Real-time planar flow velocity measurements using an optical flow algorithm implemented on GPU

Abstract

This paper presents a high-speed implementation of an optical flow algorithm which computes in real-time planar velocity fields in an experimental flow. Real-time computations of the flow velocity field allow the experimentalist to have instantaneous access to quantitative features of the flow. This can be very useful in many situations: fast evaluation of the performances and characteristics of a new setup, design optimization, easier and faster parametric studies, etc. It can also be used as a visual sensor for an input in closed-loop flow control experiments where fast estimation of the state of the flow is needed. The algorithm is implemented on a graphics processor unit. The accuracy of the computation is demonstrated. Computation speed and scalability of the processing are highlighted along with guidelines for further improvements. The system architecture is flexible, scalable and can be adapted on the fly in order to process higher resolutions or achieve higher precision. The setup is applied on a backward-facing step flow in a hydrodynamic channel. For validation purposes, classical particle image velocimetry (PIV) is used to compare with instantaneous optical flow measurements. The important flow characteristics like the dynamics of the recirculation bubble, computed in real time for the first time, are well recovered. The accuracy of real-time optical flow measurements is comparable to off-line PIV computations.

Prediction of the dynamics of a backward-facing step flow using focused time-delay neural networks and particle image velocimetry data-sets

“…It also allows for high computational speed when implemented on a GPU with CUDA functions [52]. The high spatial resolution is important for nearwall measurements while the high computation speed is important for real-time measurements that can be used as inputs in closed-loop flow control experiments [53]. The code has been used many times both for time-resolved PIV measurements with a high spatial resolution [54], as well as for closed-loop flow control experiments [51,55].…”

Data-driven order reduction and velocity field reconstruction using neural networks: The case of a turbulent boundary layer

“…The velocity field is calculated from the acquisition of successive snapshots of the vertical laser sheet in the middle of the test section using a home-made optical-flow algorithm. The first version of the code has been developed at ONERA 5 and later modified, optimized and adapted to the constraints of real-time measurements 13 . The advantage of this algorithm compared to a standard FFT-PIV algorithm is its high computational speed when implemented on GPUs with CUDA functions 8 .…”

Chaotic dynamics of large-scale structures in a turbulent wake

“…The interrogation window size is 16 × 16 pixels and the calculation is based on three iterations for each of the three pyramid reduction levels. One can find more details on this measurement method in [17][18][19][20][21] which rigorously demonstrate its offline accuracy as well as its online efficiency in closed-loop flow control experiments. An example of a 2D instantaneous velocity field is shown on Fig.…”