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Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network

Several researchers have concentrated on analyzing the nature of fuel claddings through performing burst experiments on computed loss-of-coolant accident scenarios and creating practical and conceptual computer programs. In comparison to experimental observation, the established burst criteria (a) a...

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Main Authors: Tumpa, P.P., Saiful Islam, M., May, Z., Khorshed Alam, M.
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120849564&doi=10.1007%2f978-981-16-6636-0_31&partnerID=40&md5=4ed1fdf5b8a0397cc285d0a646c8ed36
http://eprints.utp.edu.my/28915/
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spelling my.utp.eprints.289152022-03-16T08:42:52Z Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network Tumpa, P.P. Saiful Islam, M. May, Z. Khorshed Alam, M. Several researchers have concentrated on analyzing the nature of fuel claddings through performing burst experiments on computed loss-of-coolant accident scenarios and creating practical and conceptual computer programs. In comparison to experimental observation, the established burst criteria (a) assumes that the cladding tube deforms in a symmetrical manner (b) infers the characteristics of Zircaloy-4 cladding for mixed-phase of α + β step (c) ignores azimuthal temperature variations. To resolve all of the shortcomings of the burst criteria, this paper proposed an artificial neural network to forecast the burst parameters. In this research, a feedforward backpropagation algorithm with the logsig activation function is used to build this neural network model. A neural network architecture of 2-15-15-15-3, which is a model of three hidden layers containing fifteen neurons in each layer is designed. The mean deviation of burst temperature, burst stress, and burst strain gained from the burst criteria is 1.15, 3.82, and 39.41, respectively, while these parameters are predicted by the proposed neural network includes mean deviations of 0.43, 1.57, and 3.85, respectively. The proposed neural network has been discovered to be more efficient than existing models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Springer Science and Business Media Deutschland GmbH 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120849564&doi=10.1007%2f978-981-16-6636-0_31&partnerID=40&md5=4ed1fdf5b8a0397cc285d0a646c8ed36 Tumpa, P.P. and Saiful Islam, M. and May, Z. and Khorshed Alam, M. (2022) Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network. Lecture Notes on Data Engineering and Communications Technologies, 95 . pp. 407-418. http://eprints.utp.edu.my/28915/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Several researchers have concentrated on analyzing the nature of fuel claddings through performing burst experiments on computed loss-of-coolant accident scenarios and creating practical and conceptual computer programs. In comparison to experimental observation, the established burst criteria (a) assumes that the cladding tube deforms in a symmetrical manner (b) infers the characteristics of Zircaloy-4 cladding for mixed-phase of α + β step (c) ignores azimuthal temperature variations. To resolve all of the shortcomings of the burst criteria, this paper proposed an artificial neural network to forecast the burst parameters. In this research, a feedforward backpropagation algorithm with the logsig activation function is used to build this neural network model. A neural network architecture of 2-15-15-15-3, which is a model of three hidden layers containing fifteen neurons in each layer is designed. The mean deviation of burst temperature, burst stress, and burst strain gained from the burst criteria is 1.15, 3.82, and 39.41, respectively, while these parameters are predicted by the proposed neural network includes mean deviations of 0.43, 1.57, and 3.85, respectively. The proposed neural network has been discovered to be more efficient than existing models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
format Article
author Tumpa, P.P.
Saiful Islam, M.
May, Z.
Khorshed Alam, M.
spellingShingle Tumpa, P.P.
Saiful Islam, M.
May, Z.
Khorshed Alam, M.
Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network
author_facet Tumpa, P.P.
Saiful Islam, M.
May, Z.
Khorshed Alam, M.
author_sort Tumpa, P.P.
title Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network
title_short Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network
title_full Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network
title_fullStr Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network
title_full_unstemmed Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network
title_sort nuclear power plant burst parameters prediction during a loss-of-coolant accident using an artificial neural network
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120849564&doi=10.1007%2f978-981-16-6636-0_31&partnerID=40&md5=4ed1fdf5b8a0397cc285d0a646c8ed36
http://eprints.utp.edu.my/28915/
_version_ 1738656900535287808
score 13.211869

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