A numerical data set will be developed to assist in the design of Savonius wind turbines. The main objective of study is to improve Savonius turbine blade designs to increase torque coefficients, rotational speeds, and pressure coefficients. Simulating 3D models and validating them with wind tunnel data were part of the experimental design methodology. Multi-objective optimization is used to optimize turbine performance. Twist angle, aspect ratio, and overlap ratio are all important factors in determining the optimal torque and power coefficients. Data-driven objective functions were modeled using the group method of data handling (GMDH). Using an evolutionary Pareto-based optimization approach, polynomial models were used to plot Pareto fronts and TOPSIS to calculate optimal commercial points. The torque coefficient, rotational speed, and power coefficient are all improved by 13.74%, 0.071%, and 5.32%, respectively. As a result of the multi-objective optimization of the turbine, some significant characteristics of objective functions were discovered.

Wind energy technology is progressing at an accelerated pace due to the growing global demand for renewable energy, particularly horizontal axis wind turbines (HAWTs). Ducted wind turbines (DWTs) have emerged as a promising enhancement to traditional horizontal axis wind turbines, demonstrating substantial improvements in efficiency and power output. This study presents a comprehensive evaluation of four distinct designs for DWTs, with a focus on critical factors including efficiency, cost-effectiveness, durability, and environmental sustainability. Among the designs analyzed, the Diffuser configuration emerges as the most advantageous in terms of cost, delivering energy more economically than the Nozzle-Diffuser design. The durability analysis underscores the Diffuser design’s superior longevity, indicated by a durability index of 0.9, reflecting its robust construction and low maintenance demands. Additionally, the Diffuser design is shown to be highly environmentally sustainable, achieving an Environmental Impact Score of 0.91, highlighting its minimal carbon footprint and high recyclability. The study concludes that the Diffuser design offers a balanced and effective solution, combining cost efficiency, durability, and environmental considerations while maintaining competitive efficiency levels.

Savonius wind turbines are attractive options due to their simple structure, low noise, appropriate torque, and independence from the wind direction. In this study, a new experimental test is designed to analyze SR3345 and SR5050 blade profiles and to understand their effects on the performance of Savonius wind turbines. The fabricated wind turbine is placed in an open circuit wind tunnel with a velocity of 6 m/s. The power and torque coefficients of the turbine are compared with the two profiles considered based on overlap ratios between 0 and 0.3. Despite its increased stability, the results confirm that the central shaft reduces the power coefficient of the turbine. For blade SR3345, the optimum performance occurs at tip speed ratios of less than one, while for blade SR5050, the reasonable performance occurs at higher tip speed ratios. Blades SR3345 and SR5050 achieve the highest power coefficients at overlap ratios of 0 and 0.18 when a central shaft is present.

Optimizing the design of the exhaust manifold plays a crucial role in enhancing engine efficiency, reducing fuel consumption, and minimizing pollutant emissions in internal combustion engines. This study aims to improve the exhaust manifold geometry of the M3 engine, manufactured by PIDOCO (Pioneer Intelligence Design Origin Co.). The primary design objectives include reducing pressure drop and enhancing flow uniformity across the surface of the monolithic catalyst. To achieve these goals, a modified exhaust manifold geometry for the M3 engine was designed, and Computational Fluid Dynamics (CFD) simulations were conducted to evaluate the proposed designs. The geometries were created using CATIA software, while ANSYS Fluent was employed for CFD analysis. The results indicate that optimizing the exhaust manifold geometry reduces pressure drop by 45%, improves the flow uniformity index by 36%, and leads to a minimum 4.5% reduction in vehicle gaseous emissions.

The optimization of gas turbine combustion chamber design has gained significant importance due to the complexity of combustion processes, temperature distribution, and pollutant emissions. To optimize three key geometric parameters of the combustion chamber simultaneously, a hybrid approach that combines numerical modeling, artificial neural networks (ANNs), and a modified multi-objective genetic algorithm (NSGA-II) is proposed. The reduction of non-volatile particulate matter (nvPM) emissions is regarded as one of the most critical pollution concerns, even though gaseous pollutants such as CO and NOx are also important. To simulate the combustion chamber in the initial phase, a chemical reactor network (CRN) is employed, followed by training the ANN with results from the numerical model. The modified NSGA-II multi-objective genetic algorithm is used to simultaneously optimize the previously mentioned parameters to enhance combustion and thermal performance while minimizing pollutant emissions, particularly nvPM. To identify the optimal final solution, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) applies multi-criteria decision-making. As a result of this approach, CO emissions are reduced by 7.1%, NOx by 4.9%, and nvPM emissions by 16% simultaneously, compared to the initial values. This method can lead to the development of gas turbine combustion chambers with higher efficiency and lower emissions.