IOR Uncertainty Quantification for PIV/LDV Measurements

In summary, the conversation discusses the use of Particle Image Velocimetry and Laser Doppler Velocimetry techniques in experiments. There is a mismatch in the Refractive Index of the fluid and model, leading to uncertainty in the measurements. Suggestions are made to conduct a sensitivity analysis to quantify the uncertainty caused by the variation in refractive index.
  • #1
C. C.
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0
Hello All,
I am running some experiments using Particle Image Velocimetry and Laser Doppler Velocimetry techniques. There is a slight mismatch in the Refractive Index of the fluid and my model (Approximately 5% difference) which is creating some uncertainty in my measurements. I was wondering if anyone has any ideas as to how I would be able to quantify the uncertainty in my calculations knowing that the refractive index of the fluid (n1) and model (n2). For simplicity, the model I am using is a spherical shell with the fluid inside. If someone can direct me toward finding a solution, it would be appreciated.
 
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  • #2
Thanks,KiranIt depends on the specific optical setup you are using for your experiment. Generally speaking, the mismatch in refractive index will cause some amount of light scattering, which can affect the accuracy of the measurements. The best way to quantify the uncertainty is to conduct a sensitivity analysis, where you measure the effects of varying the refractive indices in small increments, and then observe the changes in your measurements. This will enable you to understand the magnitude of the uncertainty associated with the variation in refractive index.
 

1. What is IOR uncertainty quantification?

IOR uncertainty quantification is a method used in particle image velocimetry (PIV) and laser Doppler velocimetry (LDV) measurements to determine the uncertainty or error in the calculated velocity values. It takes into account the sources of error in the measurement process, such as camera pixel size, laser beam width, and particle image displacement, to provide a more accurate estimation of the measured velocity values.

2. Why is IOR uncertainty quantification important in PIV/LDV measurements?

IOR uncertainty quantification is important because it allows for a more accurate and reliable interpretation of the velocity data obtained from PIV/LDV measurements. By accounting for sources of error, the uncertainty in the measured values can be reduced, providing more confidence in the results and allowing for more meaningful analysis and comparison with theoretical or numerical models.

3. How is IOR uncertainty quantification calculated?

IOR uncertainty quantification is typically calculated using statistical methods, such as error propagation or Monte Carlo simulations. These methods involve analyzing the sources of error and their associated uncertainties, and then propagating these uncertainties through the PIV/LDV measurement process to determine the overall uncertainty in the calculated velocity values.

4. What are some common sources of error in PIV/LDV measurements?

Some common sources of error in PIV/LDV measurements include camera noise, particle image blur, and imperfect seeding of particles in the flow. Other sources of error can include variations in camera and laser calibration, and the presence of ambient light or reflections in the measurement area.

5. How can IOR uncertainty quantification be used to improve PIV/LDV measurements?

IOR uncertainty quantification can be used to improve PIV/LDV measurements by providing a more accurate estimation of the measured velocity values. This can help to identify and eliminate any sources of error, and also allows for better comparison and validation of the measured data with theoretical or numerical models. Additionally, by understanding the level of uncertainty in the measurements, researchers can make more informed decisions about the significance and reliability of their results.

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