Spreading characteristics of swirling double-concentric jets excited at resonance Strouhal number

• Main Authors: Jufar, S.R., Le, M.D., Hsu, C.M.
• Format: Article
• Published: Elsevier Inc. 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072581526&doi=10.1016%2fj.expthermflusci.2019.109922&partnerID=40&md5=8966978c1b2653b0d6fd93250f1fc8db; http://eprints.utp.edu.my/23331/

Turbulent mixing plays a significant role in combustion processes. In low and medium load operations, the Reynolds number required to ensure transition from laminar to turbulent may not be attained. This paper presents a study on enhancing the mixing of swirling double-concentric jets at Reynolds number below the threshold required to ensure turbulent mixing. The central jet was acoustically excited using a custom-made loudspeaker placed at the plenum of a fifth order polynomial nozzle. The entrainment and spreading characteristics were studied experimentally using velocity field data measured by a high-speed particle image velocimetry (PIV). The time-averaged and fluctuating velocity components were used to investigate the flow characteristics. Enhancements in mixing were presented quantitatively using spreading rate of the jets. When the flow was subjected to excitation, the spreading rate has significantly increased in comparison to the naturally evolving flow. The spreading rate was proportion to the level of the pulsation intensity. Although the Reynolds number of the present study was much lower than the threshold required to ensure turbulent mixing, at higher pulsation intensities, the spreading rate and the jet opening angle approached the limiting level attained by fully turbulent jets. Hence, in applications where the higher Reynolds number required to attain adequate mixing is not practical, flow excitation can be used as an alternative for improving mixing. The jet spreading rate and entrainment characteristics demonstrated by the time-averaged PIV data substantiated the results inferred from the flow visualization. © 2019 Elsevier Inc.