Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions

Alexey Penenko, Vladimir Penenko, Zhadyra Mukatova

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

Abstract

Direct variational data assimilation algorithm for the non-stationary one-dimensional advection-diffusion model and in situ measurements is presented. Data assimilation is carried out by adjusting the uncertainty (control) function that has the sense of the emission sources. In the algorithm a target functional containing the misfit between the modeled and measured values and a regularizer, containing a norm of the control function derivative, is minimized on every time step of the discretized advection-diffusion model. The minimum is obtained by the solution of the tri-diagonal matrix system. The performance of the algorithm was evaluated in the numerical experiments.

Original languageEnglish
Title of host publicationProceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages126-130
Number of pages5
ISBN (Electronic)9781538615966
DOIs
Publication statusPublished - 14 Nov 2017
Event2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 - Novosibirsk, Russian Federation
Duration: 18 Sep 201722 Sep 2017

Conference

Conference2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017
CountryRussian Federation
CityNovosibirsk
Period18.09.201722.09.2017

Keywords

  • Advection-diffusion model
  • Data assimilation
  • Finite-difference scheme
  • Variational approach

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