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The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models’ parameters in real time and for improving data assimilation results.
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This content will become publicly available on June 27, 2026
Parameter analysis in continuous data assimilation for various turbulence models
In this study, we conduct parameter estimation analysis on a data assimilation algorithm for two turbulence models: the simplified Bardina model and the Navier–Stokes-α model. Rigorous estimates are presented for the convergence of continuous data assimilation methods when the parameters of the turbulence models are not known a priori. Our approach involves creating an approximate solution for the turbulence models by employing an interpolant operator based on the observational data of the systems. The estimation depends on the parameter alpha in the models. Additionally, numerical simulations are presented to validate our theoretical results.
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- Award ID(s):
- 2316894
- PAR ID:
- 10625330
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Communications in Nonlinear Science and Numerical Simulation
- ISSN:
- 10075704
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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