A particle image velocimetry (PIV) computer software is analyzed in this work by applying automatic differentiation on it. We create two artificial images that contained particles that where moved with a known velocity field over time. These artificial images were created with parameters that we would have on real PIV experiments. Then we applied a PIV software to find the velocity output vectors. As we mentioned before, we applied automatic differentiation through all the algorithm to track the derivatives of the output vectors regarding interesting parameters declared as inputs. By analyzing these derivatives we analyze the sensitivity of the output vectors to changes on each one of the parameters analyzed. One of the most important derivatives calculated in this project was the derivative of the output regarding the image intensity. In future work we plan to use this derivative combined with the intensity probability distribution of each image pixel, to find PIV uncertainties. If we achieve this goal we will find an uncertainty method that will save computational power and will give uncertainty values with computer accuracy.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc955037 |
| Date | 12 1900 |
| Creators | Grullon Varela, Rodolfo Antonio |
| Contributors | Horne, Kyle, Shi, Sheldon, Zhao, Weihuan |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Grullon Varela, Rodolfo Antonio, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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