Estimating flow fields with Reduced Order Models
Abstract
The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands. We use a projection-based reduced order model (ROM) instead. Based on this ROM, a realtime observer can be devised that estimates the temporally and spatially resolved velocity and pressure fields inside the pump. The entire fluid-solid domain is treated as a fluid in order to be able to consider moving rigid bodies in the reduction method. A greedy algorithm is introduced for finding suitable and as few measurement locations as possible. Robust observability is ensured with an extended Kalman filter, which is based on a time-variant observability matrix obtained from the nonlinear velocity ROM. We present the results of the velocity and pressure ROMs based on a unsteady Reynolds-averaged Navier-Stokes CFD simulation of a 2D centrifugal pump, as well as the results for the extended Kalman filter.
Cite
@article{arxiv.2202.05736,
title = {Estimating flow fields with Reduced Order Models},
author = {Kamil David Sommer and Lucas Reineking and Yogesh Parry Ravichandran and Romuald Skoda and Martin Mönnigmann},
journal= {arXiv preprint arXiv:2202.05736},
year = {2023}
}
Comments
20 pages, 12 figures