Preliminary development of machine learning-based error correction model for low-fidelity reactor physics simulation

Oktavian, Muhammad Rizki; Nistor, J.; Gruenwald, J.T.; Xu, Yuedong

doi:10.1016/j.anucene.2023.109788

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…The subscripts H and L indicate high-fidelity and low-fidelity data, respectively. According to the equation, the predicted high-fidelity data can be determined by adding the error predictions from the machine learning model to the low-fidelity solutions 22 .…”

Section: Methodsmentioning

confidence: 99%

Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations

Oktavian,

Nistor,

Gruenwald

et al. 2024

Sci Rep

Self Cite

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This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with conventional simulation techniques. The ML model is trained to identify and correct errors in low-fidelity simulation outputs, traditionally derived from core physics computations. These corrections aim to align the low-fidelity results closely with high-fidelity data. Precise predictions of nuclear reactor parameters like core eigenvalue and power distribution are crucial for efficient fuel management and adherence to technical specifications. Current high-fidelity transport calculations, while accurate, are impractical for real-time predictions due to extensive computational demands. Our approach, therefore, utilizes the standard two-step simulation process-assembly-level lattice physics calculations followed by whole-core nodal diffusion computations-to generate initial results, which are then refined using the ML-based error correction model. The methodology focuses on improving simulation accuracy in regular BWR operations rather than developing a universal ML predictor for reactor physics. By training an advanced neural network model on the difference in high-fidelity and low-fidelity simulations, the model can reduce the nodal power error from low-fidelity simulations to around 1% on average and the core eigenvalue down to under 100 pcm. This result is under the condition of the normal variations of control rod pattern and core flow rate changes in standard BWR operations used in the training and evaluation of the machine learning model. This work suggests a promising approach for achieving more accurate, computationally feasible simulation solutions in nuclear reactor operation and management.

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Section: Methodsmentioning

confidence: 99%

Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations

Oktavian,

Nistor,

Gruenwald

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Data Availabilitymentioning

confidence: 99%

Integrating Core Physics and Machine Learning for Improved Parameter Prediction in Boiling Water Reactor Operations

Oktavian,

Nistor,

Gruenwald

et al. 2023

Preprint

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This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with conventional simulation techniques. The ML model is trained to identify and correct errors in low-fidelity simulation outcomes, traditionally derived from core physics computations. These corrections aim to align the low-fidelity results closely with high-fidelity data. Precise predictions of nuclear reactor parameters like core eigenvalue and power distribution are crucial for efficient fuel management and adherence to technical specifications. Current high-fidelity transport calculations, while accurate, are impractical for real-time predictions due to extensive computational demands. Our approach, therefore, utilizes the standard two-step simulation process—assembly-level lattice physics calculations followed by whole-core nodal diffusion computations—to generate initial results, which are then refined using the ML-based error correction model. The methodology focuses on improving simulation accuracy in regular BWR operations rather than developing a universal ML predictor for reactor physics. This work suggests a promising approach for achieving more accurate, computationally feasible simulation solutions in nuclear reactor operation and management.

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“…However, low-fidelity methods, such as the nodal expansion method (SARAX and CASMO), generally introduce coarser meshes, group collapse and more approximation during modeling and simulating, which might exhibit significant error, especially at the reactor boundary [20]. Furthermore, some advanced reactor designs cannot be sufficiently modeled using the existing nodal codes [21].…”

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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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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Preliminary development of machine learning-based error correction model for low-fidelity reactor physics simulation

Cited by 6 publications

References 40 publications

Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations

Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations

Integrating Core Physics and Machine Learning for Improved Parameter Prediction in Boiling Water Reactor Operations

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

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