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- W4387144484 abstract "Abstract This study aims to optimize the impeller geometry of a backward-curved blade centrifugal fan in a helicopter oil cooler to improve the aerodynamic performance of the impeller. The study consists of three main parts: aerodynamic design and parametrization of the impeller, numerical analysis of impeller performance, and multi-objective design optimization to maximize fan static pressure and total to static isentropic efficiency. In the first part, a one-dimensional impeller design was performed using quasi-experimental methods and a direct optimization method with the multi-objective genetic algorithm, and the impeller geometry was parameterized through a commercial tool. The three-dimensional flow through the impeller was solved in the second part with a commercial computational fluid dynamics tool. The Reynolds-averaged Navier-Stokes equations were solved on a multi-block grid, and a second-order accurate finite volume method is employed. The most appropriate turbulence model and grid size were selected considering time, cost, and fidelity. In the third part, a sensitivity analysis was performed with the design-of-experiment method, and the parameters that affect the objective function most significantly were determined. A multi-objective design optimization based on a non-dominated sorting genetic algorithm was performed with the Kriging response surface method, and Pareto-optimal solutions were obtained. Results show that, at the design point, there is a 9.6% and 0.96% increase in the fan static pressure and total to static isentropic efficiency, respectively. Trained kriging response surface model predicted the fan static pressure and total to static isentropic efficiency with an error of 0.12% and 0.33%, respectively." @default.
- W4387144484 created "2023-09-29" @default.
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- W4387144484 date "2023-06-26" @default.
- W4387144484 modified "2023-10-18" @default.
- W4387144484 title "Aerodynamic Design and Performance Optimization of a Centrifugal Fan Impeller" @default.
- W4387144484 doi "https://doi.org/10.1115/gt2023-101835" @default.
- W4387144484 hasPublicationYear "2023" @default.
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