CAREER: Model-Based Control and Diagnostics for Transcritical CO2 Vapor Compression Cycle Systems

职业：跨临界二氧化碳蒸汽压缩循环系统的基于模型的控制和诊断

• 批准号: 0644363
• 负责人: Bryan Rasmussen
• 金额: $ 40万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644363&HistoricalAwards=false
• 关键词: CAREER; Model; Based; Control; Diagnostics

This Faculty Early Career Development (CAREER)research proposes to project leverages the development of a relative breakthrough in control-oriented modeling of vapor compression systems, which has been recently developed by the PI and his colleagues with close industrial collaboration. This novel modeling technique gives a low order dynamic model of a typical cycle that still retains physical system characteristics and enables the development of model-based controllers that are more universal, adaptable, and robust to changes in environmental conditions. This modeling paradigm will be applied to both subcritical and transcritical air conditioning systems, and utilized to develop model-based control strategies and diagnostic algorithms. A dual Youla parameter based interpolation framework is proposed for the formation of gain scheduled control and fault detection strategies. This approach permits interpolation between individually tuned controllers while ensuring stable transitions. Custom simulation tools will be used to provide a virtual environment for initial testing of control concepts, while experimental tests will complement the simulation studies in the evaluation of the proposed approaches. This project proposes to apply a novel dynamic modeling paradigm to vapor compression cycle systems, while developing advanced model based control and diagnostic algorithms appropriate to the nonlinear, coupled dynamics of these systems. Particular attention will be given to transcritical CO2 based systems that are an attractive alternative to current air conditioning and refrigeration systems due to lower environmental impact. Nonlinear control strategies are proposed to maximize system efficiency while simultaneously satisfying changing demands for cooling capacity. Additionally, diagnostic algorithms will be employed to identify soft system faults that precede catastrophic system failure. The proposed work has broad application in automotive, aerospace, and residential energy industries, and the potential for dramatic economic and environmental improvements by significantly reducing energy usage, component failure, and the negative impacts of Hydrofluorocarbon (HFC) refrigerants in terms of global warming. Integrated with the research efforts is an educational initiative that seeks to increase minority participation in undergraduate research experiences (REUs). A combination of industry REUs, internships, and student-led short courses will accomplish dual duties of human capital development and technology transfer. Concurrently, the PI will work with local administration to improve minority participation in existing departmental REU programs.

这项教师早期职业发展（CAREER）研究建议项目利用蒸汽压缩系统的控制导向建模的相对突破的发展，该系统最近由PI及其同事通过密切的工业合作开发。这种新的建模技术给出了一个低阶的动态模型的一个典型的周期，仍然保留物理系统的特性，并使基于模型的控制器，更通用，适应性强，鲁棒性强的环境条件的变化的发展。这种建模范式将被应用到亚临界和跨临界空调系统，并利用开发基于模型的控制策略和诊断算法。提出了一种基于双尤拉参数的插值框架，用于形成增益调度控制和故障检测策略。这种方法允许在单独调谐的控制器之间进行插值，同时确保稳定的过渡。将使用定制的模拟工具为控制概念的初步测试提供虚拟环境，而实验测试将在评估拟议方法时补充模拟研究。本项目提出将一种新的动态建模范式应用于蒸汽压缩循环系统，同时开发适合于这些系统的非线性耦合动态的基于先进模型的控制和诊断算法。将特别关注跨临界CO2系统，由于对环境的影响较低，该系统是当前空调和制冷系统的有吸引力的替代品。 提出了非线性控制策略，以最大限度地提高系统效率，同时满足不断变化的需求，冷却能力。此外，将采用诊断算法来识别灾难性系统故障之前的软系统故障。拟议的工作在汽车，航空航天和住宅能源行业具有广泛的应用，并通过显着减少能源使用，组件故障和氢氟碳化合物（HFC）制冷剂在全球变暖方面的负面影响来显着改善经济和环境的潜力。与研究工作相结合的是一项教育倡议，旨在增加少数民族参与本科生研究经验（雷乌斯）。 结合行业雷乌斯、实习和学生主导的短期课程，将完成人力资本开发和技术转让的双重职责。同时，PI将与地方行政部门合作，以提高少数民族参与现有的部门REU方案。