A new MEMS microphone array for the wavenumber analysis of wall-pressure fluctuations: application to the modal investigation of a ducted low-Mach number stage

2019 ◽  
Author(s):  
Edouard Salze ◽  
Emmanuel Jondeau ◽  
Antonio Pereira ◽  
Simon L. Prigent ◽  
Christophe Bailly
Keyword(s):  
Mach Number ◽  
Microphone Array ◽  
Pressure Fluctuations ◽  
Wall Pressure ◽  
Low Mach Number ◽  
Wall Pressure Fluctuations ◽  
Mems Microphone

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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