CAREER: Model-Based Control and Diagnostics for Transcritical CO2 Vapor Compression Cycle Systems
CAREER: Model-Based Control and Diagnostics for Transcritical CO2 Vapor Compression Cycle Systems
批准号:
0644363
负责人:
Bryan Rasmussen
金额:
$40.0万
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
项目状态:
已结题
起止时间:
2007-03-01 至 2013-02-28
中文摘要
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英文摘要
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.
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Distributed Model Predictive Control for Building Energy Systems
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财政年份:2016
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负责人:Bryan Rasmussen
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依托单位:
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