Solutions to aliasing in time-resolved flow data

TL;DR

This paper introduces new strategies for detecting and mitigating aliasing in time-resolved flow data, using derivative data, spatial filtering, and tailored methods to improve data quality without excessive costs.

Contribution

It presents novel methods for aliasing mitigation applicable to large datasets, including derivative-based detection and filtering techniques, validated on models and LES data.

Findings

Derivative-based aliasing detection effectively identifies aliasing.

Spatial filtering reduces aliasing in convective systems.

Proposed methods improve data fidelity without high computational costs.

Abstract

Avoiding aliasing in time-resolved flow data obtained through high fidelity simulations while keeping the computational and storage costs at acceptable levels is often a challenge. Well-established solutions such as increasing the sampling rate or low-pass filtering to reduce aliasing can be prohibitively expensive for large data sets. This paper provides a set of alternative strategies for identifying and mitigating aliasing that are applicable even to large data sets. We show how time-derivative data, which can be obtained directly from the governing equations, can be used to detect aliasing and to turn the ill-posed problem of removing aliasing from data into a well-posed problem, yielding a prediction of the true spectrum. Similarly, we show how spatial filtering can be used to remove aliasing for convective systems. We also propose strategies to prevent aliasing when generating a database, including a method tailored for computing nonlinear forcing terms that arise within the resolvent framework. These methods are demonstrated using a non-linear Ginzburg-Landau model and large-eddy simulation (LES) data for a subsonic turbulent jet.