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Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions. / Penenko, Alexey; Penenko, Vladimir; Mukatova, Zhadyra.

Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 126-130 8109853.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Penenko, A, Penenko, V & Mukatova, Z 2017, Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions. in Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017., 8109853, Institute of Electrical and Electronics Engineers Inc., pp. 126-130, 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017, Novosibirsk, Russian Federation, 18.09.2017. https://doi.org/10.1109/SIBIRCON.2017.8109853

APA

Penenko, A., Penenko, V., & Mukatova, Z. (2017). Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions. In Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 (pp. 126-130). [8109853] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON.2017.8109853

Vancouver

Penenko A, Penenko V, Mukatova Z. Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions. In Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 126-130. 8109853 doi: 10.1109/SIBIRCON.2017.8109853

Author

Penenko, Alexey ; Penenko, Vladimir ; Mukatova, Zhadyra. / Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions. Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 126-130

BibTeX

@inproceedings{cb2a5782c99b4192a823aafffa7e85ae,
title = "Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions",
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.",
keywords = "Advection-diffusion model, Data assimilation, Finite-difference scheme, Variational approach",
author = "Alexey Penenko and Vladimir Penenko and Zhadyra Mukatova",
year = "2017",
month = nov,
day = "14",
doi = "10.1109/SIBIRCON.2017.8109853",
language = "English",
pages = "126--130",
booktitle = "Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017 ; Conference date: 18-09-2017 Through 22-09-2017",

}

RIS

TY - GEN

T1 - Direct data assimilation algorithms for advection-diffusion models with the increased smoothness of the uncertainty functions

AU - Penenko, Alexey

AU - Penenko, Vladimir

AU - Mukatova, Zhadyra

PY - 2017/11/14

Y1 - 2017/11/14

N2 - 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.

AB - 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.

KW - Advection-diffusion model

KW - Data assimilation

KW - Finite-difference scheme

KW - Variational approach

UR - http://www.scopus.com/inward/record.url?scp=85040519469&partnerID=8YFLogxK

U2 - 10.1109/SIBIRCON.2017.8109853

DO - 10.1109/SIBIRCON.2017.8109853

M3 - Conference contribution

AN - SCOPUS:85040519469

SP - 126

EP - 130

BT - Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017

Y2 - 18 September 2017 through 22 September 2017

ER -

ID: 9133607