GOALI: Integrated Experimental and Computational Multi-Zonal Approach to Multiple-Scale Problems: Flow in a Stirred Tank Reactor

GOALI：针对多尺度问题的综合实验和计算多区域方法：搅拌釜反应器中的流动

• 批准号: 9910543
• 负责人: Sivaramakrishna Balachandar
• 金额: $ 36万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9910543&HistoricalAwards=false
• 关键词: GOALI; Integrated; Experimental; Computational; Multi

Abstract - Balachandar - 9910543This project is to develop a new generation of predictive methodology for complex and turbulent multiple-scale flow problems. The PIs are looking for natural separation of the entire flow domain into multiple zones, each characterized by their own range of length and time scales. Different techniques, both experimental and computational, will be employed over the different domains, as appropriate. The goal is the development of generalized data-facing that will permit seamless interfaces between the different techniques across different zones. Data-facing involves matching of flow physics across interfaces between different zones, as well as consistent transfer of numerical data. The methodology to be used involves two techniques: large eddy simulation (LES) of turbulent flow and multi-dimensional velocimetry experiments, in particular, particle image velocimetry (PIV). The approach to dynamic data-facing between parallel PIV experiments and LES computations is the use of conditional statistics, approximated by the stochastic estimation procedure.The above concept of dynamic data-facing between integrated experiments and computations will be applied to the problem of flow in a stirred tank. The mixing of two or more fluids is a complex physical process that is fundamental to a wide range of engineering applications. In many chemical industrial processing units the rate at which mixing can be achieved determines the overall throughput and profitability of the plant. In other devices, such as turbines that rely on the combination of fluid mixtures, the thermal efficiency depends on the quality and speed of mixing in a sensitive way. Scale-up is critical to good plant design. Through integrated PIV and PLIF experiments and large eddy simulations, accurate prediction of macro-scale mixing and the problem of reliable scale-up will be addressed using a range of laboratory to pilot plant scale stirred tank geometries.

摘要-Balachandar -9910543这个项目是为了开发新一代的预测方法，以解决复杂而动荡的多尺度流动问题。 PI正在寻找将整个流域域分为多个区域的自然分离，每个区域都以自己的长度和时间尺度范围为特征。 无论是实验和计算的不同技术，都将在不同的域上采用。 目标是开发广义数据，该数据将允许在不同区域的不同技术之间进行无缝接口。 面向数据涉及跨不同区域之间界面的流体物理匹配，以及数值数据的一致传输。 要使用的方法涉及两种技术：湍流和多维速度测试实验的大型涡流模拟（LES），尤其是粒子图像速度法（PIV）。 平行PIV实验和LES计算之间动态数据针对的方法是使用条件统计数据，通过随机估计程序近似。上述集成实验和计算之间动态数据针对动态数据的概念将应用于流动问题中的流动问题一个搅拌的坦克。 两种或多个流体的混合是一个复杂的物理过程，对广泛的工程应用至关重要。 在许多化学工业加工单元中，可以实现混合的速度决定了植物的总体吞吐量和盈利能力。 在其他设备中，例如依靠流体混合物组合的涡轮机，热效率取决于以敏感方式混合的质量和速度。 扩大对良好的植物设计至关重要。 通过集成的PIV和PLIF实验以及大型涡流模拟，将使用一系列实验室来解决宏观尺度混合的准确预测以及可靠的缩放尺度的问题，以搅拌植物规模搅拌储罐的几何形状。