{"title":"转子优化的数据驱动降阶模型","authors":"N. Peters, Christopher J. Silva, J. Ekaterinaris","doi":"10.5194/wes-8-1201-2023","DOIUrl":null,"url":null,"abstract":"Abstract. For rotor design applications, such as wind turbine rotors or urban air mobility (UAM) rotorcraft and flying-car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving reasonably accurate but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed-pressure predictions on rotor blades subjected to topology change due to variations in the twist and taper ratio. Rotor twist, θ, was varied between 0, 10, 20, and 30∘, while the taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases: a high-pitch or a high-thrust rotor in hover, a low-pitch or a low-thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed that reasonably accurate distributed-load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 h on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotors in forward flight, cost was reduced from 20 h on 440 cores to less than a second on a single core. The POD ROM was used to carry out a design optimization of the rotor such that the figure of merit was maximized for hovering-rotor cases and the lift-to-drag effective ratio was maximized in forward flight.\n","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A data-driven reduced-order model for rotor optimization\",\"authors\":\"N. Peters, Christopher J. Silva, J. Ekaterinaris\",\"doi\":\"10.5194/wes-8-1201-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. For rotor design applications, such as wind turbine rotors or urban air mobility (UAM) rotorcraft and flying-car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving reasonably accurate but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed-pressure predictions on rotor blades subjected to topology change due to variations in the twist and taper ratio. Rotor twist, θ, was varied between 0, 10, 20, and 30∘, while the taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases: a high-pitch or a high-thrust rotor in hover, a low-pitch or a low-thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed that reasonably accurate distributed-load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 h on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotors in forward flight, cost was reduced from 20 h on 440 cores to less than a second on a single core. The POD ROM was used to carry out a design optimization of the rotor such that the figure of merit was maximized for hovering-rotor cases and the lift-to-drag effective ratio was maximized in forward flight.\\n\",\"PeriodicalId\":46540,\"journal\":{\"name\":\"Wind Energy Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wind Energy Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/wes-8-1201-2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wind Energy Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/wes-8-1201-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
A data-driven reduced-order model for rotor optimization
Abstract. For rotor design applications, such as wind turbine rotors or urban air mobility (UAM) rotorcraft and flying-car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving reasonably accurate but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed-pressure predictions on rotor blades subjected to topology change due to variations in the twist and taper ratio. Rotor twist, θ, was varied between 0, 10, 20, and 30∘, while the taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases: a high-pitch or a high-thrust rotor in hover, a low-pitch or a low-thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed that reasonably accurate distributed-load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 h on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotors in forward flight, cost was reduced from 20 h on 440 cores to less than a second on a single core. The POD ROM was used to carry out a design optimization of the rotor such that the figure of merit was maximized for hovering-rotor cases and the lift-to-drag effective ratio was maximized in forward flight.