Results of the application and demonstration calculations

Georgios Alexandridis; Christoph Blaesius; Christophe Demaziere; Christophe Destouches; Abdelhamid Dokhane; Aiden Durrant; Vladimir Fiser; Gaëtan Girardin; Joachim Herb; Georgios Ioannou; Robert Jacqmin; Alexander Knospe; Stefanos Kollias; Carsten Lange; Georgios Leontidis; Sandor Lipcsei; Cristina Montalvo; Antonios Mylonakis; Laurent Pantera; Yann Perin; Christoph Pohl; Marcus Seidl; Andreas Stafylopatis; Petr Stulik; Luis Alejandro Torres; Gumersindo  Verdu Martin; Vasudha Verma; Antoni Vidal-Ferràndiz; Marco Viebach; Paolo Vinai

Results of the application and demonstration calculations

Georgios Alexandridis, Christoph Blaesius, Christophe Demaziere, Christophe Destouches, Abdelhamid Dokhane, Aiden Durrant, Vladimir Fiser, Gaëtan Girardin, Joachim Herb, Georgios Ioannou, Robert Jacqmin, Alexander Knospe, Stefanos Kollias, Carsten Lange, Georgios Leontidis, Sandor Lipcsei, Cristina Montalvo, Antonios Mylonakis, Laurent Pantera, Yann PerinChristoph Pohl, Marcus Seidl, Andreas Stafylopatis, Petr Stulik, Luis Alejandro Torres, Gumersindo Verdu Martin, Vasudha Verma, Antoni Vidal-Ferràndiz, Marco Viebach, Paolo Vinai

Research output: Other contribution

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Abstract

This report describes the results of the application of the newly developed tools for reactor noise analysis, advanced signal processing and machine learning methodologies to actual plant data. More specifically, the modeling tools developed in WP1 are used to simulate and analyze real plant measurements from the pre-Konvoi 3-loop NPP at Gösgen, a German pre-Konvoi 4-loop reactor, a Hungarian VVER-440 reactor and a Czech VVER-1000 reactor. Subsequently, the techniques developed in WP3 are used to identify the root causes of neutron flux fluctuations in the actual plant data for the reactors presented above. The results are compared with the simulation results corresponding to the selected power plants, based on the hypotheses developed for reactor noise analysis.

Original language	English
Publisher	European Commission
Number of pages	194
Publication status	Published - 31 Jul 2020

Bibliographical note

CORTEX - Research and Innovation Action (RIA)
This project has received funding from the European
Union's Horizon 2020 research and innovation programme
under grant agreement No 754316.

Keywords

nuclear reactors
Machine learning
Anomaly detection
nuclear reactor noise analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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CORTEX-D4.4-Results-of-the-application-and-demonstration-calculationsFinal published version, 20.3 MBLicence: Unspecified

https://cortex-h2020.eu/wp-content/uploads/2020/08/CORTEX-D4.4-Results-of-the-application-and-demonstration-calculations.pdf

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Alexandridis, G., Blaesius, C., Demaziere, C., Destouches, C., Dokhane, A., Durrant, A., Fiser, V., Girardin, G., Herb, J., Ioannou, G., Jacqmin, R., Knospe, A., Kollias, S., Lange, C., Leontidis, G., Lipcsei, S., Montalvo, C., Mylonakis, A., Pantera, L., ... Vinai, P. (2020, Jul 31). Results of the application and demonstration calculations. European Commission. https://cortex-h2020.eu/wp-content/uploads/2020/08/CORTEX-D4.4-Results-of-the-application-and-demonstration-calculations.pdf

