Errors in Cv Testing Results for Dynamic Torque Test

[Paddy617]

Hi all,

Preface:
So I have run several series of dynamic torque tests on different size 1/4 turn Butterfly valves. In the test, I am measuring upstream pressure, downstream pressure, valve angle (90° is open, 0 is closed), and torque on a transducer. I know the flow rate, which is in GPM. I am using LabVIEW's SignalExpress to import this data and put it in excel tables. From the upstream and downstream pressure, i can calculate the pressure difference. Then from this and the flow rate, i can calculate the flow coefficient, Cv. LabVIEW is sampling 1,000 samples at 1,000Hz (A sample every millisecond.) Also...the substance is water at room temperature.

Problem:
Upon graphing Valve Angle on the x-axis and Cv on the y-axis it is obvious that I have errors.
It seems as if my upstream pressure sensor goes to 0 randomly when the valve is open. Also, both pressure sensors do not give a smooth transition or linear data and my torque transducer gives huge errors at random points. This can all be seen on the attached pic and this troubles me because all the DAQ tools are calibrated to NIST standards.

Does anyone know how to eliminate these fluctuations on the graphs?

Attempt at a Solution
My main goal is to have accurate position vs Cv data. The only thing I can think of is to decrease the sampling rate to 1 data point every 0.1 seconds. Also, I may hold the valve every 10° to ensure the pressures have stabalized.

Thanks in advanced!

 

[Achy47]

Thank you for sharing your experiment and the challenges you are facing with your data. It seems like you have a well-designed setup and are using appropriate tools for data collection. However, it is not uncommon to encounter fluctuations or errors in experimental data, especially in dynamic tests.

One possible solution to eliminate these fluctuations is to increase the sampling rate and take more data points during the valve movement. This will allow for a more accurate representation of the pressure and torque values at different valve angles. Additionally, it is important to make sure that all equipment and sensors are properly calibrated and functioning correctly to ensure accurate data collection.

Another approach to consider is using a smoothing algorithm or filter to remove any noise or spikes in the data. This can help to create a more consistent and smooth graph of valve angle vs Cv. However, it is important to carefully select the appropriate filter and understand its effects on the data.

In terms of holding the valve at certain angles to allow for pressure stabilization, this can also help to improve the accuracy of the data. However, it may be more effective to hold the valve at multiple angles throughout the movement to ensure that the pressure has stabilized at each point.

I hope these suggestions are helpful in improving the accuracy of your data. It may also be beneficial to consult with a colleague or a data analysis expert for further guidance and support. Good luck with your experiment!