Time Accurate Prediction of Fluid Motion

流体运动的时间精确预测

• 批准号: 2110379
• 负责人: William Layton
• 金额: $ 42.45万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110379&HistoricalAwards=false
• 关键词: Time; Accurate; Prediction; Fluid; Motion

Fluids transport and mix heat, chemical species, and contaminants. Accurate simulation of fluid velocities for low viscosity fluids and for longer times is essential for safety, critical prediction and design in engineering and science. Accurate flow predictions are key to limiting damage of hurricanes (estimated to be hundreds of billions of dollars in typical years) and to energy efficiency optimization (85% of US energy is generated by combustion for which accurate simulation of turbulent mixing is critical). Fundamental barriers to accurate flow prediction will be addressed in this research. This research also develops expertise of PhD students who will contribute to the next generation of applied and computational mathematicians to address future challenges. This project will support six graduate students per year and one undergraduate per year for each of the three years of the project.This project will develop time accurate methods in computational fluid dynamics. Time accurate prediction must improve current turbulence models because they are only satisfactory at statistical equilibrium. The proposed research will extend them to non-stationary turbulence, simplify the models and reduce the number of calibration parameters by aligning them more closely to the true energy flow between means and fluctuations. Since fluid flow has complex dynamics, pushing out the limited predictability horizon requires reliable and efficient ensemble simulations. The research will develop algorithms allowing larger ensembles without sacrificing accuracy in realizations. Effective, adaptive time discretizations are under used in computational fluid dynamics. This research will develop new time stepping methods of low computational, space and cognitive complexity that are stable, accurate and easily introduced into complex codes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

流体传输并混合热量，化学物种和污染物。精确模拟低粘度流体和更长时间的流体速度对于安全，批判性预测和设计至关重要。准确的流动预测是限制飓风的损害的关键（典型几年中估计为数千亿美元）和能源效率优化（美国85％的能源是通过燃烧产生的，对于湍流混合的准确模拟至关重要）。这项研究将解决准确流动预测的基本障碍。这项研究还开发了博士生的专业知识，他们将为下一代应用和计算数学家做出贡献，以应对未来的挑战。 该项目将每年为该项目的三年中的每一个支持六名研究生，每年一名本科生。该项目将开发计算流体动力学的时间准确方法。时间准确的预测必须改善当前的湍流模型，因为它们仅在统计平衡下令人满意。拟议的研究将将它们扩展到非平稳的湍流，简化模型并通过使它们与均值和波动之间的真实能量保持一致来减少校准参数的数量。由于流体流动具有复杂的动力学，因此推出有限的可预测性范围需要可靠，有效的集合模拟。这项研究将开发算法，允许更大的合奏，而无需牺牲实现的准确性。有效的自适应时间离散化在计算流体动力学中使用。这项研究将开发出低计算，空间和认知复杂性的新时间步进方法，这些方法稳定，准确且容易被引入复杂的代码。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响来通过评估来进行评估。