Exploiting modern image processing in surface flow visualisation

Abdelsalam, Tarek I.; Williams, Richard; Ingram, Grant

Abstract

Surface flow visualisation is an experimental technique where the surface of interest is painted with an oil and dye mixture before a flow is applied to the object. In regions of high shear stress the oil/dye mixture is then removed and in regions of low shear stress the oil/dye mixture stays or builds up. The resulting pattern can be analysed to determine the structure near the surface under test, this is normally done in a qualitative manner with flow structures being identified based on the expertise of the experimentalist. Modern image processing tools can identify shapes and lines in pictures and this paper describes the development of an algorithm to apply these techniques to surface flow visualisation and derive quantitative numerical data from the visualisation images. The well know "Durham Cascade" a low speed, linear cascade with around 110 degrees of turning is used as a test case for this technique. The direction of streamlines, location of saddle points and positions of maximum shear stress were identified and quantitatively compared to computational fluid dynamics of the same case. Overall this paper describes a process by which new information can be extracted from an existing and well used technique.

Citation

Abdelsalam, T. I., Williams, R., & Ingram, G. (2017). Exploiting modern image processing in surface flow visualisation. In Proceedings of the 1st Global Power and Propulsion Forum : GPPF 2017, Jan 16-18, 2017, Zurich, Switzerland (GPPF-2017)

• Conference Name: 1st Global Power and Propulsion Forum
• Conference Location: Zurich, Switzerland
• Publication Date: Jan 17, 2017
• Deposit Date: Mar 27, 2017
• Publicly Available Date: Mar 27, 2017
• Pages: GPPF-2017
• Series ISSN: 2504-4400
• Book Title: Proceedings of the 1st Global Power and Propulsion Forum : GPPF 2017, Jan 16-18, 2017, Zurich, Switzerland.
• Publisher URL: https://gpps.global/documents/events/zurich17/papers/control-instrumentation/GPPF_2017_paper_18.pdf