Work

Parameter Analysis in Continuous Data Assimilation for Various Turbulence Models

Year: 2024

Type: preprint

Abstract: 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. 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

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.03042

Authors: Albanez, Debora A. F., Benvenutti, Maicon Jose, Little, Samuel, Tian, Jing

Institution: Towson University

Language: English

Topic: Navier-Stokes equation solutions

Subfield: Applied Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Universidade Federal de Santa Catarina, Universidade Tecnológica Federal do Paraná

Award: LEAPS-MPS: Exploring various subgrid scale turbulence models via convergence analysis, data assimilation and deep learning

OpenAlex ID: W4403555627

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