An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

Gong, Hang; Cheng, Sibo; Chen, Zhang; Li, Qing; Quilodrán-Casas, César; Xiao, Dunhui; Arcucci, Rossella

doi:10.1016/j.anucene.2022.109431

Cited by 36 publications

(11 citation statements)

References 67 publications

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“…Tree-based algorithms, including Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were applied to test the prediction performance for the sizes of AgNPs. These methods have been extensively used in a wide range of engineering problems [45], [46], [47]. After implementation, all the proposed models were compared based on the accuracy of their predictions.…”

Section: Machine Learning Modellingmentioning

confidence: 99%

Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning

Nathanael¹,

Cheng²,

Kovalchuk³

et al. 2023

Chemical Engineering Research and Design

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Section: Machine Learning Modellingmentioning

confidence: 99%

Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning

Nathanael¹,

Cheng²,

Kovalchuk³

et al. 2023

Chemical Engineering Research and Design

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“…The works of [105] and [106] used AE to learn the Koopman invariant subspace for Dynamic Mode Decomposition (DMD). A number of studies have also successfully applied POD-based AEs for urban air pollution [107] and nuclear engineering [108], [68]. These methods benefit from both the accuracy of DL AEs and the interpretability of projection-based approaches.…”

Section: ML For Predicting High-dimensional Dynamical Systemsmentioning

confidence: 99%

“…The model coefficients related to the fire spread rate are then consistently updated using real-time satellite observations. The same technology has been applied to nuclear reactor physics [68] with spatially-sparse observations. By construction, this approach can incorporate observations with flexible length time windows where pure ML can get into difficulties with unfixed input dimension.…”

Section: B ML and Da For Parameter Estimationsmentioning

confidence: 99%

Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

Cheng¹,

Quilodran-Casas²,

Ouala³

et al. 2023

Preprint

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Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.

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“…Notably, in the emerging research area, Gong et al . proposed a digital twin technique for nuclear reactor operations, which also presents an urgent need for nuclear power plant accident datasets 24 – 26 .…”

Section: Background and Summarymentioning

confidence: 99%

An open time-series simulated dataset covering various accidents for nuclear power plants

Xiao

Liang

et al. 2022

Sci Data

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Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.

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An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

Cited by 36 publications

References 67 publications

Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning

Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning

Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

An open time-series simulated dataset covering various accidents for nuclear power plants

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