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 avoid 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 large-eddy simulation
(LES) data for a subsonic turbulent jet and a non-linear Ginzburg-Landau model.Comment: 25 pages, 14 figure