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Analytical Wind Farm Flow Modeling for Power Production Estimation and Optimization
Analytical Wind Farm Flow Modeling for Power Production Estimation and Optimization
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104747
- ISBN
- 9798290652320
- DDC
- 621.165
- 서명/저자
- Analytical Wind Farm Flow Modeling for Power Production Estimation and Optimization
- 발행사항
- [Sl] : Stanford University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 134 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Gorle, Catherine.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2025.
- 초록/해제
- 요약Wind farm design and control optimization problems involve predicting how wind farm performance and flow physics respond to different atmospheric conditions, namely the ambient mean wind speed and wind direction. In these optimization applications, analytical modeling tools are often used due to their simplicity and low computational cost, which is important when simulating a large number of wind conditions or wind farm configurations. However, these low-fidelity tools also come with a high degree of modeling and parameter error and uncertainty, which can be difficult to precisely characterize over these various inflow conditions. This dissertation addresses some of these challenges associated with analytical wind farm flow modeling along three axes: model development, model calibration, and model correction. Regarding model development, I derive an analytical formulation of wind farm annual energy production for use in gradient-based layout optimization problems. For model calibration, I define a Bayesian inference framework for estimating model parameters with quantified uncertainty. As for model correction, I describe a multi-fidelity approach to wind farm power prediction that incorporates information from high-fidelity computational fluid dynamics simulations across a range of inflow conditions. Overall, these separate projects aim to improve the efficiency and robustness of analytical wind farm modeling for optimal design and control applications.
- 일반주제명
- Turbines
- 일반주제명
- Wind power
- 일반주제명
- Aerodynamics
- 일반주제명
- Layouts
- 일반주제명
- Energy
- 일반주제명
- Parameter estimation
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202104747
■006m o d
■007cr#unu||||||||
■020 ▼a9798290652320
■035 ▼a(MiAaPQ)AAI32149764
■035 ▼a(MiAaPQ)Stanfordzq935pp2877
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.165
■1001 ▼aLoCascio, Michael James.
■24510▼aAnalytical Wind Farm Flow Modeling for Power Production Estimation and Optimization
■260 ▼a[Sl]▼bStanford University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a134 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Gorle, Catherine.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2025.
■520 ▼aWind farm design and control optimization problems involve predicting how wind farm performance and flow physics respond to different atmospheric conditions, namely the ambient mean wind speed and wind direction. In these optimization applications, analytical modeling tools are often used due to their simplicity and low computational cost, which is important when simulating a large number of wind conditions or wind farm configurations. However, these low-fidelity tools also come with a high degree of modeling and parameter error and uncertainty, which can be difficult to precisely characterize over these various inflow conditions. This dissertation addresses some of these challenges associated with analytical wind farm flow modeling along three axes: model development, model calibration, and model correction. Regarding model development, I derive an analytical formulation of wind farm annual energy production for use in gradient-based layout optimization problems. For model calibration, I define a Bayesian inference framework for estimating model parameters with quantified uncertainty. As for model correction, I describe a multi-fidelity approach to wind farm power prediction that incorporates information from high-fidelity computational fluid dynamics simulations across a range of inflow conditions. Overall, these separate projects aim to improve the efficiency and robustness of analytical wind farm modeling for optimal design and control applications.
■590 ▼aSchool code: 0212.
■650 4▼aTurbines
■650 4▼aWind power
■650 4▼aAtmospheric boundary layer
■650 4▼aAerodynamics
■650 4▼aLayouts
■650 4▼aEnergy
■650 4▼aParameter estimation
■690 ▼a0791
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358755▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
