Predicting Vortex-shedding Induced Pulsation Amplitudes in Piping Systems
Recent technological advances in the use of hydraulic fracturing to extract natural gas from shale gas deposits have caused a rise in the installation and expansion of centrifugal compressor stations to transport the gas. One of the design criteria for centrifugal compressor piping systems is the prevention of piping and valve failures from high pulsation amplitudes and vibration caused by vortex-shedding induced (VSI) pulsations of gas flow past closed piping branches. Most vortex-shedding analyses are performed on the basis of frequency avoidance. If a coincidence is predicted between the acoustic natural frequency of a piping segment and the flow induced vortex shedding frequency, piping changes must be made to avoid the coincidence, since there is currently no known method for accurately predicting the severity of the resulting pulsation amplitudes and, therefore, the possibility of piping failures. Often, these piping changes are expensive and time consuming and likely unnecessary for smaller bore piping (10-inch or less), assuming the flow-acoustic coincidence does not coincide with a mechanical resonance. The majority of research performed to date on vortex-shedding excitation of piping stubs focuses on predicting the frequencies of excitation using an experimentally determined Strouhal number or range of numbers for a given geometry or piping feature. However, to properly design a piping system using a combination of support structures and piping changes, reliable prediction of the pulsation amplitude is essential for determining the shaking forces acting on the piping system and the resulting vibration and stress amplitudes. Due to the expense of experimental testing and availability of equipment, it is difficult to obtain accurate amplitude measurements of VSI pulsations of fluids at Reynolds numbers typically associated with natural gas transmission. This paper will discuss the experimental testing performed and the development of a predictive method for
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