Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction

Abstract

In fluid mechanics research, data gathered from measurements and simulations may be challenging to interpret due to complexities such as transience, non-linearity, and high dimensionality. Velocity data from the airflow through an internal combustion engine often exhibit such properties; nevertheless, accurate characterizations of these airflows are required in order to correctly predict and control the subsequent combustion and emission processes in pursuit of net zero targets. The temporal mean is a common way of representing an ensemble of realizations of velocity fields, but the averaging process can artificially diminish the magnitudes of the resultant vectors. Accurate representation of these vector magnitudes is of particular importance, as the velocity magnitudes in the intake airflow are thought to be primary drivers of the subsequent variation in an engine flow, which influences emission formation and overall efficiency. As an alternative to the ensemble mean, this work proposes the application of a dimensionality reduction method known as the sparsity-promoting dynamic mode decomposition (SPDMD), which can extract core structures from an ensemble of velocity fields while retaining more realistic vector magnitudes. This is demonstrated for the first time with large-eddy simulation (LES) velocity data and compared to a corresponding set of experimental particle image velocimetry (PIV) data. The SPDMD 0 Hz modes are shown to be more representative of the velocity magnitudes present in both datasets. This facilitates more accurate quantification of the differences in vector magnitudes between simulations and experiments, and more reliable identification of which LES snapshots are closer to the PIV ensemble.