CAREER:Sensors for Quantification of Degradation in Polymer Electrolyte Fuel Cells

摘要：Matthew Mench机构：宾夕法尼亚州立大学提案编号：0644811标题：职业：聚合物电解质燃料电池降解定量传感器智力价值许多复杂的系统遭受逐渐退化，可能导致灾难性的故障。 由于退化的时间尺度相对较慢，因此这些类型的异常故障几乎不可能在早期阶段用常规感测技术检测到。 PI正在计划一种方法，以便能够早期检测和量化聚合物电解质燃料电池（PEFC）中潜在的灾难性演变故障。目标是在实施异常缓解和适度降级策略之前进行早期检测，从而扩展服务可用性、平均性能并实现更积极的初始设计。 这一目标对于实现目前限制PEFC的延长寿命至关重要。实现这种感测能力的方法学上类似于心电图，其中心脏对外部应力的响应的时间序列数据用于快速诊断已经演变多年的疾病。这种感测和量化方法通过快速时间尺度刺激响应方法提供相对缓慢变化的异常的早期诊断。 在选定的外部施加的刺激，时间序列数据被划分成一个响应向量，使用符号动力学。 将动态响应向量特性与标称响应向量进行比较以精确地量化异常水平。 这种早期的故障检测技术将使几乎察觉不到的慢时间尺度的异常被量化在线快速的时间尺度，以及之前发生重大degradations.Broader影响：更广泛的影响，这项研究应有助于产生的基础设施和知识基础需要使无处不在的PEFC的实施，并减少美国长期依赖化石燃料。 CO传感器将在Nuvera和技术开发公司TA D的合作下开发成原型，以最大限度地发挥影响。在教育方面，该计划将使数百名学生，从高中到研究生院，燃料电池技术。 三个博士学生将直接参与，每年40名本科生将参加一个升级的高级课程，包括工业扬声器和与亚利桑那大学的合作。 目前正在编写的教科书也将受益于这项研究的成果。 高中学生和教师将参加一个暑期项目，并将开发一个新的研究生课程。总体而言，综合研究和教育计划将有助于建立一个永久性的基础设施，以解决目前阻碍广泛采用PEFC的关键技术挑战。

ABSTRACTPI: Matthew Mench Institution: Pennsylvania State UniversityProposal Number: 0644811Title: CAREER: Sensors for Quantification of Degradation in Polymer Electrolyte Fuel CellsIntellectual meritMany complex systems suffer gradual degradation that can result in catastrophic failure. Because the time scale of degradation is relatively slow, these types of anomalous faults are nearly impossible to detect at an early stage with conventional sensing technology. The PI is planning an approach that is to enable early detection and quantification of potentially catastrophic evolving faults in polymer electrolyte fuel cells (PEFCs). The objective is early detection that will precede implementation of anomaly mitigation and graceful degradation strategies, extending service availability, mean performance, and enabling a more aggressive initial design. This objective is essential to achieve the extended longevity that currently limits PEFCs. The methodology to achieve this sensing capability is heuristically similar to an electrocardiogram, in which the time series data of a heart's response to external stress are used to rapidly diagnose ailments that have evolved over many years.This sensing and quantification methodology provides early stage diagnosis of relatively slowly varying anomalies through a fast time scale stimulus-response approach. Following a chosen externally applied stimulus, the time series data are partitioned into a response vector using symbolic dynamics. The dynamic response vector characteristics are compared to a nominal response vector to precisely quantify the anomaly level. This early fault detection technique will enable nearly imperceptible slow time scale anomalies to be quantified online in a fast time scale, well before significant degradation occurs.Broader Impact:The broader impact of this research should help generate the infrastructure and knowledge base needed to enable ubiquitous PEFC implementation, and reduce long term United States dependence of fossil fuels. The CO sensor will be developed into a prototype under a collaboration between Nuvera and TA&D, a technology development company, maximizing impact. Educationally, the program will expose hundreds of students, from high school through graduate school, to fuel cell technology. Three Ph.D. students will be directly involved, and forty undergraduates per year will be enrolled in an upgraded senior level course including industrial speakers and a collaboration with the University of Arizona. A textbook currently in development will also benefit from the output of this research. High school students and teachers will participate in an summer program, and a new graduate-level course will be developed. Overall, the integrated research and educational program will help establish a permanent infrastructure to address key technical challenges that presently hinder widespread PEFC adoption.