Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods(ELSEVIER, 2025) Li, Huaguang; Ali, Ali B. M.; Hussein, Rasha Abed; Singh, Narinderjit Singh Sawaran; Abdullaeva, Barno; Ahmad, Zubair; Salahshour, Soheil; Baghoolizadeh, Mohammadreza; Pirmoradian, Mostafa; University of Warith Alanbiyaa; University of Manara; INTI International University; Tashkent State Pedagogical University; King Khalid University; King Khalid University; Okan University; Bahcesehir University; Ministry of Education of Azerbaijan Republic; Khazar University; Shahrekord University; Islamic Azad UniversityBackground: Because of their enhanced thermophysical characteristics, namely greater thermal conductivity, viscosity control, and long-term stability than traditional nanofluids, hybrid nanofluids drew interest. Such properties make them suitable candidates for many industrial applications such as solar systems and thermal management. However, knowing the thermophysical properties of these materials accurately is difficult because of the complexities of nanoparticles and the interaction with the base fluid. This paper utilizes machine learning methods to predict the thermophysical properties of water/ethylene glycol mixture-based hybrid nanofluids containing reduced silver-graphene oxide. Method: ology: This study aimed to predict Viscosity (DV), Thermal Conductivity (TC) and Density (D) by three machine learning algorithms including multiple linear regression (MLR), Multiple Polynomial Regression (MPR) and Gaussian Process Regression (GPR). A 5 x 28 dataset was used for training and testing the network, with 80 % of the data used for training the network and 20 % for testing the network. Evaluating the performance of algorithms is based on the evaluation indices of Correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Standard Deviation (STD). In addition, optimization is done by the Non- dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm and the impact results of different mutation and combination rates are examined. Results: The MPR algorithm yielded the lowest MoD values (0.07 % and-0.06 %) and the highest prediction accuracy among the models tested (R = 0.9999, RMSD = 2.726 x 10-4, STD = 0.0219). Furthermore, NSGA-II optimization results revealed that the temperature and concentration of nanoparticles could effectively increase the thermal conductivity, while too high concentration could also increase viscosity. Finally, through the TOPSIS method, the best point was chosen giving a blend of ideal thermophysical properties. This signifies that machine learning methods can be successfully employed for the prediction and optimization of hybrid nanofluid characteristics.Publication Metadata only A comprehensive review of data analytics and storage methods in geothermal energy operations(ELSEVIER, 2025) Basem, Ali; Al-Nussairi, Ahmed Kateb Jumaah; Khidhir, Dana Mohammad; Singh, Narinderjit Singh Sawaran; Baghoolizadeh, Mohammadreza; Fazilati, Mohammad Ali; Salahshour, Soheil; Sajadi, S. Mohammad; Hasanabad, Ali Mohammadi; University of Warith Alanbiyaa; University of Manara; Knowledge University; INTI International University; Shahrekord University; Islamic Azad University; Okan University; Bahcesehir University; Ministry of Education of Azerbaijan Republic; Khazar UniversityGeothermal energy storage (GES) systems are thoroughly examined in this research, with a focus on methods like borehole thermal energy storage (BTES), underground thermal energy storage (UTES), and aquifer thermal energy storage (ATES). It highlights the importance of thermal energy storage (TES) systems in addressing global energy challenges. The feasibility of UTES for large-scale energy storage and its integration with geothermal power plants is investigated. The ATES, with the advantage of large storage capacity and low operating costs has could be employed in regions with suitable aquifers. The adaptability of BTES to different ground conditions and its small land footprint made it a spotlight for the researchers. The study emphasizes the role of TES technologies in meeting the growing demand for renewable energy, reducing the impact of climate change, and providing efficient energy solutions for heating, ventilating, and air conditioning. HVAC systems. Also, the application of geothermal power plants and TES systems in decreasing the dependence on nonrenewable energy sources and increasing energy efficiency increase investigated. The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. Picture selection, systematic time series feature engineering and model evaluation were all areas that showed a lot of promise in the systematic review for future research and practical applications.Publication Metadata only Utilizing machine learning algorithms for prediction of the rheological behavior of ZnO (50%)-MWCNTs (50%)/ Ethylene glycol (20%)-water (80%) nano-refrigerant(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Song, Xiedong; Baghoolizadeh, Mohammadreza; Alizadeh, As'ad; Basem, Ali; Jasim, Dheyaa