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Remembering ANS member Gil Brown
Brown
The nuclear community is mourning the loss of Gilbert Brown, who passed away on July 11 at the age of 77 following a battle with cancer.
Brown, an American Nuclear Society Fellow and an ANS member for nearly 50 years, joined the faculty at Lowell Technological Institute—now the University of Massachusetts–Lowell—in 1973 and remained there for the rest of his career. He eventually became director of the UMass Lowell nuclear engineering program. After his retirement, he remained an emeritus professor at the university.
Sukesh Aghara, chair of the Nuclear Engineering Department Heads Organization, noted in an email to NEDHO members and others that “Gil was a relentless advocate for nuclear energy and a deeply respected member of our professional community. He was also a kind and generous friend—and one of the reasons I ended up at UMass Lowell. He served the university with great dedication. . . . Within NEDHO, Gil was a steady presence and served for many years as our treasurer. His contributions to nuclear engineering education and to this community will be dearly missed.”
Fan Li, Belle R. Upadhyaya
Nuclear Technology | Volume 173 | Number 1 | January 2011 | Pages 17-25
Technical Paper | NPIC&HMIT Special / Nuclear Plant Operations and Control | doi.org/10.13182/NT11-A11480
Articles are hosted by Taylor and Francis Online.
Fault diagnosis is an important area in the nuclear industry for effective and continuous operation of power plants. All the approaches for fault diagnosis depend critically on the sensors that measure important process variables in the system. The locations of these sensors determine the effectiveness of the diagnostic methods. However, the emphasis of most fault diagnosis approaches is primarily on procedures to perform fault detection and isolation (FDI) given a set of sensors. Little attention has been given to the actual allocation of sensors for achieving efficient FDI performance. A graph-based approach, the directed graph (DG), is proposed in this paper as a solution for the optimization of sensor locations for efficient fault identification. The application of the DG modeling in deciding the locations of sensors based on the concepts of observability and fault resolution is introduced. A reliability maximization-based optimization framework for sensor placement from a fault diagnosis perspective is described. The helical coil steam generator unit of the International Reactor Innovative and Secure system is outlined to underscore the utility of the algorithms for large systems.