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Title: Dynamic Data-Driven Event Reconstruction for Atmospheric Releases

Abstract

The role of an event reconstruction capability in a case of an atmospheric release is to characterize the source by answering the critical questions--How much material was released? When? Where? and What are the potential consequences? Accurate estimation of the source term is essential to accurately predict plume dispersion, effectively manage the emergency response, and mitigate consequences in a case of an atmospheric release of hazardous material. We are developing a capability that seamlessly integrates observational data streams with predictive models in order to provide probabilistic estimates of unknown source term parameters consistent with both data and model predictions. Our approach utilizes Bayesian inference with stochastic sampling using Markov Chain and Sequential Monte Carlo methodology. The inverse dispersion problem is reformulated into a solution based on efficient sampling of an ensemble of predictive simulations, guided by statistical comparisons with data. We are developing a flexible and adaptable data-driven event-reconstruction capability for atmospheric releases that provides (1) quantitative probabilistic estimates of the principal source-term parameters (e.g., the time-varying release rate and location); (2) predictions of increasing fidelity as an event progresses and additional data become available; and (3) analysis tools for sensor network design and uncertainty studies. Our computational framework incorporatesmore » multiple stochastic algorithms, operates with a range and variety of atmospheric models, and runs on multiple computer platforms, from workstations to large-scale computing resources. Our final goal is a multi-resolution capability for both real-time operational response and high fidelity multi-scale applications.« less

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
928545
Report Number(s):
UCRL-TR-220719
TRN: US200812%%507
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; COMPUTERS; DESIGN; HAZARDOUS MATERIALS; PLUMES; SAMPLING; SOURCE TERMS

Citation Formats

Mirin, A A, and Kosovic, B. Dynamic Data-Driven Event Reconstruction for Atmospheric Releases. United States: N. p., 2006. Web. doi:10.2172/928545.
Mirin, A A, & Kosovic, B. Dynamic Data-Driven Event Reconstruction for Atmospheric Releases. United States. doi:10.2172/928545.
Mirin, A A, and Kosovic, B. Wed . "Dynamic Data-Driven Event Reconstruction for Atmospheric Releases". United States. doi:10.2172/928545. https://www.osti.gov/servlets/purl/928545.
@article{osti_928545,
title = {Dynamic Data-Driven Event Reconstruction for Atmospheric Releases},
author = {Mirin, A A and Kosovic, B},
abstractNote = {The role of an event reconstruction capability in a case of an atmospheric release is to characterize the source by answering the critical questions--How much material was released? When? Where? and What are the potential consequences? Accurate estimation of the source term is essential to accurately predict plume dispersion, effectively manage the emergency response, and mitigate consequences in a case of an atmospheric release of hazardous material. We are developing a capability that seamlessly integrates observational data streams with predictive models in order to provide probabilistic estimates of unknown source term parameters consistent with both data and model predictions. Our approach utilizes Bayesian inference with stochastic sampling using Markov Chain and Sequential Monte Carlo methodology. The inverse dispersion problem is reformulated into a solution based on efficient sampling of an ensemble of predictive simulations, guided by statistical comparisons with data. We are developing a flexible and adaptable data-driven event-reconstruction capability for atmospheric releases that provides (1) quantitative probabilistic estimates of the principal source-term parameters (e.g., the time-varying release rate and location); (2) predictions of increasing fidelity as an event progresses and additional data become available; and (3) analysis tools for sensor network design and uncertainty studies. Our computational framework incorporates multiple stochastic algorithms, operates with a range and variety of atmospheric models, and runs on multiple computer platforms, from workstations to large-scale computing resources. Our final goal is a multi-resolution capability for both real-time operational response and high fidelity multi-scale applications.},
doi = {10.2172/928545},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Apr 19 00:00:00 EDT 2006},
month = {Wed Apr 19 00:00:00 EDT 2006}
}

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