Статья

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

Reza Rostamzadeh‐RenaniEnergy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyMohammadreza BaghoolizadehDepartment of Mechanical Engineering, Shahrekord University, Shahrekord 88186-34141, IranS. Mohammad SajadiDepartment of Nutrition, Cihan University-Erbil, Kurdistan Region, IraqMostafa PirmoradianDepartment of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, IranMohammad Rostamzadeh‐RenaniEnergy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalySh. BaghaeiDepartment of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, IranSoheil SalahshourDepartment of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

2023en

ABI

Аннотация

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 (φ) 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 between 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.

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Идентификаторы

DOI: 10.1016/j.aej.2023.10.059

Цитирования и источники

Цитирований: 4Использованных источников: 0

Показатели — AkademScholar