Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

Kobayashi, Kazuma; Kumar, Dinesh; Bonney, Matthew; Chakraborty, Souvik; Paaren, Kyle M.; Usman, Shoaib; Alam, Syed Bahauddin

doi:10.1007/978-3-030-72322-4_205-1

Cited by 3 publications

(2 citation statements)

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“…18 DeepONet utilizes fully connected neural networks for both the branch and trunk networks. The branch network has a layer size of [190,80,80], while the trunk network has a layer size of [2,80,80] to accommodate the two-dimensional input (x, y).…”

Section: Deeponet Modelmentioning

confidence: 99%

“…1 The NRC has highlighted several potential benefits of DTs in nuclear energy applications, including increased operational efficiencies, enhanced safety and reliability, reduced errors, faster information sharing, and improved predictive capabilities. 1,2 However, it is imperative to recognize that the evolution of DT technology within the realm of nuclear systems is still at its inception, bringing forth a range of intricate challenges that necessitate diligent resolution and strategic overcoming. 3 These challenges span across multifarious domains, encapsulating crucial aspects like the integrating data from various sources, the modeling & simulation (M&S) of complex nuclear systems, the synchronization in real-time between the digital replica and physical asset, and the critical domains of cybersecurity and safeguarding data privacy.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Operator Driven Digital Twin-EnablingTechnology: Accelerated Surrogate Prediction for NuclearSystems

Kobayashi,

Alam

2023

Preprint

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This research introduces the Deep Operator Network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) systems for nuclear energy systems. With the increasing importance of nuclear energy as a carbon-neutral solution, adopting DT technology has become crucial to enhancing operational efficiencies, safety, and predictive capabilities in nuclear engineering applications. DeepONet exhibits remarkable prediction accuracy, outperforming traditional ML methods.Through extensive benchmarking and evaluation, this study showcases the scalability and computational efficiency of DeepONet in solving a challenging particle transport problem. By taking functions as input data and constructing the operator G from training data, DeepONet can handle diverse and complex scenarios effectively.However, the application of DeepONet also reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world implementation. Addressing these challenges will further enhance the method's practicality and reliability.Overall, DeepONet presents a promising and transformative tool for nuclear engineering research and applications. Its accurate prediction and computational efficiency capabilities can revolutionize DT systems, advancing nuclear engineering research. This study marks an important step towards harnessing the power of surrogate modeling techniques in critical engineering domains.

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Section: Deeponet Modelmentioning

confidence: 99%