An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation

Gong, Hang; Yu, Yingrui; Li, Qing; Quan, Chaoyu

doi:10.1016/j.anucene.2020.107346

Cited by 33 publications

(13 citation statements)

References 34 publications

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“…r denotes the correlation scale length in the 2D space and is also generated uniformly with r ∼ U (1,5). Being part of Matern kernels, the SOAR function is often used in DA for prior error covariance modeling [26,6] thanks to its smoothness and good conditioning. The simulation of x b,t k = [u b,t k , v b,t k ] via the same discretization of Eq.39 (except the initial conditions) is used as background states at time t k in the DA modeling.…”

Section: Observation (Y) Transformationmentioning

confidence: 99%

“…In order to improve the reconstruction and prediction of dynamical systems with uncertainties, data assimilation (DA) techniques, originally developed in numerical weather prediction (NWP) [1] and geosciences [2], are widely applied to industrial problems, such as hydrology [3], wildfire forecasting [4], drought monitoring [5] and nuclear engineering [6]. DA algorithms aim to find the optimal approximation (also known as the analyzed state) of the state variables (usually representing a physical field of interest, such as velocity, temperature etc.…”

Section: Introductionmentioning

confidence: 99%

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Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks

Cheng¹,

Qiu²

2021

Preprint

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Data assimilation techniques are widely used to predict complex dynamical systems with uncertainties, based on time-series observation data. Error covariance matrices modelling is an important element in data assimilation algorithms which can considerably impact the forecasting accuracy. The estimation of these covariances, which usually relies on empirical assumptions and physical constraints, is often imprecise and computationally expensive especially for systems of large dimension. In this work, we propose a data-driven approach based on long short term memory (LSTM) recurrent neural networks (RNN) to improve both the accuracy and the efficiency of observation covariance specification in data assimilation for dynamical systems. Learning the covariance matrix from observed/simulated time-series data, the proposed approach does not require any knowledge or assumption about prior error distribution, unlike classical posterior tuning methods. We have compared the novel approach with two state-of-the-art covariance tuning algorithms, namely DI01 and D05, first in a Lorenz dynamical system and then in a 2D shallow water twin experiments framework with different covariance parameterization using ensemble assimilation. This novel method shows significant advantages in observation covariance specification, assimilation accuracy and computational efficiency.

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Section: Observation (Y) Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks

Cheng¹,

Qiu²

2021

Preprint

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“…Data-driven approaches enabled by machine learning (ML) have led to significant progress in many nuclear engineering applications, including reactor performance optimization [8,9,10], transient-state prediction [11,12], anomaly detection [13], data assimilation [14,15], model validation and uncertainty quantification [16,17,18,19,20], and digital twin [21]. Among various ML methods, deep neural networks (DNNs) provide us with an especially promising technical approach to developing a data-driven coarse-mesh turbulence model.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks

Liu¹,

Hu²,

Kraus³

et al. 2021

Preprint

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Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclearsystem code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this paper, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial-temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of-flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained model's generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.

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“…Data assimilation (DA) is applied in a wide range of industrial problems, such as numerical weather prediction (NWP) [1], hydrology, fire forecasting [2] or nuclear engineering [3]. Recently, DA methods have also been used to COVID-19 pandemic analysis, including predicting disease diffusion and proposing optimal vaccination strategies ( [4]).…”

Section: Introductionmentioning

confidence: 99%

Observation data compression for variational assimilation of dynamical systems

Cheng

Lucor

Argaud³

2021

Preprint

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Accurate estimation of error covariances (both background and observation) is crucial for efficient observation compression approaches in data assimilation of large-scale dynamical problems. We propose a new combination of a covariance tuning algorithm with existing PCA-type data compression approaches, either observation-or information-based, with the aim of reducing the computational cost of real-time updating at each assimilation step. Relying on a local assumption of flow-independent error covariances, dynamical assimilation residuals are used to adjust the covariance in each assimilation window. The estimated covariances then contribute to better specify the principal components of either the observation dynamics or the state-observation sensitivity. The proposed approaches are first validated on a shallow water twin experiment with correlated and non-homogeneous observation error. Proper selection of flow-independent assimilation windows, together with sampling density for background error estimation, and sensitivity of the approaches to the observations error covariance knowledge, are also discussed and illustrated with various numerical tests and results. The method is then applied to a more challenging industrial hydrological model with real-world data and non-linear transformation operator provided by an operational precipitation-flow simulation software.

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An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation

Cited by 33 publications

References 34 publications

Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks

Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks

Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks

Observation data compression for variational assimilation of dynamical systems

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