  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.
