Integrating cross-correlation techniques and neural networks for feedwater flow measurement
| Autor: | J.A. Carrasco, J.I. Sanabrias, D. Ruan, Paolo F. Fantoni, L. Fernandez, Davide Roverso |
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| Rok vydání: | 2003 |
| Předmět: |
Artificial neural network
Computer science Boiler feedwater Energy Engineering and Power Technology Computational intelligence Control engineering Fuzzy control system Flow measurement law.invention Noise Nuclear Energy and Engineering Flow (mathematics) law Nuclear power plant Safety Risk Reliability and Quality Waste Management and Disposal |
| Zdroj: | Progress in Nuclear Energy. 43:267-274 |
| ISSN: | 0149-1970 |
| DOI: | 10.1016/s0149-1970(03)00036-2 |
| Popis: | This paper reports an early progress of a feasibility study of a computational intelligence approach to the enhancement of the accuracy of flow measurements in the framework of an ongoing cooperation between Tecnatom s.a. in Madrid and the OECD Halden Reactor Project (HRP) in Halden. The aim of this research project is to contribute to the development and validation of a flow sensor in a nuclear power plant (NPP). The basic idea is to combine the use of applied computational intelligence approaches (noise analysis, neural networks, fuzzy systems, wavelets etc.) with existing traditional flow measurements, and in particular with cross-correlation flowmeter concepts. |
| Databáze: | OpenAIRE |
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