2021
DOI: 10.3390/machines9100218
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Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

Abstract: The radial-flow turbine, a key component of the supercritical CO2 ( S-CO2) Brayton cycle, has a significant impact on the cycle efficiency. The inlet volute is an important flow component that introduces working fluid into the centripetal turbine. In-depth research on it will help improve the performance of the turbine and the entire cycle. This article aims to improve the volute flow capacity by optimizing the cross-sectional geometry of the volute, thereby improving the volute performance, both at design and… Show more

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Cited by 4 publications
(2 citation statements)
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“…Compared with genetic algorithms, the ISAA method saves 59.6% of computational time and 41.5% of computational time compared to the SAA method. [69]. First, they carried out parameter sensitivity analysis based on the Gaussian process surrogate model, and then realized the Bayesian optimization (BO) process.…”
Section: S-co2 Power Cyclementioning
confidence: 99%
“…Compared with genetic algorithms, the ISAA method saves 59.6% of computational time and 41.5% of computational time compared to the SAA method. [69]. First, they carried out parameter sensitivity analysis based on the Gaussian process surrogate model, and then realized the Bayesian optimization (BO) process.…”
Section: S-co2 Power Cyclementioning
confidence: 99%
“…Yang et al [9] analyzed the effects of different aspect ratio distribution regimes for the same sectional shape on the turbocharger turbine performance under pulsating flow conditions through experiments and CFD simulations. Bian et al [10] optimized the geometric parameters (global spread angle, corner radius factor, outlet size factor) of a trapezoid section volute using the Bayesian optimization algorithm through CFD simulations, which resulted in a significant improvement of the flow loss inside the volute and the flow distortion at the volute outlet. Furthermore, the outlet size factor was found to be the most influential of the three optimization factors on the optimization objective.…”
Section: Introductionmentioning
confidence: 99%