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• W3204647123 startingPage "117603" @default.
• W3204647123 abstract "The volumetric efficiency of internal combustion engines quantifies the effectiveness of the admission process, whereas the thermal efficiency quantifies the amount of work generated based on the total energy released by the fuel. A method to optimize these efficiencies is by changing the valve timing. In this study, the parallelized particle swarm optimization method is implemented to maximize the volumetric efficiency, and its performance is compared with the differential evolution method. Calculations are performed using a parallelized computational code comprising a one-dimensional model for unsteady compressible gas flow in the intake and exhaust ducts, a single-zone combustion model for the in-cylinder process, and optimization routines based on particle swarm optimization and differential evolution techniques. In both optimizations, the design variables are the opening and closing angles of the intake and exhaust valves. Both optimization methods reach the same maximum volumetric efficiency, but the particle swarm optimization presents a smaller deviation from the mean values and better performance in terms of computational time. In addition, a higher volumetric efficiency is associated with higher values of the exhaust valve opening angle and intake valve closing angle at speeds of 2000 and 3000 rpm, respectively. Finally, it can be concluded that both optimization methods can optimize the volumetric efficiency and reach the same maximum value; however, the particle swarm optimization performs better in terms of the computational time and exhibits fewer deviations from the mean values." @default.
• W3204647123 created "2021-10-11" @default.
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• W3204647123 date "2021-11-01" @default.
• W3204647123 modified "2023-10-17" @default.
• W3204647123 title "Maximizing volumetric efficiency using stochastic optimization techniques for internal combustion engines" @default.
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• W3204647123 doi "https://doi.org/10.1016/j.applthermaleng.2021.117603" @default.
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