Aerospace 2023, 10, 576. https://doi.org/10.3390/aerospace10070576 www.mdpi.com/journal/aerospace Article Velocity Mapping of an H2 − O2 Exhaust Jet in Air by Means of Schlieren Image Velocimetry (SIV) Emilia Georgiana Prisăcariu 1,2, *, Tudor Prisecaru 2 , Răzvan Edmond Nicoară 1 , Jeni Vilag 1 and Valeriu Alexandru Vilag 1 1 Romanian Research and Development Institute for Gas Turbines, COMOTI, 061126 Bucharest, Romania 2 Faculty of Mechanical Engineering and Mechatronics, Politehnica University of Bucharest, 060042 Bucharest, Romania * Correspondence: emilia.prisacariu@comoti.ro; Tel.: +40-764060265 Abstract: Visualization methods have always been used to inspect fows that are invisible to the naked eye. Seedless velocimetry has been regarded as an alternative to other intrusive quantitative methods and adapted to ft many applications in the industrial or scientifc feld. Schlieren image velocimetry (SIV) uses the general working principle of a schlieren system to acquire fow images, while relying on a particle image velocimetry (PIV)-like algorithm to obtain quantitative data related to the studied fow. The test case of this study consists of a turbulent round exhaust jet generated by a micro-thruster that uses H 2 −O 2 as a propellent. Mapping the local velocities of the fow is achieved by initially performing a lagrangian tracking method which makes use of a direct image correlation algorithm. These results are then compared to the velocity map obtained from a kymo- graph applied to a series of images. The velocity profles obtained through SIV will be compared to the velocity profle of the jet provided by the CFD simulation. The schlieren investigation of the jet’s local velocity map is set to determine the thruster’s capabilities, and conclude if the thruster reaches the desired Mach for which it has been designed. Keywords: SIV; direct image correlation; image processing; threshold; kymography; PIV 1. Introduction The schlieren image velocimetry (SIV) method presents implementation similarities with the particle image velocimetry (PIV) method. The PIV visualization method relies on tracking particles introduced in the studied fow to generate the instantaneous velocity feld, through the acquisition of two consecutive images recorded with short-time delay. The results are obtained by image cross correlation. PIV can be considered an artifcial- tracking mechanism [1] due to the tracking of particles’ movement based on the supposi- tion that the particles follow the fow perfectly, and not tracking the fow itself. The PIV method can prove to be extremely challenging or even impossible to apply to certain types of fow, such as the electrohydrodynamic and compressible fows, which are the issues arising from the selection of the particles and their spreading mechanism [2]. Moreover, SIV is performed through cross correlating consecutive schlieren images recorded with a short-time delay. The schlieren optical system allows for the recording of images of the optical inhomogeneities present in the studied medium, which are revealed by the deviation of the light. In the case of a turbulent jet, the eddies formed within the fow are considered SIV’s “tracer particles”. Therefore, the later method represents a more direct tracking mechanism using fow elements to describe the fow’s velocity, and thus can be considered as “self-seeded” [1]. There are many studies regarding the implementation of the SIV method on diferent test cases. Papamoschou [3] demonstrated that it is possible to acquire velocity infor- mation regarding high-speed fows when inquiring about the global convective velocity of a supersonic shear layer using a pulsed light and a short camera exposure time, while Citation: Prisăcariu, E.G.; Prisecaru, T.; Nicoară, R.E.; Vilag, J.; Vilag, V.A. Velocity Mapping of an H2 − O2 Exhaust Jet in Air by Means of Schlieren Image Velocimetry (SIV). Aerospace 2023, 10, 576. htps://doi.org/10.3390/ aerospace10070576 Academic Editor: Kung-Ming Chung Received: 4 May 2023 Revised: 8 June 2023 Accepted: 19 June 2023 Published: 21 June 2023 Copyright: © 2023 by the author. Licensee MDPI, Basel, Switerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Atribution (CC BY) license (htps://creativecommons.org/license s/by/4.0/).