Transition Core Modeling for Extended-Enrichment Accident-Tolerant Fuels in Light Water Reactors Using PARCS/Polaris

Oktavian, Muhammad Rizki; Mertyurek, Ugur; Xu, Yunlin

doi:10.1080/00295639.2022.2162790

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In this work, the model was made in quarter symmetry to save computational time and further reduce the data complexity 20 . The symmetry was conducted in the radial direction only.…”

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

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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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“…In this work, the model was made in quarter symmetry to save computational time and further reduce the data complexity 20 . The symmetry was conducted in the radial direction only.…”

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

View full text Add to dashboard Cite

show abstract

“…In this work, the model was made in quarter symmetry to save computational time and further reduce the data complexity 16 . The symmetry was conducted in the radial direction only.…”

Section: Low-fidelity and High-fidelity Datamentioning

confidence: 99%

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

Oktavian,

Nistor,

Gruenwald

et al. 2023

Preprint

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 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.

show abstract

Improving Neutron Diffusion Solver on Small-Size Boiling Water Reactors with a Neural Network-Based Correction Model

Oktavian,

Lastres,

Nistor

et al. 2023

Preprint

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Transition Core Modeling for Extended-Enrichment Accident-Tolerant Fuels in Light Water Reactors Using PARCS/Polaris

Cited by 3 publications

References 5 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

Improving Neutron Diffusion Solver on Small-Size Boiling Water Reactors with a Neural Network-Based Correction Model

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