Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

Price, Dean; Radaideh, Majdi I.; Kochunas, Brendan

doi:10.1016/j.nucengdes.2022.111776

Cited by 13 publications

(3 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of swarm intelligence is based on the social collaboration of individuals to learn with their own experience in a group. It is applied to optimize fuel reloading problems in PWR (Meneses et al, 2009), optimization of fuel core loading pattern in a VVER nuclear reactor (Babazadeh et al, 2009), optimization of secondary circuit system in marine NPP (Zhao et al, 2023), optimize the design parameters of radiation shielding system material (Lei et al, 2023), optimization of control drum operation for a microreactor under normal and transient conditions (Price et al, 2022), and designing of space nuclear reactor fuel (Rafiei and Ansarifar, 2022). Besides, the PSO mechanism is employed in NPPs for fault diagnosis (Wang H. et al, 2021), designing maintenance and safety systems (Wang J. et al, 2021), and control system development (Coban, 2011;Safarzadeh and Noori-kalkhoran, 2021;Ejigu and Liu, 2022a;Ayele Ejigu and Liu, 2022;Muthuraj et al, 2023).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Ejigu,

Tuo,

Liu

2024

Front. Nucl. Eng.

View full text Add to dashboard Cite

Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Ejigu,

Tuo,

Liu

2024

Front. Nucl. Eng.

View full text Add to dashboard Cite

show abstract

“…the NN parameters, which will further be used to update the parameters, using gradient descent. It is worth mentioning that the MSE is more sensitive to outliers compared to MAE, which is more robust with outliers [31].…”

Section: Feedforward Networkmentioning

confidence: 99%

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Moloko¹,

Bokov²,

Xu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Artificial Neural Networks (ANNs) have been successfully used in various nuclear engineering applications, such as predicting reactor physics parameters within reasonable time and with a high level of accuracy. Despite this success, they cannot provide information about the model prediction uncertainties, making it difficult to assess ANN prediction credibility, especially in extrapolated domains. In this study, Deep Neural Networks (DNNs) are used to predict the assembly axial neutron flux profiles in the SAFARI-1 research reactor, with quantified uncertainties in the ANN predictions and extrapolation to cycles not used in the training process. The training dataset consists of copper-wire activation measurements, the axial measurement locations and the measured control bank positions obtained from the reactor's historical cycles. Uncertainty Quantification of the regular DNN models' predictions is performed using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by Variational Inference (BNN VI). The regular DNNs, DNNs solved with MCD and BNN VI results agree very well among each other as well as with the new measured dataset not used in the training process, thus indicating good prediction and generalization capability. The uncertainty bands produced by MCD and BNN VI agree very well, and in general, they can fully envelop the noisy measurement data points. The developed ANNs are useful in supporting the experimental measurements campaign and neutronics code Verification and Validation (V&V).

show abstract

“…Recently, evolutionary algorithms (EA) for automatic controller generation has been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. These algorithms begin by creating a set of elements, called the initial generation.…”

Section: Introductionmentioning

confidence: 99%

A Tool for Control Research Using Evolutionary Algorithm That Generates Controllers with a Pre-Specified Morphology

et al. 2023

View full text Add to dashboard Cite

In control research and design it is frequently necessary to explore, evaluate, tune and compare many control strategies. These activities are assisted by software tools of increasing complexity; however, even with the existing high performance tools these activities are very time consuming due to they imply hundred if not thousand of simulations. If the process of doing such simulations is not automated it can be a very time consuming task. There has been proposed evolutionary algorithms (EA) that in the search for an optimal control automatically generate many control structures. However, the space of possible controllers for any dynamical system is huge. Hence it is mandatory to restrict the search space. The best way to restrict the controller search space is to let the designer influence the search direction. In this paper we propose a software tool for control research that has as its main part an EA that produce only controllers having a pre-specified morphology. By specifying a controller morphology the designer can influence the search direction without losing the exploration capability of evolutionary algorithms. The EA is endowed with a cost function tailored for fast evaluation of closed-loop controller performance. The use of the tool is illustrated by searching an sliding mode and similar controllers for an unstable linear and two nonlinear systems.

show abstract

Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

Cited by 13 publications

References 68 publications

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

A Tool for Control Research Using Evolutionary Algorithm That Generates Controllers with a Pre-Specified Morphology

Contact Info

Product

Resources

About