Browsing by Author "Rostamzadeh-Renani, Reza"

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Citation Count: 3
A 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, Davood
As 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/).
Citation Count: 7
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; 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.
Citation Count: 5
Prediction 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, Soheil
For 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.