An Optimisation-Based Decision Support System Framework for Multi-Objective in- Core Fuel Management of Nuclear Reactor Cores

Schlünz, E.B.; Bokov, Pavel M.; Vuuren, Jan H. van

doi:10.7166/27-3-1650

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In this section, the test dataset is used to verify the generalization ability of the surrogate model in predicting neutron flux distribution. The relative error is used to quantify the deviation, with the min relative error (MinRE), mean relative error (MeanRE) and max relative error (MaxRE) represented by Equations ( 8)- (10), respectively.…”

Section: D Neutron Flux Distribution Predictionmentioning

confidence: 99%

“…Zhang et al proposed a surrogate model based on convolutional neural network (CNN) for predicting the eigenvalue and the assembly-wise power distribution with a simplified pressurized water reactor (PWR) during depletion [9], and the results indicate that neural network has the capability to predict core key parameters and reduce the computation time. Schlunz et al constructed ANN surrogate models for solving the SAFARI-1 in-core fuel management optimization Energies 2023, 16, 4044 2 of 18 (ICFMO) problems [10], demonstrating that the ANNs could significantly reduce the computational time. Jang et al developed a prediction algorithm based on CNN to replace the numerical analysis code in the optimization of the loading pattern [11], which can facilitate rapid identification of the optimal loading pattern.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Surrogate Model Based on Back-Propagation Neural Network for Key Neutronics Parameters Prediction in Molten Salt Reactor

Bei,

Dai,

et al. 2023

Energies

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The simulation and analysis of neutronics parameters in Molten Salt Reactors (MSRs) is fundamental for the design of the reactor core. However, high-fidelity neutron transport calculations of the MSR are time-consuming and require significant computational resources. Artificial neural networks (ANNs) have been used in various industries, and in recent years are increasingly introduced in the nuclear industry. Back-Propagation neural network (BPNN) is one type of ANN. A surrogate model based on BP neural network is developed to quickly predict two key neutronics parameters in reactor core: the effective multiplication factor (keff) and the three-dimensional channel-by-channel neutron flux distribution. The dataset samples are generated by modeling and simulating different operation states of the Molten Salt Reactor Experiment (MSRE) using the Monte Carlo code. Hyper-parameters optimization is performed to obtain the optimal surrogate model. The numerical results on the test dataset show good agreement between the surrogate model and the Monte Carlo code. Additionally, the surrogate model significantly reduces computation time compared to the Monte Carlo code and greatly enhances efficiency. The feasibility and advantages of the proposed surrogate model is demonstrated, which has important significance for real-time prediction and design optimization of the reactor core.

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Section: D Neutron Flux Distribution Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Surrogate Model Based on Back-Propagation Neural Network for Key Neutronics Parameters Prediction in Molten Salt Reactor

Bei,

Dai,

et al. 2023

Energies

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Trend of decision support system frameworks from 2010-2021

Jaafar,

Noor,

Ali

et al. 2023

The 15th Universiti Malaysia Terengganu Annual Symposium 2021 (Umtas 2021)

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Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry

Khadka,

Sthapit,

Epiphaniou

et al. 2024

ACM Comput. Surv.

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The widespread adoption and success of Machine Learning (ML) technologies depend on thorough testing of the resilience and robustness to adversarial attacks. The testing should focus on both the model and the data. It is necessary to build robust and resilient systems to withstand disruptions and remain functional despite the action of adversaries, specifically in the security-sensitive industry like the Nuclear Industry (NI) where consequences can be fatal in terms of both human lives and assets. We analyse ML based research works that have investigated adversaries and defence strategies in the NI. We then present the progress in the adoption of ML techniques, identify use cases where adversaries can threaten the ML-enabled systems and finally identify the progress on building Resilient Machine Learning (rML) systems entirely focusing on the NI domain.

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An Optimisation-Based Decision Support System Framework for Multi-Objective in- Core Fuel Management of Nuclear Reactor Cores

Cited by 3 publications

References 23 publications

Three-Dimensional Surrogate Model Based on Back-Propagation Neural Network for Key Neutronics Parameters Prediction in Molten Salt Reactor

Three-Dimensional Surrogate Model Based on Back-Propagation Neural Network for Key Neutronics Parameters Prediction in Molten Salt Reactor

Trend of decision support system frameworks from 2010-2021

Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry

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