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An Les-Based Multi-Fidelity Framework for Wind Loading Prediction
An Les-Based Multi-Fidelity Framework for Wind Loading Prediction
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152114
- ISBN
- 9798384339403
- DDC
- 720
- 서명/저자
- An Les-Based Multi-Fidelity Framework for Wind Loading Prediction
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 119 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Gorle, Catherine;Fringer, Oliver;Kitanidis, Peter.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약According to recent UN estimates, 68% of the world population will live in urban areas by 2050. In response to the escalating demand for urban housing, the US estate development sector has undergone a transformation over the last three decades. A deliberate reduction in the construction of low-rise buildings has been accompanied by a threefold increase in the development of both mid and high-rise structures. As of November 2019, a noteworthy 11% of residential buildings under development in the United States were high-rise. This data highlights a significant trend in the industry, indicating a strategic shift towards vertical development to optimize land use in densely populated urban areas. Apart from the spatial advantages that high-rise buildings offer, it is crucial to acknowledge the challenges associated with their construction. Specifically, the issue of wind loading, stemming from the substantial height of these structures, emerges as a critical factor in guaranteeing the safety of occupants. As these buildings reach greater heights, the impact of wind forces becomes more pronounced, necessitating a meticulous evaluation of wind loading.As prescribed by standards and codes, wind loading on structures is assessed through wind tunnel testing. However, while this approach is in accordance with regulatory guidelines, it is crucial to acknowledge certain limitations associated with wind tunnel testing. Primarily, one notable drawback is the inherent limitation on the amount of data that can be gathered. Sensors can only be strategically placed at discrete points, constraining the comprehensiveness of the data collection process. This limitation may impact the accuracy of the overall assessment. Moreover, the cost of wind tunnel testing can escalate significantly, especially when multiple building layouts need to be evaluated. This financial consideration adds a layer of complexity to the decision-making process, potentially influencing the feasibility of conducting comprehensive tests. Another significant constraint lies in the fact that wind tunnel testing can only investigate idealized conditions. The experimental characterization of wind loading may encounter challenges in the presence of complex terrain morphology and non-neutral boundary layers, leading to deviations between experimental and on-site conditions.An alternative and increasingly viable tool for wind loading predictions is represented by Computational Fluid Dynamics (CFD). With the current availability of computational power, running high-fidelity Large Eddy Simulation (LES) has become much more cost effective, reaching a point where even coarse simulations can be completed within one or two days. CFD offers distinct advantages over wind tunnel testing, addressing some of the limitations inherent in the traditional approach. Unlike wind tunnel testing, CFD simulations can be executed at full scale, providing a more accurate representation of real-world conditions. The flexibility of CFD allows for the incorporation of complex computational domains, overcoming the constraint of evaluating wind loading only on scaled-down structures. One of the notable strengths of CFD lies in its ability to offer virtually infinite resolution, providing insightful data on flow patterns and consequently offering a comprehensive understanding of the wind loading dynamics.,Furthermore, CFD eliminates the need for additional prototyping costs. Evaluating various design solutions can be seamlessly integrated into the computational model without the necessity of physically printing 3D building prototypes. Despite the attractiveness of CFD, its routine application for wind loading predictions faces challenges. One obstacle is that existing codes and standards often do not permit its use, creating a barrier to widespread adoption. Additionally, the computational time required for running high-resolution LES can be substantial, reaching the order of millions of CPU hours. Overcoming these challenges would be pivotal in fully realizing the potential of CFD as a sophisticated and efficient tool for predicting wind loading in structural design.Building confidence in CFD, particularly LES, as a reliable tool for predicting wind loading is crucial for its widespread adoption in industry standards and codes. While existing research has demonstrated LES accuracy in predicting mean and root-mean-square pressure coefficients, there is a noticeable gap in studies validating its readiness for predicting wind loading on building surfaces. The primary objective of this thesis is to contribute to the growing confidence in LES by utilizing it to predict peak pressure coefficients at wind directions not perpendicular to the building surfaces. This involves validating LES simulations of the flow around a high-rise building by comparing the results with wind tunnel experiments. The results in this thesis indicate that LES is capable to accurately predict peak wind loading in critical areas of the building facades as long as proper boundary conditions and mesh resolutions are used and sufficient computational resources are available.While predicting peak pressure coefficients is crucial for Large Eddy Simulation (LES) in design procedures, the computational demands can be prohibitive. Achieving accurate predictions for peak pressure with LES simulation may require computational time in excess of a million CPU hours. Considering the need for simulations at every 10 degrees for a full wind rose evaluation, employing LES becomes impractical. This thesis addresses this challenge by aiming to significantly reduce the computational effort required for wind loading predictions across the entire wind rose. Two LES-based multi-fidelity frameworks, utilizing different surrogate models-kriging and neural networks- are explored to cut the computation cost in half. The results demonstrate the effectiveness of these frameworks in achieving substantial cost reduction while retaining accuracy in predicting wind loading.
- 일반주제명
- Built environment
- 일반주제명
- Building facades
- 일반주제명
- Fluid dynamics
- 일반주제명
- Buildings
- 일반주제명
- Neural networks
- 일반주제명
- High rise buildings
- 일반주제명
- Geometry
- 일반주제명
- Atmospheric sciences
- 일반주제명
- Fluid mechanics
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384339403
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■035 ▼a(MiAaPQ)Stanfordbp063cg7009
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a720
■1001 ▼aCiarlatani, Mattia Fabrizio.
