Picture offshore wind farm

Open Access research that is improving renewable energy technology...

Strathprints makes available scholarly Open Access content by researchers across the departments of Mechanical & Aerospace Engineering (MAE), Electronic & Electrical Engineering (EEE), and Naval Architecture, Ocean & Marine Engineering (NAOME), all of which are leading research into aspects of wind energy, the control of wind turbines and wind farms.

Researchers at EEE are examining the dynamic analysis of turbines, their modelling and simulation, control system design and their optimisation, along with resource assessment and condition monitoring issues. The Energy Systems Research Unit (ESRU) within MAE is producing research to achieve significant levels of energy efficiency using new and renewable energy systems. Meanwhile, researchers at NAOME are supporting the development of offshore wind, wave and tidal-current energy to assist in the provision of diverse energy sources and economic growth in the renewable energy sector.

Explore Open Access research by EEE, MAE and NAOME on renewable energy technologies. Or explore all of Strathclyde's Open Access research...

An intelligent system for interpreting the nuclear refuelling process within an advanced gas-cooled reactor

Steele, J.A. and Martin, L.A. and McArthur, S.D.J. and Moyes, A.J. and McDonald, J.R. and Howie, D. and Elrick, R. and Yule, I.Y. (2003) An intelligent system for interpreting the nuclear refuelling process within an advanced gas-cooled reactor. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 217 (2). pp. 159-167. ISSN 0957-6509

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

Evaluation of the data produced during the refuelling process in a nuclear power plant is required to ensure proper 'set-down' of the fuel assembly, thereby allowing the continued and safe operation of the station. The process of evaluating the data can be time consuming owing to the large amounts of data requiring considerable domain experience and interpretation. This paper presents an intelligent system (IS) to automate the process of data analysis, thereby shortening the evaluation time and providing an explanation of the reasoning behind its conclusions. The intelligent system utilizes a knowledge-based system (KBS), neural network based classification, K-means clustering techniques and rule induction methods to evaluate the data and inform the operator of any errors encountered.