J.; Sultan, Abbas J.; Salahshour, Soheil; Piromradian, Mostafa; Jining University; Inner Mongolia University of Finance & Economics; Shahrekord University; Cihan University-Erbil; Al-Amarah University College; University of Warith Alanbiyaa; University of Technology- Iraq; University of Missouri System; Missouri University of Science & Technology; Okan University; Bahcesehir University; Lebanese American University; Islamic Azad UniversityThis paper aims to explore the utilization of machine learning techniques for the accurate prediction of rheological properties in a specific nanofluid system, ZnO(50 %)-MWCNTs (50 %)/Ethylene glycol (20 %)-water (80 %), designed for nano-refrigeration applications. The effective manipulation of the rheological behavior of nanofluids is pivotal for enhancing their heat transfer efficiency and overall performance. By harnessing the predictive power of machine learning, this study endeavors to unravel the intricate relationships governing the rheological characteristics of the nano-refrigerant, ultimately contributing to the development of advanced cooling solutions. The obtained results show that pnf of ZnO(50%)-MWCNTs (50%)/ Ethylene glycol(20%)-water (80%) nano-refrigerant is little affected by T, and even when T varies, this result does not alter much. Also, the lowest pnf occurs when it has the highest temperature and the lowest gamma and m. Finally, it was concluded that the best algorithm in terms of the Taylor diagram for pnf output is the MPR algorithm and the worst is the ECR algorithm and the pattern of gamma changes shows that the ideal value of gamma is the biggest when pnf levels fall in tandem with their growth.Publication Metadata only Combination of group method of data handling neural network with multi-objective gray wolf optimizer to predict the viscosity of MWCNT-TiO2-oil SAE50 nanofluid(ELSEVIER, 2024) Zhou, Hongfei; Ali, Ali B. M.; Zekri, Hussein; Abdulaali, Hanaa Kadhim; Bains, Pardeep Singh; Sharma, Rohit; Abduvalieva, Dilsora; Baghoolizadeh, Mohammadreza; Salahshour, Soheil; Hashemian, Mohammad; Hebei Petroleum University of Technology; University of Warith Alanbiyaa; University of Zakho; University of Technology- Iraq; Jain University; Vivekananda Global University; Shobhit University; Shahrekord University; Tashkent State Pedagogical University; Okan University; Bahcesehir University; Lebanese American University; Islamic Azad UniversityBackground: Nanofluids are the most widely used materials in various engineering fields. They have different properties under different conditions, and predicting their properties requires several experiments. Artificial intelligence can predict the properties of nanofluids in the shortest time and cost. Methodology: This study aims to predict the viscosity and share rate of MWCNT-TiO2 (40-60)-oil SAE50 nano-lubricant (NL). Machine learning algorithms and neural networks can respond best to this important matter. For this purpose, the Group Method of Data Handling (GMDH) neural network is combined with the meta-heuristic algorithm Multi-Objective Gray Wolf Optimizer (MOGWO). This way, the experimental data is first given to the artificial neural network (ANN). Then, the meta-heuristic algorithm optimizes the hyperparameters of the ANN to bring the predicted results closer to the experimental data and minimize the error. The MOGWO algorithm's regulators are the number of iterations and the number of wolves investigated in this study to better select this algorithm. Then, these modes are measured using two criteria, correlation coefficient (R) and rote mean squared error (RMSE), to choose the best mode. Finally, by using the extracted equations by the GMDH neural network, the best models or the Pareto front can be obtained using the MOGWO meta-heuristic algorithm. Results: The error histogram diagram shows the excellent performance of the combination of the GMDH neural network and the MOGWO meta-heuristic algorithm. The values of R and RMSE for viscosity and shear rate are equal to 0.99217, 15.8749, and 0.99031, 68.7723, respectively. The optimization results showed that the best conditions to meet viscosity and cutting rate are when phi, T, and gamma equal 1.21*e-5, 46.71, and 50.11.Publication Metadata only Comprehensive review of green roof and photovoltaic-green roof systems for different climates to examine the energy-saving and indoor thermal comfort(PERGAMON-ELSEVIER SCIENCE LTD, 2025) Liao, Xiayan; Ali, Ali B. M.; Singh, Narinderjit Singh Sawaran; Baghoolizadeh, Mohammadreza; Alam, Mohammad Mahtab; Orlova, Tatyana; Salahshour, Soheil; Alizadeh, As'ad; Leshan Normal University; University of Warith Alanbiyaa; INTI International University; Shahrekord University; King Khalid University; Tashkent State Pedagogical University; Okan University; Bahcesehir University; Piri Reis University; Cihan University-ErbilBuilt-up regions are increasingly at risk from climate change and urban heat islands (UHIs). Solar panels and green roof systems (PV/GR) can