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 该项目旨在开发新一代复杂、湍流多尺度流动问题的预测方法。 PI 正在寻求将整个流域自然分离为多个区域，每个区域都有自己的长度和时间尺度范围。 将酌情在不同领域采用不同的实验和计算技术。 目标是开发通用的数据面向，这将允许跨不同区域的不同技术之间的无缝接口。 面向数据涉及不同区域之间界面上的流动物理匹配，以及数值数据的一致传输。 所使用的方法涉及两种技术：湍流的大涡模拟（LES）和多维测速实验，特别是粒子图像测速（PIV）。 并行 PIV 实验和 LES 计算之间动态数据面对的方法是使用条件统计，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面对的概念将应用于搅拌罐中的流动问题。 两种或多种流体的混合是一个复杂的物理过程，是广泛的工程应用的基础。 在许多化学工业加工装置中，实现混合的速率决定了工厂的总体产量和盈利能力。 在其他设备中，例如依赖于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。 扩大规模对于良好的工厂设计至关重要。 通过集成的 PIV 和 PLIF 实验以及大涡模拟，宏观尺度混合的准确预测和可靠放大的问题将使用一系列实验室到中试工厂规模的搅拌罐几何形状来解决。