Iterative ensemble Kalman filter for atmospheric dispersion in nuclear accidents: An application to Kincaid tracer experiment

Zhang, Xiaole; Su, Guofeng; Chen, J.G.; Raskob, W.; Yuan, Hongyong; Huang, Qi

doi:10.1016/j.jhazmat.2015.05.035

Cited by 42 publications

(21 citation statements)

References 33 publications

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“…In this study, 20 trials in various atmospheric stability conditions are selected and the meteorological variables are taken from an analysis of meteorological and micro-meteorological observations in Yee and Biltoft (2004) (Table 1). It is noted that the errors related to meteorological data can affect the accuracy of the source term estimation (Zhang et al, 2014(Zhang et al, , 2015, although this error is not considered in this study. In each trial, the gas was continuously released for ≈ 15 min, during which concentration measurements were made.…”

Section: The Mock Urban Setting Test (Must) Tracer Field Experimentsmentioning

confidence: 99%

“…The probabilistic category treats source parameters as random variables associated with the probability distribution. This includes the Bayesian estimation theory (Bocquet, 2005;Monache et al, 2008;Yee et al, 2014), Monte Carlo algorithms using Markov chains (MCMC) (Gamerman and Lopes, 2006;Keats, 2009), and various stochastic sampling algorithms (Zhang et al, 2014(Zhang et al, , 2015. Deterministic methods use cost functions to assess the difference between observed and modeled concentrations and are based on an iterative process to minimize this difference (Seibert, 2001;Penenko et al, 2002;Sharan et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

et al. 2019

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Abstract. This study presents an optimization methodology for reducing the size of an existing monitoring network of the sensors measuring polluting substances in an urban-like environment in order to estimate an unknown emission source. The methodology is presented by coupling the simulated annealing (SA) algorithm with the renormalization inversion technique and the computational fluid dynamics (CFD) modeling approach. This study presents an application of the renormalization data-assimilation theory for optimally reducing the size of an existing monitoring network in an urban-like environment. The performance of the obtained reduced optimal sensor networks is analyzed by reconstructing the unknown continuous point emission using the concentration measurements from the sensors in that optimized network. This approach is successfully applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The main results consist of reducing the size of a fixed network of 40 sensors deployed in the MUST experiment. The optimal networks in the MUST urban region are determined, which makes it possible to reduce the size of the original network (40 sensors) to ∼1/3 (13 sensors) and 1∕4 (10 sensors). Using measurements from the reduced optimal networks of 10 and 13 sensors, the averaged location errors are obtained as 19.20 and 17.42 m, respectively, which are comparable to the 14.62 m obtained from the original 40-sensor network. In 80 % of the trials with networks of 10 and 13 sensors, the emission rates are estimated within a factor of 2 of the actual release rates. These are also comparable to the performance of the original network, whereby in 75 % of the trials the releases were estimated within a factor of 2 of the actual emission rates.

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Section: The Mock Urban Setting Test (Must) Tracer Field Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

et al. 2019

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“…An atmospheric dispersion module for radioactivity release evaluations using a Kalman filter technique has been developed as a part of diagnostic system for accident management in [8]. Different modified ensemble Kalman filters for atmospheric dispersion modelling have been recently proposed by Zhang et al [9], [10]. Particle filter based methods have been integrated with atmospheric transport model to improve model predictions [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Radionuclide Release Activity Using an Unscented Kalman Filter

Vajpayee

Becerra

Bausch

et al. 2019

2019 6th International Conference on Instrumentation, Control, and Automation (ICA)

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Estimation of radionuclide release is an important problem due to its impact on population and environment. Especially, radioactivity release, plume height, and wind velocity need to be estimated reliably to plan emergency response in case of any unforeseen situation. In this paper, a non-linear estimation technique based on Unscented Kalman Filter has been proposed to estimate radioactivity release, wind velocity, and height of release using environmental data collected from radiation monitors placed in the proximity of release point. The Gaussian plume model has been considered to model atmospheric dispersion phenomenon of radionuclide release and for the calculation of dose rates. The performance of the proposed estimation technique has been evaluated in terms of root mean squared error. The estimation algorithm is found to be performing satisfactorily.

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“…Furthermore, Kalman filter and its extended methods are also extensively used in data-driven modeling due to its extensive framework [9,10]. A modified ensemble Kalman filter for nuclear accident prediction was proposed [11,12]. Reddy et al utilized particle filter to improve diffusion model based on Gaussian multi-puff equation [13].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection

Zhu

Qiu

Chen

et al. 2018

IJERPH

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The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

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Iterative ensemble Kalman filter for atmospheric dispersion in nuclear accidents: An application to Kincaid tracer experiment

Cited by 42 publications

References 33 publications

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

Estimation of Radionuclide Release Activity Using an Unscented Kalman Filter

Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection

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