Investigation of Wall-Pressure Fluctuations in Three Pressure-Induced Turbulent Separation Bubbles

2019 ◽  
Author(s):  
Arnaud S. Le Floc'h ◽  
Abdelouahab T. Mohammed-Taifour ◽  
Louis Dufresne ◽  
Julien Weiss
Keyword(s):  
Pressure Fluctuations ◽  
Wall Pressure ◽  
Wall Pressure Fluctuations ◽  
Turbulent Separation ◽  
Separation Bubbles

Unsteadiness in a large turbulent separation bubble

2016 ◽  
Vol 799 ◽  
pp. 383-412 ◽  
Author(s):  
Abdelouahab Mohammed-Taifour ◽  
Julien Weiss
Keyword(s):  
Shear Layer ◽  
Separation Bubble ◽  
Pressure Fluctuations ◽  
Pressure Sensors ◽  
Low Frequency ◽  
Wall Pressure ◽  
Medium Frequency ◽  
Wall Pressure Fluctuations ◽  
Turbulent Separation ◽  
The Mean

The unsteady behaviour of a massively separated, pressure-induced turbulent separation bubble (TSB) is investigated experimentally using high-speed particle image velocimetry (PIV) and piezo-resistive pressure sensors. The TSB is generated on a flat test surface by a combination of adverse and favourable pressure gradients. The Reynolds number based on the momentum thickness of the incoming boundary layer is 5000 and the free stream velocity is$25~\text{m}~\text{s}^{-1}$. The proper orthogonal decomposition (POD) is used to separate the different unsteady modes in the flow. The first POD mode contains approximately 30 % of the total kinetic energy and is shown to describe a low-frequency contraction and expansion, called ‘breathing’, of the TSB. This breathing is responsible for a variation in TSB size of approximately 90 % of its average length. It also generates low-frequency wall-pressure fluctuations that are mainly felt upstream of the mean detachment and downstream of the mean reattachment. A medium-frequency unsteadiness, which is linked to the convection of large-scale vortices in the shear layer bounding the recirculation zone and their shedding downstream of the TSB, is also observed. When scaled with the vorticity thickness of the shear layer and the convection velocity of the structures, this medium frequency is very close to the characteristic frequency of vortices convected in turbulent mixing layers. The streamwise position of maximum vertical turbulence intensity generated by the convected structures is located downstream of the mean reattachment line and corresponds to the position of maximum wall-pressure fluctuations.

The Quantification of Internal Noise Levels and Wall Pressure Spectra in Industrial Gas Pipelines

1991 ◽  
Author(s):  
M. P. Norton ◽  
A. Pruiti
Keyword(s):  
Prediction Models ◽  
Transmission Loss ◽  
Pressure Fluctuations ◽  
Internal Noise ◽  
Wall Pressure ◽  
Gas Pipelines ◽  
Noise Levels ◽  
Empirical Prediction ◽  
Wall Pressure Fluctuations ◽  
Semi Empirical

Abstract This paper addresses the issue of quantifying the internal noise levels/wall pressure fluctuations in industrial gas pipelines. This quantification of internal noise levels/wall pressure fluctuations allows for external noise radiation from pipelines to be specified in absolute levels via appropriate noise prediction models. Semi-empirical prediction models based upon (i) estimated vibration levels and radiation ratios, (ii) semi-empirical transmission loss models, and (iii) statistical energy analysis models have already been reported on by Norton and Pruiti 1,3 and are not reported on here.

Prediction of Elbow Flow Dynamics Using Correlated Wall Pressure Data

2018 ◽  
Author(s):  
André Baramili ◽  
Ludovic Chatellier ◽  
Laurent David ◽  
Loïc Ancian
Keyword(s):  
Pressure Fluctuations ◽  
Wall Pressure ◽  
Partial Least Square ◽  
Flow Dynamics ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Multiple Time ◽  
Duct Flow ◽  
Flow Induced Vibration ◽  
Wall Pressure Fluctuations

The present study focuses on the analysis of the flow-induced vibration phenomenon typically encountered on piping systems containing an elbow. The correlation between the turbulent flow through the elbow and the dynamic forcing it yields on the piping walls was assessed experimentally. A closed water loop containing a transparent elbow was designed in order to develop fully turbulent duct flow condition. Particle Image Velocimetry (PIV) was applied in the transparent zone in order to provide unsteady data on the flow dynamics through the elbow; simultaneously, wall pressure fluctuations were measured on and around the elbow. Several flow configurations were tested in order to obtain a large coupled database linking the flow features to the resulting dynamic excitation on the walls. Finally, Partial Least Square Regression (PLSR) was applied in order to harvest the correlated information contained in multiple pressure signals at multiple time-delays and build a relationship capable of estimating the temporal evolution of the velocity field using a set of measured wall pressure signals.

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