Browsing by Author "Baghoolizadeh, Mohammadreza"
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Article Citation Count: 0A 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.; Alizadeh, As'adBeginning 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.Article Citation Count: 0Geometrical 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; Salahshour, Soheıl; Basem, Ali; Hamza, Hussein; Sudhamsu, Gadug; Al-Musawi, Tariq J.; Alizadeh, A.The 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.Article Citation Count: 3A multi-objective and CFD based optimization of roof-flap geometry and position for simultaneous drag and lift reduction(Keai Publishing Ltd, 2024) Rostamzadeh-Renani, Mohammad; Salahshour, Soheıl; Sajadi, S. Mohammad; Rostamzadeh-Renani, Reza; Azarkhavarani, Narjes Khabazian; Salahshour, Soheil; Toghraie, DavoodAs the transport sector is responsible for the consumption of a vast proportion of the oil produced, it is mandatory to research feasible solutions to tackle this issue. The application of aerodynamic attachments for passive flow control and reducing resisting aerodynamic forces such as drag and lift is one of the most practicable ways to minimize vehicle energy consumption. The flaps are one of the most innovative aerodynamic attachments that can enhance the flow motion in the boundary layer at the trailing edge of the wings. In the present paper, the flap is designed and modeled for controlling the airflow at the roof-end of a 2D Ahmed body model, inspired by the schematic of the flap at the trailing edge of the wing. As a result, the flap 's geometry and position from the roof -end of the car model are parameterized, which leads to having four design variables. The objective functions of the present study are the vehicle 's drag coefficient and lift coefficient. 25 Design of Experiment (DOE) points are considered enabling the Box-Behnken method. Then, each DOE point is modeled in the computational domain, and the flow -field around the model is simulated using Ansys Fluent software. The results obtained for the DOE points are employed by different regressors, and the relation between design variables and objective functions is extracted using GMDH-ANN. The GMDH-ANN is then coupled with three types of optimization algorithms, among which the Genetic algorithm proves to have the most ideal coupling process for optimization. Finally, after analyzing the variations in the geometry and position of the roof flap from the car roof -end, the roof -flap with specifications of L = 0.1726 m, a = 5.0875 degrees , H = 0.0188 m, and d = 0.241 m can optimize the car drag and lift coefficients by 21.27% and 19.91%, respectively. The present research discusses the opportunities and challenges of optimal design roof -flap geometry and its influence on car aerodynamic performance. 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/bync-nd/4.0/).Article Citation Count: 7Multi-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; Salahshour, Soheıl; Baghoolizadeh, Mohammadreza; Rostamzadeh-Renani, Mohammad; Andani, Hamid Taheri; Salahshour, Soheil; Baghaei, Sh.In 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.Article Citation Count: 0Prediction and extensive analysis of MWCNT-MgO/oil SAE 50 hybrid nano-lubricant rheology utilizing machine learning and genetic algorithms to find ideal attributes(Elsevier Sci Ltd, 2024) Baghoolizadeh, Mohammadreza; Salahshour, Soheıl; Sajadi, S. Mohammad; Salahshour, Soheil; Baghaei, Sh.Genetic algorithms and machine learning methods can accurately anticipate hybrid nanofluids' complicated rheology. Scientists and engineers can understand hybrid materials by using genetic algorithms to optimize and machine learning to discover complicated relationships between input variables and rheological responses. As a continuation of the author's previous research on the rheological properties of a nano-lubricant based on engine oil and hybrid nanoparticles, this study uses machine learning and genetic algorithms to theoretically assess the dynamic viscosity of the MWCNT-MgO/oil SAE 50 hybrid nanofluid and identify optimal properties. MLR, DTree, Ridge, PLR, SVM, Lasso, ECR, GPR, and MPR are used for regression analysis. Best multi-objective issue solutions are represented by the Pareto front. The NSGA-II algorithm determines the Pareto front. The MPR and NSGA-II algorithms provide a Pareto front with the most precise optimal spot boundaries. The Weighted Sum Method (WSM) simplifies multi-objective problems into single-objective problems, making optimal solutions easier to find. The results show that the maximum margin of deviation for mu nf and tau is - 2.5615 and - 5.239, respectively. According to the Taylor chart, the best mu nf mode for R, RMSE and STD is equal to 0.9983, 7.6639, 130.0056. Also, these values for tau are equal to 0.9996, 15.4515, and 516.0219.Article Citation Count: 5Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling(Elsevier, 2023) Rostamzadeh-Renani, Reza; Salahshour, Soheıl; Sajadi, S. Mohammad; Pirmoradian, Mostafa; Rostamzadeh-Renani, Mohammad; Baghaei, Sh.; Salahshour, SoheilFor conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (phi) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap be-tween experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.Article Citation Count: 0Regression 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; Salahshour, Soheıl; Baghoolizadeh, Mohammadreza; Kamoon, Saeed S.; Al-Yasiri, Mortatha; Salahshour, Soheil; Hekmatifar, MaboudThe 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.Article Citation Count: 1Utilizing 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; Salahshour, Soheıl; Alizadeh, As'ad; Basem, Ali; Jasim, Dheyaa J.; Sultan, Abbas J.; Piromradian, MostafaThis 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.