Advanced image analysis

in PIV

Background

Aerodynamics investigation is required in several fields from automotive to aerospace engineering for different porpoises, e.g. aerodynamic drag reduction, attenuation of aerodynamic loading on body parts, reduction of aeroacoustic emissions and fundamental investigation.

A-posteriori uncertainty quantification

An approach for the a-posteriori quantification the PIV data uncertainty has been studied. The principle of the method relies on the concept of super-resolution: particle images are paired and their residual disparity is computed to infer the measurement uncertainty.

The approach has general applicability and is effective in quantifying the uncertainty due to typical error sources encountered in PIV.

The approach for a-posteriori uncertainty quantification has been successfully applied in the processing phase to select optimal processing parameters (e.g. the interrogation window size) such that the measurement uncertainty is minimized.

Publications

- A. Sciacchitano, F. Scarano, B. Wieneke, (2012) “Multi-frame pyramid correlation for time-resolved PIV”, Exp Fluids 53, pp 1087-1105

- A. Sciacchitano, R.P. Dwight, F. Scarano (2012) “Navier-Stokes simulations in gappy PIV data”, Exp Fluids 53, pp 1421-1435

- A. Sciacchitano, B. Wieneke, F. Scarano (2013) “PIV uncertainty quantification by image matching”, Meas. Sci. Technol. 24, 045302

- F.F.J. Schrijer, A. Sciacchitano, F. Scarano (2013) “Spatio-temporal and modal analysis of unsteady fluctuations in the high-subsonic base flow”, Phys. Fluids, under review

Examples of application of PIV for aerodynamic investigation

PIV working principle and limitations

Particle image velocimetry (PIV) is a diagnostic tool capable of measuring velocity fields in two- and three-dimensional domains. The technique measures the displacement during a short time interval of small tracer particles added to the flow and carried by the fluid. The particles are twice illuminated in a plane of the flow by a light source, typically a laser. The light they scatter is recorded onto two subsequent image frames by a digital imaging device, usually a charge couple device (CCD). The measurement of the particle images displacement in a known time interval leads to the computation of the velocity field in the imaged area.

Although PIV is considered a mature technique, several limitations are present. Among others:

• Inaccurate measurements close to stationary or moving surfaces due to laser light reflections.

• Low precision of temporal derivatives

• “Gaps” in PIV data where the flow velocity is not measured

• Absence of an acknowledged methodology for a-posteriori uncertainty quantification

Advanced image analysis

The PhD project focuses on how advanced image analysis can be employed to improve PIV results in the phases of image enhancement, data processing and uncertainty quantification.

Pyramid correlation for increased dynamic range

A novel technique has been investigated to increase the dynamic velocity range (DVR) of time-resolved PIV data, making it possible to measure accurate velocity temporal derivative. The pyramid correlation makes use of the temporal information contained in recordings that are correlated in time. A reduction of measurement errors by factor 5 is achieved.

PhD Candidate: Andrea Sciacchitano Department: AWEP Section: Aerodynamics Supervisor: F. Scarano Promoter: F. Scarano Start date: 01-05-2010 Funding: FLOVIST Project – LaVision GmbH Cooperations: LaVision GmbH

Aero

space

Engin

eering

Typical PIV setup

Pyramid correlation scheme in the correlation space

Acceleration time series measured with standard algorithm (WIDIM) and pyramid correlation

Fluctuations due to noise

Particle image disparity (left) and its distribution within an interrogation window (right)

Actual error d and estimated error d as a function of the out-of-plane displacement (top) and of the particle image diameter (bottom)

Acceleration contour measured with standard algorithm (WIDIM, left) and pyramid correlation (right)