Collaborative Research: Lagrangian Statistics and Acceleration in Turbulent Shear Flows: Simulation and Modeling

合作研究：湍流剪切流中的拉格朗日统计和加速：模拟和建模

• 批准号: 0328314
• 负责人: Pui-Kuen Yeung
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-12-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0328314&HistoricalAwards=false
• 关键词: Collaborative; Research; Lagrangian; Statistics; Acceleration

ABSTRACTPROPOSAL NO.: CTS-0328329, CTS-0328314PROPOSAL TYPE: INVESTIGATOR INITIATED (COLLABORATIVE)PRINCIPAL INVESTIGATORS: STEPHEN B. POPE, PUI-KUEN YEUNGINSTITUTION: CORNELL UNIVERSITY, GEORGIA INST. TECH.LAGRANGIAN STATISTICS AND ACCELERATION IN TURBULENT SHEAR FLOWS: SIMULATION AND MODELINGThis research is focused on the systematic use of direct numerical simulations (DNS) to study the statistical properties of Lagrangian fluid particle motion in turbulent shear flows, and the development of new stochastic models based on the fluid particle acceleration. Turbulent fluid flows are prevalent in several engineering disciplines as well as in oceanography and atmospheric sciences. A Lagrangian viewpoint is essential in describing several phenomena, such as turbulent dispersion, and also in turbulence modeling using the probability density function method, which has proven particularly successful for turbulent combustion. Despite recent progress in the field there is still a lack of knowledge concerning important effects of anisotropy and inhomogeneity, which are generally present in applications. New and comprehensive Lagrangian statistics covering a substantial Reynolds-number range are to be extracted from DNS of canonical flows. Success of the new simulations will contribute to the resolution of many scaling issues and hence enable the rigorous development of a new class of stochastic models for the acceleration, with tensor coefficients appropriate to anisotropic and inhomogeneous flows. This work is expected to lead to significant advances in turbulence modeling applicable in diverse contexts such as: droplet coalescence in atmospheric clouds; modeling of turbulent flame behavior in the design of improved combustion devices; and fluid-mechanical models of (small) marine organism behavior which has implications for the oceanic food chain and fishery management. At least two jointly supervised PhD students will work on this project using very large-scale scientific computing platforms. This training, together with the knowledge they accumulate in the research process, will prepare them well for future careers in academia or industry.*co-funded jointly by the Fluid Dynamics & Hydraulics, and Combustion & Plasma Systems programs

摘要项目编号：CTS-0328329，CTS-0328314项目类型：调查员发起(合作)的主要调查人员：斯蒂芬·B·波普，培权YEUNGINSTUT：康奈尔大学，佐治亚州Inst.湍流剪切流中的拉格朗日统计和加速：模拟和建模本研究的重点是系统地使用直接数值模拟(DNS)来研究湍流剪切流中拉格朗日流体质点运动的统计特性，以及基于流体质点加速度的新的随机模型的发展。湍流在几个工程学科以及海洋学和大气科学中很普遍。拉格朗日观点在描述几种现象时是必不可少的，例如湍流弥散，以及在使用概率密度函数方法进行湍流模拟时也是如此，该方法已被证明对湍流燃烧特别成功。尽管该领域最近取得了进展，但对于各向异性和非均质性的重要影响，人们仍然缺乏了解，这些影响通常存在于应用中。新的和全面的拉格朗日统计涵盖了相当大的雷诺数范围，将从正则流的域名中提取。新模拟的成功将有助于解决许多尺度问题，从而能够严格开发一类新的随机加速模型，其张量系数适用于各向异性和非均匀流动。这项工作预计将在适用于各种情况的湍流模拟方面取得重大进展，例如：大气云中的液滴合并；改进燃烧装置设计中的湍流火焰行为模拟；以及对海洋食物链和渔业管理有影响的(小型)海洋生物行为的流体力学模型。至少两名联合指导的博士生将使用超大规模科学计算平台参与这个项目。这种培训，加上他们在研究过程中积累的知识，将为他们未来在学术界或行业的职业生涯做好准备。*由流体动力学和液压以及燃烧和等离子系统项目共同资助