Hosseini, S.E.; Karimi, O.; AsemanBakhsh, M.A. Experimental Investigation and Multi-Objective Optimization of Savonius Wind Turbine Based on Modified Non-Dominated Sorting Genetic Algorithm-II. Wind Engineering 2023, doi:10.1177/0309524x231217726.
Hosseini, S.E.; Karimi, O.; AsemanBakhsh, M.A. Experimental Investigation and Multi-Objective Optimization of Savonius Wind Turbine Based on Modified Non-Dominated Sorting Genetic Algorithm-II. Wind Engineering 2023, doi:10.1177/0309524x231217726.
Hosseini, S.E.; Karimi, O.; AsemanBakhsh, M.A. Experimental Investigation and Multi-Objective Optimization of Savonius Wind Turbine Based on Modified Non-Dominated Sorting Genetic Algorithm-II. Wind Engineering 2023, doi:10.1177/0309524x231217726.
Hosseini, S.E.; Karimi, O.; AsemanBakhsh, M.A. Experimental Investigation and Multi-Objective Optimization of Savonius Wind Turbine Based on Modified Non-Dominated Sorting Genetic Algorithm-II. Wind Engineering 2023, doi:10.1177/0309524x231217726.
Abstract
This study will lead to the development of a numerical data set that will assist in the design of Savonius wind turbines. A major objective of this study is to develop a better design for Savonius turbine blades in order to increase their torque coefficients, rotational speeds, and pressure coefficients. As part of the experimental design methodology, a full dataset was generated by simulating three-dimensional scale models utilizing computational fluid dynamics (CFD) simulations that were validated by wind tunnel data. This process is multi-objective optimization for optimizing turbine performance. The twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. The group method of data handling (GMDH) algorithm was utilized to model objective functions based on input-output data. The Pareto fronts were plotted using polynomial models obtained from the evolutionary Pareto-based optimization approach (modified NSGA-II), and the optimal commercial points were determined using TOPSIS. A comparison of three- and two-objective optimization data revealed that two-objective optimization data lie within the boundaries of a three-objective optimization problem. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively, using multi-objective optimization. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.
Copyright:
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