Wavelet time–frequency analysis of accelerating and decelerating flows in a tube bank |
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| Affiliation: | 1. The State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, 650093, PR China;2. College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, Yunnan, PR China;3. National Engineering Laboratory for Vacuum Metallurgy, Kunming University of Science and Technology, Kunming, 650093, PR China;4. School of Chemical Science and Technology, Yunnan University, Kunming, 650091, PR China;1. Division of Heat Transfer, Department of Energy Sciences, Lund University, P.O. Box 118, SE-22100 Lund, Sweden;2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi''an 710072, Shaanxi, China;1. Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea;2. Korea Atomic Energy Research Institute, 989-111 Daedeok-daero, Yuseong-gu, Daejeon 34057, Republic of Korea |
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| Abstract: | In the present work, the steady approximation for accelerating and decelerating flows through tube banks is discussed. With this purpose, the experimental study of velocity and pressure fluctuations of transient turbulent cross-flow in a tube bank with square arrangement and a pitch-to-diameter ratio of 1.26 is performed. The Reynolds number at steady-state flow, computed with the tube diameter and the flow velocity in the narrow gap between the tubes, is 8 × 104. Air is the working fluid. The accelerating and decelerating transients are obtained by means of start and stop of the centrifugal blower. Wavelet and wavelet packet multiresolution analysis were applied to decompose the signal in frequency intervals, using Daubechies 20 wavelet and scale functions, thus allowing the analysis of phenomena in a time–frequency domain. The continuous wavelet transform was also applied, using the Morlet function. The signals in the steady state, which presented a bistable behavior, were separated in two modes and analyzed with usual statistic tools. The results were compared with the steady-state assumption, demonstrating the ability of wavelets for analyzing time varying signals. |
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