provide several advantages to support ecologically sustainable cities. Research gaps in hot climates at the building and urban sizes are highlighted in this study, which examines the advantages of GR and combined PV/GR systems as well as their optimal design parameters. An extensive analysis of published works from the Scopus database was conducted to examine how energy-saving and indoor thermal comfort (UH-ES-ITC) was accomplished in urban structures, as well as the impact of green roofs (GR) and photovoltaic/GR systems on UHI mitigation. It's been found that, especially at building scale, GR and GR/PV systems enhance notable qualities in hot, dry locations. Sadly, not much research has been done on GR/PV systems on coupling scales. Among the research gaps identified in this study are those related to the methodology, scope, climate, objectives, variables, and features of this integration in different climate zones. Researchers and urban planners might use the findings to inform future research directions and implementation.Publication Metadata only Multi-objective optimization of vertical and horizontal solar shading in residential buildings to increase power output while reducing yearly electricity usage(PERGAMON-ELSEVIER SCIENCE LTD, 2025) Zhang, Lei; Alizadeh, Asad; Baghoolizadeh, Mohammadreza; Salahshour, Soheil; Ali, Elimam; Escorcia-Gutierrez, Jose; Baoji University of Arts & Sciences; Cihan University-Erbil; Shahrekord University; Okan University; Bahcesehir University; Ministry of Education of Azerbaijan Republic; Khazar University; Prince Sattam Bin Abdulaziz University; Universidad de la CostaBackground: One of the effective strategies to reduce residential energy use is shading devices. Shading devices can be installed adjacent to windows in vertical or horizontal orientations to regulate the amount of sunlight entering a building. This study focuses on Photovoltaic Shading Devices (PVSDs), which combine traditional shading functions with photovoltaic (PV) technology. PVSDs are designed to block excessive sunlight and convert incident solar radiation into electricity, thereby serving dual purposes of energy conservation and renewable energy production. Methodology: This work presents a multi-objective optimization of PVSD configurations to maximize power output and reduce annual electricity consumption in residential buildings. Nine design variables were optimized using EnergyPlus software for energy simulation and JEPLUS + EA software for optimization. The study analyzed four cities representing different climate types. Results: The results showed that the use of solar shading for the city of Bandar Abbas reduces electricity consumption by 1000 kWh per year and also produces 1920 kWh of electricity. In the south, west, north, and east directions, electricity has improved between 15 and 37%, 10-29 %, 7-22 %, and 7-30 % annually. The findings highlight the effectiveness of PVSDs in balancing energy efficiency and renewable energy production, with significant variations depending on climate and building orientation.Publication Metadata only A comprehensive review of a building-integrated photovoltaic system (BIPV)(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Chen, Lin; Baghoolizadeh, Mohammadreza; Basem, Ali; Ali, Sadek Habib; Ruhani, Behrooz; Sultan, Abbas J.; Salahshour, Soheil; Alizadeh, As'ad; Yantai University; Shahrekord University; University of Warith Alanbiyaa; Al-Amarah University College; University of Technology- Iraq; University of Missouri System; Missouri University of Science & Technology; Okan University; Bahcesehir University; Lebanese American University; Urmia UniversityBeginning in the early 1990s, photovoltaic (PV) technologies were integrated with building envelopes to reduce peak electrical load and fulfill building energy demands. The PV technologies are referred to be building- integrated (BI) PV systems when they are either incorporated or mounted to the envelopes. BIPV system groupings include BIPV roofs, BIPV facades, BIPV windows, and BIPV shadings. In this study, the technology division of photovoltaic cells and the BIPV system groupings are discussed and investigated. This evaluation addresses several variables that impact the BIPV system applications' functionality and design. The tilt angle of PV shading devices, transmittance, window-to-wall ratio (WWR), and glass orientation are the parameters that have been found. Researchers will find this review paper useful in constructing the BIPV system since it offers opportunities for future study.Publication Metadata only Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Jin, Weihong; Basem, Ali; Baghoolizadeh, Mohammadreza; Kamoon, Saeed S.; Al-Yasiri, Mortatha; Salahshour, Soheil; Hekmatifar, Maboud; University of Warith Alanbiyaa; Shahrekord University; Al-Amarah University College; Okan University; Bahcesehir University; Lebanese American University; Islamic Azad UniversityThe research used an artificial neural network (ANN) model to examine the