A Bayesian approach of the Source Term Estimate coupling retro-dispersion computations with a Lagrangian Particle Dispersion Model and the Adaptive Multiple Importance Sampling

Harizo Rajaona 1, 2 François Septier 2, 3 Yves Delignon 2, 3 Patrick Armand 1 Christophe Olry 4 Armand Albergel 4
1 DAM/DIF - DAM Île-de-France
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
3 Institut TELECOM/TELECOM Lille1
4 ARIA Technologies
Abstract : This paper presents an enhanced version of a STE adaptive algorithm based on probabilistic Bayesian inference (AMIS) that estimates the parameters of an atmospheric pollution source term. After introducing the problem and presenting the initial results obtained with the first version of the algorithm, we describe an efficient way to reduce the computational load in its procedure by shifting the most time-consuming step outside the iterative loop. This is made possible by applying the duality relationship between forward and adjoint advection-diffusion equations, and is proven to work well on a synthetic example using Retro-SPRAY, the backward implementation of SPRAY, the Lagrangian particle dispersion model of the PMSS suite.
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Conference papers
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https://hal-imt.archives-ouvertes.fr/hal-01359091
Contributor : François Septier <>
Submitted on : Thursday, September 1, 2016 - 5:41:45 PM
Last modification on : Saturday, March 23, 2019 - 1:24:58 AM

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  • HAL Id : hal-01359091, version 1

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INSTITUT-TELECOM | CEA | CRISTAL-SIGMA | IMT-LILLE-DOUAI | CRISTAL | UNIV-LILLE | DAM

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Harizo Rajaona, François Septier, Yves Delignon, Patrick Armand, Christophe Olry, et al.. A Bayesian approach of the Source Term Estimate coupling retro-dispersion computations with a Lagrangian Particle Dispersion Model and the Adaptive Multiple Importance Sampling. 17th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (HARMO 17), May 2016, Budapest, Hungary. pp.1-5. ⟨hal-01359091⟩

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