■24513▼aAn Les-Based Multi-Fidelity Framework for Wind Loading Prediction
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a119 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Gorle, Catherine;Fringer, Oliver;Kitanidis, Peter.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aAccording to recent UN estimates, 68% of the world population will live in urban areas by 2050. In response to the escalating demand for urban housing, the US estate development sector has undergone a transformation over the last three decades. A deliberate reduction in the construction of low-rise buildings has been accompanied by a threefold increase in the development of both mid and high-rise structures. As of November 2019, a noteworthy 11% of residential buildings under development in the United States were high-rise. This data highlights a significant trend in the industry, indicating a strategic shift towards vertical development to optimize land use in densely populated urban areas. Apart from the spatial advantages that high-rise buildings offer, it is crucial to acknowledge the challenges associated with their construction. Specifically, the issue of wind loading, stemming from the substantial height of these structures, emerges as a critical factor in guaranteeing the safety of occupants. As these buildings reach greater heights, the impact of wind forces becomes more pronounced, necessitating a meticulous evaluation of wind loading.As prescribed by standards and codes, wind loading on structures is assessed through wind tunnel testing. However, while this approach is in accordance with regulatory guidelines, it is crucial to acknowledge certain limitations associated with wind tunnel testing. Primarily, one notable drawback is the inherent limitation on the amount of data that can be gathered. Sensors can only be strategically placed at discrete points, constraining the comprehensiveness of the data collection process. This limitation may impact the accuracy of the overall assessment. Moreover, the cost of wind tunnel testing can escalate significantly, especially when multiple building layouts need to be evaluated. This financial consideration adds a layer of complexity to the decision-making process, potentially influencing the feasibility of conducting comprehensive tests. Another significant constraint lies in the fact that wind tunnel testing can only investigate idealized conditions. The experimental characterization of wind loading may encounter challenges in the presence of complex terrain morphology and non-neutral boundary layers, leading to deviations between experimental and on-site conditions.An alternative and increasingly viable tool for wind loading predictions is represented by Computational Fluid Dynamics (CFD). With the current availability of computational power, running high-fidelity Large Eddy Simulation (LES) has become much more cost effective, reaching a point where even coarse simulations can be completed within one or two days. CFD offers distinct advantages over wind tunnel testing, addressing some of the limitations inherent in the traditional approach. Unlike wind tunnel testing, CFD simulations can be executed at full scale, providing a more accurate representation of real-world conditions. The flexibility of CFD allows for the incorporation of complex computational domains, overcoming the constraint of evaluating wind loading only on scaled-down structures. One of the notable strengths of CFD lies in its ability to offer virtually infinite resolution, providing insightful data on flow patterns and consequently offering a comprehensive understanding of the wind loading dynamics.,Furthermore, CFD eliminates the need for additional prototyping costs. Evaluating various design solutions can be seamlessly integrated into the computational model without the necessity of physically printing 3D building prototypes. Despite the attractiveness of CFD, its routine application for wind loading predictions faces challenges. One obstacle is that existing codes and standards often do not permit its use, creating a barrier to widespread adoption. Additionally, the computational time required for running high-resolution LES can be substantial, reaching the order of millions of CPU hours. Overcoming these challenges would be pivotal in fully realizing the potential of CFD as a sophisticated and efficient tool for predicting wind loading in structural design.Building confidence in CFD, particularly LES, as a reliable tool for predicting wind loading is crucial for its widespread adoption in industry standards and codes. While existing research has demonstrated LES accuracy in predicting mean and root-mean-square pressure coefficients, there is a noticeable gap in studies validating its readiness for predicting wind loading on building surfaces. The primary objective of this thesis is to contribute to the growing confidence in LES by utilizing it to predict peak pressure coefficients at wind directions not perpendicular to the building surfaces. This involves validating LES simulations of the flow around a high-rise building by comparing the results with wind tunnel experiments. The results in this thesis indicate that LES is capable to accurately predict peak wind loading in critical areas of the building facades as long as proper boundary conditions and mesh resolutions are used and sufficient computational resources are available.While predicting peak pressure coefficients is crucial for Large Eddy Simulation (LES) in design procedures, the computational demands can be prohibitive. Achieving accurate predictions for peak pressure with LES simulation may require computational time in excess of a million CPU hours. Considering the need for simulations at every 10 degrees for a full wind rose evaluation, employing LES becomes impractical. This thesis addresses this challenge by aiming to significantly reduce the computational effort required for wind loading predictions across the entire wind rose. Two LES-based multi-fidelity frameworks, utilizing different surrogate models-kriging and neural networks- are explored to cut the computation cost in half. The results demonstrate the effectiveness of these frameworks in achieving substantial cost reduction while retaining accuracy in predicting wind loading.
■590 ▼aSchool code: 0212.
■650 4▼aBuilt environment
■650 4▼aBuilding facades
■650 4▼aFluid dynamics
■650 4▼aAtmospheric boundary layer
■650 4▼aBuildings
■650 4▼aNeural networks
■650 4▼aHigh rise buildings
■650 4▼aGeometry
■650 4▼aAtmospheric sciences
■650 4▼aFluid mechanics
■690 ▼a0800
■690 ▼a0725
■690 ▼a0204
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162934▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
