Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Cho, Yubin (Student TU Delft; To70 Aviation Consultants)
Huang, Yilin (TU Delft System Engineering)
Verbraeck, A. (TU Delft Policy Analysis)

Krzhizhanovskaya, Valeria V. (editor)
Závodszky, Gábor (editor)
Lees, Michael H. (editor)
Sloot, Peter M.A. (editor)
Sloot, Peter M.A. (editor)
Sloot, Peter M.A. (editor)
Dongarra, Jack J. (editor)
Brissos, Sérgio (editor)
Teixeira, João (editor)

2020

Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.

Data Assimilation
Discrete-event simulation
Dynamic Data-Driven Simulation
Particle Filters
Sensitivity analysis

http://resolver.tudelft.nl/uuid:d9a89f26-e529-4543-a54f-9079bf3be2c0

https://doi.org/10.1007/978-3-030-50433-5_3

2020-12-15

978-3-030-50432-8

Computational Science – ICCS 2020 - 20th International Conference, Proceedings, 12142

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12142 LNCS

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2020 Yubin Cho, Yilin Huang, A. Verbraeck