Alexandridis, G, Blaesius, C, Demaziere, C, Destouches, C, Dokhane, A, Durrant, A, Fiser, V, Girardin, G, Herb, J, Ioannou, G, Jacqmin, R, Knospe, A, Kollias, S, Lange, C, Leontidis, G, Lipcsei, S, Montalvo, C, Mylonakis, A, Pantera, L, Perin, Y, Pohl, C, Seidl, M, Stafylopatis, A, Stulik, P, Torres, LA, Verdu Martin, G, Verma, V, Vidal-Ferràndiz, A, Viebach, M & Vinai, P 2020, Results of the application and demonstration calculations. European Commission. <https://cortex-h2020.eu/wp-content/uploads/2020/08/CORTEX-D4.4-Results-of-the-application-and-demonstration-calculations.pdf>

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author = "Georgios Alexandridis and Christoph Blaesius and Christophe Demaziere and Christophe Destouches and Abdelhamid Dokhane and Aiden Durrant and Vladimir Fiser and Ga{\"e}tan Girardin and Joachim Herb and Georgios Ioannou and Robert Jacqmin and Alexander Knospe and Stefanos Kollias and Carsten Lange and Georgios Leontidis and Sandor Lipcsei and Cristina Montalvo and Antonios Mylonakis and Laurent Pantera and Yann Perin and Christoph Pohl and Marcus Seidl and Andreas Stafylopatis and Petr Stulik and Torres, {Luis Alejandro} and {Verdu Martin}, Gumersindo and Vasudha Verma and Antoni Vidal-Ferr{\`a}ndiz and Marco Viebach and Paolo Vinai",

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AU - Alexandridis, Georgios

AU - Blaesius, Christoph

AU - Demaziere, Christophe

AU - Destouches, Christophe

AU - Dokhane, Abdelhamid

AU - Durrant, Aiden

AU - Fiser, Vladimir

AU - Girardin, Gaëtan

AU - Herb, Joachim

AU - Ioannou, Georgios

AU - Jacqmin, Robert

AU - Knospe, Alexander

AU - Kollias, Stefanos

AU - Lange, Carsten

AU - Leontidis, Georgios

AU - Lipcsei, Sandor

AU - Montalvo, Cristina

AU - Mylonakis, Antonios

AU - Pantera, Laurent

AU - Perin, Yann

AU - Pohl, Christoph

AU - Seidl, Marcus

AU - Stafylopatis, Andreas

AU - Stulik, Petr

AU - Torres, Luis Alejandro

AU - Verdu Martin, Gumersindo

AU - Verma, Vasudha

AU - Vidal-Ferràndiz, Antoni

AU - Viebach, Marco

AU - Vinai, Paolo

N1 - CORTEX - Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 754316.

PY - 2020/7/31

Y1 - 2020/7/31

N2 - This report describes the results of the application of the newly developed tools for reactor noise analysis, advanced signal processing and machine learning methodologies to actual plant data. More specifically, the modeling tools developed in WP1 are used to simulate and analyze real plant measurements from the pre-Konvoi 3-loop NPP at Gösgen, a German pre-Konvoi 4-loop reactor, a Hungarian VVER-440 reactor and a Czech VVER-1000 reactor. Subsequently, the techniques developed in WP3 are used to identify the root causes of neutron flux fluctuations in the actual plant data for the reactors presented above. The results are compared with the simulation results corresponding to the selected power plants, based on the hypotheses developed for reactor noise analysis.

AB - This report describes the results of the application of the newly developed tools for reactor noise analysis, advanced signal processing and machine learning methodologies to actual plant data. More specifically, the modeling tools developed in WP1 are used to simulate and analyze real plant measurements from the pre-Konvoi 3-loop NPP at Gösgen, a German pre-Konvoi 4-loop reactor, a Hungarian VVER-440 reactor and a Czech VVER-1000 reactor. Subsequently, the techniques developed in WP3 are used to identify the root causes of neutron flux fluctuations in the actual plant data for the reactors presented above. The results are compared with the simulation results corresponding to the selected power plants, based on the hypotheses developed for reactor noise analysis.

KW - nuclear reactors

KW - Machine learning

KW - Anomaly detection

KW - nuclear reactor noise analysis

M3 - Other contribution

PB - European Commission

ER -

Results of the application and demonstration calculations

Abstract

Bibliographical note

Keywords

UN SDGs

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