rheological properties of hybrid nonNewtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) mu nf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for mu nf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for 9, T, and gamma obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.Publication Metadata only Geometrical optimization of solar venetian blinds in residential buildings to improve the economic costs of the building and the visual comfort of the residents using the NSGA-II algorithm(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Liu, Jie; Baghoolizadeh, Mohammadreza; Basem, Ali; Hamza, Hussein; Sudhamsu, Gadug; Al-Musawi, Tariq J.; Sultan, Abbas J.; Salahshour, Soheil; Alizadeh, A.; Yanan University; Shahrekord University; University of Warith Alanbiyaa; Al-Amarah University College; Al-Mustaqbal University College; University of Technology- Iraq; Okan University; Bahcesehir University; Lebanese American University; Urmia UniversityThe entering sunlight from the building's windows mainly affects the heating and visual comfort of the occupants. The applications of Venetian blinds are a solution to improve the heating and visual comfort of the occupants. However, reducing the sunlight that enters the space can result in a rise in the building's electricity consumption. While most studies focus on the electricity production of solar panels, present study aims to examine the effect of solar venetian blinds on the indoor visual and thermal comfort of the occupants and optimize their geometry considering different geographical specifications. In the present paper, efforts are made to numerically install solar panels on Venetian blinds and analyze the effect of changing the geometrical parameters of solar Venetian blinds and the building's window dimensions on visual comfort and net electricity. Therefore, the target functions in the present paper are an improvement percentage in the daylight glare index and an improvement percentage in the net electricity costs for the analyzed building. As a result, five cities in Iran that have different climatic conditions are targeted to model the building. EnergyPlus software is employed to conduct the energy-based calculations, and the design variables and target functions are defined using JEPLUS software. The outputs are next inserted in JEPLUS+EA software to process a multi-objective optimization using the NSGA-II algorithm. The results demonstrate that the visual comfort and net electricity can be optimized by ranges of 10-100% and 1.5-10%, respectively. Furthermore, Venetian blinds are proven to have higher reception of sun radiations and better efficiency in southern cities and they can have a more proper performance while being installed for windows of southern building wall.Publication Metadata only Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and group method of data handling artificial neural networks(ELSEVIER, 2024) Rostamzadeh-Renani, Reza; Jasim, Dheyaa J.; Baghoolizadeh, Mohammadreza; Rostamzadeh-Renani, Mohammad; Andani, Hamid Taheri; Salahshour, Soheil; Baghaei, Sh.; Polytechnic University of Milan; Al-Amarah University College; Shahrekord University; Isfahan University of Technology; Islamic Azad University; Okan University; Bahcesehir University; Lebanese American UniversityIn this article, the ability of GMDH artificial neural networks (ANNs) to predict the rheological behavior (RB) of nanofluids (NFs) containing CuO NPs is studied. ANNs are a powerful mathematical tool that can identify the relationship among the parameters without the need to extract the relationship among them. The main purpose of this study is to use the GMDH ANN method to generate and predict the viscosity (mu) parameter using several input variables (IPV) such as solid volume fraction (SVF), nanoparticles (NPs), temperature (Temp), and shear rate (SR). By pairing the GMDH ANN with the evolutionary algorithm, this capability is created so that the values predicted by the ANN are more compatible with the laboratory numbers. The evolutionary algorithms (EAs) used in this study include three algorithms: Non-Dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf Optimizer (MOGWO). These algorithms are selected for optimization, among which the best performance is related to the coupling of GMDH ANN with the MOGWO algorithm. In the next step, the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Grey Wolf Optimizer (GWO) algorithms are used. This process is done to minimize the target function (TF) (mu) and evaluate the optimal points. According to the obtained results, among the EAs used in this study, the best performance belongs to the GA algorithm. Finally, in the last part of this study, the most optimal mode for IPV and output variable (OPV) of TF is determined. Numerically, the values of IPV data, such as SVF, T, and SR, are respectively 0.2242%, 50, and 246.7427, and the most optimal value for the OPV of TF (mu) was estimated as 0.96686 cP.
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