Continuous mapping of nuclear reactor core power using artificial neural network even in the presence of inactive detectors
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Monitoring the radial power distribution during the operation of a pressurized light water reactor (PWR) is crucial for ensuring safe operating conditions and achieving high levels of fuel burnup. This paper introduces a methodology utilizing Artificial Neural Networks (ANN) for reconstructing the radial power distribution in the core of a Pressurized Water Reactor (PWR) with a hot full power of 1876 MWth, such as the Angra 1 reactor. This approach uses measurements from Self-Powered Neutron Detectors (SPND), simulated through the SERPENT code. The use of ANN demonstrated good accuracy in predicting the radial power distribution with an average relative error of 1.27%, considering 36 active detectors, with maximum relative error of 6.99%. Moreover, the proposed process demonstrated robust performance, even when measurements from one, two, or three SPND detectors were unavailable, resulting in errors of 1.24%, 1.13 %, and 1.09%, respectively. Therefore, the methodology ensures a reliable reconstruction of the radial power distribution, even when SPND detector measurements are unavailable, enabling the optimization of detector use and contributing to the increase of operational safety margins.
Abstract
Monitoring the radial power distribution during the operation of a pressurized light water reactor (PWR) is crucial for ensuring safe operating conditions and achieving high levels of fuel burnup. This paper introduces a methodology utilizing Artificial Neural Networks (ANN) for reconstructing the radial power distribution in the core of a Pressurized Water Reactor (PWR) with a hot full power of 1876 MWth, such as the Angra 1 reactor. This approach uses measurements from Self-Powered Neutron Detectors (SPND), simulated through the SERPENT code. The use of ANN demonstrated good accuracy in predicting the radial power distribution with an average relative error of 1.27%, considering 36 active detectors, with maximum relative error of 6.99%. Moreover, the proposed process demonstrated robust performance, even when measurements from one, two, or three SPND detectors were unavailable, resulting in errors of 1.24%, 1.13 %, and 1.09%, respectively. Therefore, the methodology ensures a reliable reconstruction of the radial power distribution, even when SPND detector measurements are unavailable, enabling the optimization of detector use and contributing to the increase of operational safety margins.
참고문헌 (27)
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