The present work investigates the use of a numerical approximation to the Navier–Stokes equations to increase the temporal resolution of time-resolved PIV data for general flows. The solution of the governing equations is applied to 3D data obtained from tomographic PIV and is based on the vortex-in-cell method (Christiansen in J Comput Phys 13:363–379, 1973) under the hypothesis of incompressible flow. The principle of time-supersampling is that the spatial information can be leveraged to increase the temporal resolution. The unsteady numerical simulation of the dynamic evolution of the flow is applied within the 3D measurement domain and time integration is performed between each pair of consecutive measurements. Initial conditions are taken from the first measurement field and time-resolved boundary conditions are approximated between the two fields. Temporal continuity of the velocity field is obtained by imposing a weighted average of forward and backward time integration. The accuracy of this time-supersampling method is studied for two experimental datasets obtained from time-resolved tomographic PIV measurements: a turbulent wake and a circular jet. The results are compared to linear interpolation, advection-based supersampling, and measurement data at high sampling rate. In both flows, we demonstrate the ability to reconstruct detailed temporal dynamics using data sub-sampled at a rate far below the Nyquist frequency.
Experiments in Fluids – Springer Journals
Published: Mar 13, 2014
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera