Multiphase Models for CO2 Cleanup: Heat and Mass Transfer in Fluid-Particle Suspensions through Direct Numerical Simulation and Laser-Based Measurements

二氧化碳净化的多相模型：通过直接数值模拟和基于激光的测量来研究流体颗粒悬浮液中的传热和传质

• 批准号: 1034307
• 负责人: Shankar Subramaniam
• 金额: $ 35万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1034307&HistoricalAwards=false
• 关键词: Multiphase; Models; CO2; Cleanup; Heat

1034307SubramanianPredictive models for heat and mass transfer in multiphase flows are needed for implementing carbon-neutral energy-generation technologies such as chemical looping combustion with coal or biomass, and CO2 capture from the flue gas of power plants. Recent direct numerical simulations (DNS) of flow past fixed particle assemblies suggest that heat and mass transfer models currently in use do not accurately capture relevant phenomena. To address this shortcoming, this research will investigate heat and mass transfer in fluid-particle suspensions using a combination of DNS and laser measurements of velocity and scalar fields, resulting in improved models for fluid-particle suspension flows. The improved and validated models for heat transfer can then be used by industry to evaluate and optimize fluidized bed design for CO2 capture.Intellectual Merit: Current models for heat and mass transfer in two-fluid CFD models have not been fully validated. Specifically, unknown terms in the transport equations have not been quantified. This research addresses this need by quantifying these unknown terms using laser measurements and DNS. This new understanding will lead to improved models and simulation methodologies for interphase heat and mass transfer in fluid-particle suspensions. The validity of these models will be verified in a riser flow configuration. Broader Impacts: Improvements to heat and mass transfer models will result in more accurate simulation of fluidized beds for chemical looping combustion and CO2 capture using dry sorbents. This will enable engineers to rapidly evaluate the viability of proposed designs. The project will leverage various institutional outreach programs to increase participation of students including those from underrepresented groups.

1034307 Subramanian多相流中传热和传质的预测模型需要实施碳中性能源生产技术，如煤或生物质的化学链燃烧，以及从发电厂的烟道气中捕获CO2。最近的直接数值模拟（DNS）的流动过去的固定粒子组件表明，目前使用的传热传质模型不能准确地捕捉相关的现象。为了解决这一缺点，本研究将使用DNS和激光测量速度和标量场的组合来研究流体-颗粒悬浮液中的传热和传质，从而改进流体-颗粒悬浮液流的模型。 经过改进和验证的传热模型可用于工业评估和优化流化床CO2捕集设计。智力优势：目前双流体CFD模型中的传热和传质模型尚未得到充分验证。具体而言，输运方程中的未知项尚未量化。本研究通过使用激光测量和DNS量化这些未知术语来满足这一需求。这一新的认识将导致改进的模型和模拟方法，在流体颗粒悬浮液相间的热量和质量传递。这些模型的有效性将在立管流动配置中得到验证。更广泛的影响：改进传热和传质模型将导致更准确的模拟流化床化学链燃烧和二氧化碳捕获使用干吸附剂。这将使工程师能够快速评估拟议设计的可行性。该项目将利用各种机构外展计划，以增加学生的参与，包括那些来自代表性不足的群体。