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

这个学院早期职业发展（Career）研究项目建议利用蒸汽压缩系统面向控制建模的相对突破的发展，这是最近由PI和他的同事通过密切的工业合作开发的。这种新颖的建模技术给出了一个典型周期的低阶动态模型，该模型仍然保留了物理系统特征，并使基于模型的控制器的开发更加通用，适应性强，并且对环境条件的变化具有鲁棒性。该建模范例将应用于亚临界和跨临界空调系统，并用于开发基于模型的控制策略和诊断算法。提出了一种基于双Youla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。定制仿真工具将用于为控制概念的初步测试提供虚拟环境，而实验测试将补充评估拟议方法的仿真研究。该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，同时开发适用于这些系统的非线性耦合动力学的基于模型的先进控制和诊断算法。将特别关注基于跨临界二氧化碳的系统，由于对环境的影响较小，它是当前空调和制冷系统的一个有吸引力的替代方案。在满足制冷量变化需求的同时，提出了系统效率最大化的非线性控制策略。此外，诊断算法将用于识别在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，并有可能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著改善经济和环境。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历（reu）。结合行业reu，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移的双重职责。同时，PI将与地方行政部门合作，提高少数民族对现有部门REU项目的参与程度。

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.