hybrid nanofluid MWCNT- Al2O3/engine oil using statistical models with evaluating its performance in a double tube heat exchanger
Ali Heydari, Masoud Goharimanesh, Mohammad Reza Gharib
Journal of Thermal Analysis and Calorimetry (2022) Vol. 148, Iss. 16, pp. 8025-8039
Closed Access | Times Cited: 15
Page 1 - Next Page

Scroll to top