Dynamic Data-Driven Event Reconstruction for Atmospheric Releases

Mirin, A ; Serban, R ; Kosovic, B

Lawrence Livermore National Laboratory

关键词: 99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Algorithms;    Time Delay;    Computers;    Source Terms;   

DOI  :  10.2172/15011605
RP-ID  :  UCRL-TR-210565
RP-ID  :  W-7405-ENG-48
RP-ID  :  15011605

美国|英语

来源: UNT Digital Library

【 摘 要 】

This is a collaborative LDRD Exploratory Research project involving four directorates--Energy & Environment, Engineering, NAI and Computation. The project seeks to answer the following critical questions regarding atmospheric releases--''How much material was released? When? Where? and What are the potential consequences?'' Inaccurate estimation of the source term can lead to gross errors, time delays during a crisis, and even fatalities. We are developing a capability that seamlessly integrates observational data streams with predictive models in order to provide the best possible estimates of unknown source term parameters, as well as optimal and timely situation analyses consistent with both models and data. Our approach utilizes Bayesian inference and stochastic sampling methods (Markov Chain and Sequential Monte Carlo) to reformulate the inverse problem 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.