Estimating flow fields with Reduced Order Models

TL;DR

This paper presents a reduced order modeling approach combined with an extended Kalman filter to estimate fluid flow fields inside a centrifugal pump in real-time, significantly reducing computational costs compared to traditional CFD methods.

Contribution

The paper introduces a novel projection-based reduced order model and a greedy algorithm for measurement placement, enabling real-time flow estimation in complex pump geometries.

Findings

ROM accurately estimates velocity and pressure fields

Greedy algorithm minimizes measurement locations

Extended Kalman filter ensures robust observability

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.