A study on the application of artificial neural network to predict k-eff and peaking factor of a small modular PWR

Le Tran Chung1, Nguyen Thi Dung1, Tran Viet Phu2
1 Institute for Nuclear Science and Technology, VINATOM
2 Machine Perception Technology Horus AI Co., Ltd

Main Article Content


Machine learning (ML) using artificial neural network (ANN) methods is being applied to predict required parameters for nuclear reactors based on learning from big data sets. The ML models usually give faster calculation speed while the accuracy is good agreement with physical simulators. In this work, a multi-layer perceptron network was built and trained to predict k-eff and peaking factor of a small modular pressurized water reactor (PWR). The results are compared with those obtained by using a reactor physics code system, i.e. SRAC2006. The comparison shows good agreement accuracy and higher performance of the ML models.

Article Details


[1]. Nissan, Ephraim; An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel; Designs. 3. 37. DOI: 10.3390/designs3030037, 2019.
[2]. Ahmad Pirouzmand, Morteza Kazem Dehdashti; Estimation of relative power distribution and power peaking factor in a VVER-1000 reactor core using artificial neural networks; Progress in Nuclear Energy, Volume 85, 17-27, 2015.
[3]. M. El-Sefy, A. Yosri, W. El-Dakhakhni, S. Nagasaki, L. Wiebe; Artificial neural network for predicting nuclear power plant dynamic behaviors; Nuclear Engineering and Technology; Volume 53, Issue 10, 3275-3285, 2021.
[4]. Sobes, V., Hiscox, B., Popov, E. et al; AI-based design of a nuclear reactor core; Scientific Reports 11, 19646, 2021.
[5]. Ngoc Dat Nguyen, Van Thai Nguyen; Development of ANN structural optimization framework for data-driven prediction of local two-phase flow parameters; Progress in Nuclear Energy, Volume 146, 104176, 2022.
[6]. Hakim Mazrou; Performance improvement of artificial neural networks designed for safety key parameters prediction in nuclear research reactors; Nuclear Engineering and Design, Volume 239, Issue 10, 2009.
[7]. Afshin Hedayat, Hadi Davilu, Ahmad Abdollahzadeh Barfrosh, Kamran Sepanloo; Estimation of research reactor core parameters using cascade feed forward artificial neural networks; Progress in Nuclear Energy, Volume 51, Issues 6–7, 2009.
[8]. José Luis Montes, Juan Luis François, Juan José Ortiz, Cecilia Martín-del-Campo, Raúl Perusquía; Local power peaking factor estimation in nuclear fuel by artificial neural networks; Annals of Nuclear Energy, Volume 36, Issue 1, 2009.
[9]. S.M. Mirvakili, F. Faghihi, H. Khalafi; Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network; Annals of Nuclear Energy,Volume 50,2012.
[10]. Yinghao Chen, Dongdong Wang, Cao Kai, Cuijie Pan, Yayun Yu, Muzhou Hou; Prediction of safety parameters of pressurized water reactor based on feature fusion neural network; Annals of Nuclear Energy, Volume 166, 2022.
[11]. Asim Saeed, Atif Rashid; Development of Core Monitoring System for a Nuclear Power Plant using Artificial Neural Network Technique; Annals of Nuclear Energy, Volume 144, 2020.
[12]. Qingyu Huang, Shinian Peng, Jian Deng, Hui Zeng, Zhuo Zhang, Yu Liu, Peng Yuan; A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next; Heliyon 9 (2023) e13883.
[13]. Popescu, Marius-Constantin & Balas, Valentina & Perescu-Popescu, Liliana & Mastorakis, Nikos. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems. 8.
[14]. K Okumura, T Kugo, K Kaneko, K Tsuchihashi, “SRAC2006: A Comprehensive Neutronics Calculation Code System”, JAEA-Data/Code, 2007, 2007-004.
[15]. Keisuke Okumura, “COREBN: A Core Burn-up Calculation Module for SRAC2006”, JAEA-Data/Code, 2007, 2007-003.
[16]. Hoang Van Khanh, “Core Design of a Small Pressurized Water Reactor with AP1000 Fuel Assembly Using SRAC2006 and COBRA-EN Codes”, Science and Technology of Nuclear Installations. 2020. 1-7. 10.1155/2020/8847897.
[17]. Rahmi N. Ramdhani et al., “Neutronics Analysis of SMART Small Modular Reactor using SRAC2006 Code”, Journal of Physics: Conference Series. 877.012067. 10.1088/1742-6596/877/1/012067.