Year: 2026
Type: data-paper
Abstract: Deep learning is of growing interest to the fluids community due its potential applications for real-time prediction and control. Indeed, whereas computational fluid dynamics solvers are prohibitively time-intensive for real-time implementation, deep learning methods can be evaluated in milliseconds once trained. However, the majority of efforts to apply deep learning methods to flows have been performed with computational data rather than experimental data, in part due to the lack of freely available large experimental datasets. In this paper we present a 64.7 GB dataset of two-dimensional velocity fields measured with particle image velocimetry. These fields show the temporal evolution of cylinder wakes in the sub-critical regime for Reynolds numbers ranging from 240 to 2520. The data is broken into discrete time series made up of 100 time-steps each, representing approximately three vortex shedding cycles. The dataset, available for immediate and free use, is poised to facilitate research on deep learning methods in fluids and other endeavors where such a large, detailed experimental dataset is of utility.
Source: Scientific Data
Authors: Peter I. Renn, Emily H. Palmer, Morteza Gharib
Institutions: California Institute of Technology, Cooper Union
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7161287291
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