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

摘要提案编号： CTS-0328329、CTS-0328314提案类型： 发起人（协作）主要发起人：STEPHEN B。波普，杨培君学院： 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颗粒运动的统计特性，以及基于流体颗粒加速的新的随机模型的发展。 湍流在许多工程学科以及海洋学和大气科学中都很普遍。 拉格朗日观点在描述几种现象（如湍流扩散）以及使用概率密度函数法进行湍流建模中至关重要，该方法已被证明对湍流燃烧特别成功。 尽管最近在该领域取得了进展，但仍然缺乏关于各向异性和不均匀性的重要影响的知识，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大量的拉格朗日数范围内提取DNS的规范流。 新模拟的成功将有助于解决许多缩放问题，从而使严格的发展一类新的随机模型的加速度，张量系数适合各向异性和非均匀流。这项工作预计将导致显着的进步，适用于不同的情况下，如湍流建模：液滴聚结在大气云层中;湍流火焰行为的改进燃烧装置的设计建模;和流体力学模型（小）海洋生物的行为，这对海洋食物链和渔业管理的影响。 至少有两名共同监督的博士生将使用非常大规模的科学计算平台来研究这个项目。这种培训，加上他们在研究过程中积累的知识，将为他们将来在学术界或工业界的职业生涯做好准备。由流体动力学液压学和燃烧等离子体系统项目共同资助

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