PIV uncertainty quantification by image matching

By A. Sciacchitano, Bernhard Wieneke, Fulvio Scarano. 2013

299 citations recorded by the source corpus.

Subfields: Computational Mechanics, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/f059a1b11f87063dd9c9487020a4d26779e33d86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. D. Schanz
2. S. Gesemann
3. Andreas Schröder
4. Bernhard Wieneke
5. Fulvio Scarano
6. Christian Poelma
7. B. W. van Oudheusden
8. G. Elsinga

In Ecology

1. Andreas Schröder
2. D. Schanz
3. S. Gesemann
4. Bernhard Wieneke

In Environmental Engineering

1. Bernhard Wieneke
2. D. Schanz
3. S. Gesemann
4. Andreas Schröder
5. Fulvio Scarano

Citations in the corpus

• PIV uncertainty quantification from correlation statistics (2015, methodology): “This is also the basis for the particle disparity method of Sciacchitano et al (2013).”
• Shake-The-Box: Lagrangian particle tracking at high particle image densities (2016, methodology): “Wieneke already demonstrated the very high accuracy achievable by the image matching process used in IPR— however in case of single images, this holds only for very low particle image densities (Wieneke 2013).”