Search NASA⌕ Search

Engineering topics

Byrne, Raymond Harry

Publications and source records attributed to Byrne, Raymond Harry.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

ARRA Energy Storage Demonstration Projects: Lessons Learned and Recommendations

The American Recovery and Reinvestment Act (ARRA) of 2009 (Recovery Act) provided funding for 16 energy storage demonstration projects. The projects ranged in scope from feasibility studies and technology demonstrations to full-scale, operational energy storage plants. This investment had a significant positive impact on the grid-connected energy storage industry. The goal of this report is to summarize the lessons learned from the ARRA projects, and to make recommendations for future Department of Energy (DOE) investments. Information for this report primarily came from three sources: a questionnaire and interview with each project team; DOE energy storage program peer review presentations; and DOE reports required as part of the ARRA project. Some lessons learned were common to many projects. Development of standards, codes and protocols specific to energy storage systems will mitigate uncertainty over code compliance, streamline permitting, and should be a priority (especially related to safety). Removal of regulatory barriers that preclude optimal operation of an energy storage system with multiple applications would immediately enable further deployment (e.g., FERC standards preclude the marketing department from reliability activities). Maturity of the approach to monetization varies substantially between applications with frequency regulation as an ancillary service leading other applications. Finally, developers focused on ramp mitigation and time shifting envision a reference plant. This reference plant would scale up from the current demonstration systems and would lead to the deployment of 50 MW-scale peaker plants.

25 ENERGY STORAGE↗
DOE OSTI2015

Large scale tracking algorithms

Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Large Scale Tracking Algorithms

Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase significantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classification steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