Picture of automobile manufacturing plant

Driving innovations in manufacturing: Open Access research from DMEM

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Design, Manufacture & Engineering Management (DMEM).

Centred on the vision of 'Delivering Total Engineering', DMEM is a centre for excellence in the processes, systems and technologies needed to support and enable engineering from concept to remanufacture. From user-centred design to sustainable design, from manufacturing operations to remanufacturing, from advanced materials research to systems engineering.

Explore Open Access research by DMEM...

Data mining reactor fuel grab load trace data to support nuclear core condition monitoring

West, G.M. and Jahn, G.J. and McArthur, S.D.J. and McDonald, J.R. and Reed, J. (2006) Data mining reactor fuel grab load trace data to support nuclear core condition monitoring. IEEE Transactions on Nuclear Science, 53 (3). pp. 1494-1503. ISSN 0018-9499

[img]
Preview
PDF (Data mining reactor fuel grab data paper)
West_Data_Mining_Reactor.pdf - Accepted Author Manuscript

Download (348kB) | Preview

Abstract

A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described.