Experiments in Fluids

, 55:1802 | Cite as

Error reduction in molecular tagging velocimetry via image preprocessing

  • Michael Caso
  • Douglas Bohl
Research Article


The effect of preprocessing molecular tagging velocimetry (MTV) images to reduce measurement error was studied using simulated and experimental images with signal-to-noise (SN) ratios of SN = 2–16. The results of the simulations showed that image filtering reduced the measurement error by up to 30 % for conditions typically seen in real-world MTV experiments. Under some conditions (i.e., thin lines or large spatial filters), filtering was found to increase the measurement error. Experiments confirmed the simulation results, although the actual error levels were higher. The use of an averaged initial or “undelayed” image, instead of individual undelayed images, was also investigated. This strategy increased the SN of the undelayed image by averaging out the random noise. It was shown that the use of an averaged undelayed image reduced error for low SN images but potentially increased error for high SN images.


Shot Noise Error Level Error Reduction Line Thickness Bright Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported through NSF Award# 8045882.


  1. Bohl DG (2002) Experimental study of the 2-d and 3-d structure of a concentrated line vortex. Department of Mechanical Engineering. East Lansing, Michigan State University, PhDGoogle Scholar
  2. Bohl D, Koochesfahani M (2009) MTV measurements of the vortical field in the wake of an airfoil oscillating at high reduced frequency. J Fluid Mech 620:63–88zbMATHCrossRefGoogle Scholar
  3. Bohl DG, Koochesfahani MM et al (2001) Development of stereoscopic molecular tagging velocimetry. Exp Fluids 30(3):302–308CrossRefGoogle Scholar
  4. Dussault D, Hoess P (2004) Noise performance comparison of ICCD with CCD and EMCCD cameras 5563. Infrared Systems and Photonics Technology, The International Society for Optical Engineering, Denver, COGoogle Scholar
  5. Gendrich CP, Koochesfahani MM (1996) A spatial correlation technique for estimating velocity fields using molecular tagging velocimetry (MTV). Exp Fluids 22(1):67–77CrossRefGoogle Scholar
  6. Gendrich CP, Koochesfahani MM et al (1997) Molecular tagging velocimetry and other novel applications of a new phosphorescent supramolecule. Exp Fluids 23(5):361–372CrossRefGoogle Scholar
  7. Hammer P, Pouya S et al. (2013) A multi-time-delay approach for correction of the inherent error in single-component molecular tagging velocimetry. Meas Sci Technol 24(10):Art No 105302Google Scholar
  8. Hill RB, Klewicki JC (1996) Data reduction methods for flow tagging velocity measurements. Exp Fluids 20(3):142–152CrossRefGoogle Scholar
  9. Ke JH, Bohl D (2011) Effect of experimental parameters and image noise on the error levels in simultaneous velocity and temperature measurements using molecular tagging velocimetry/thermometry (MTV/T). Exp Fluids 50(2):465–478CrossRefGoogle Scholar
  10. Koochesfahani M, Nocera D (2007) Molecular tagging velocimetry. In: Foss J, Tropea C, Yarin A (eds) Handbook of experimental fluid dynamics. Springer, BerlinGoogle Scholar
  11. Maynes D, Webb AR (2002) Velocity profile characterization in sub-millimeter diameter tubes using molecular tagging velocimetry. Exp Fluids 32(1):3–15CrossRefGoogle Scholar
  12. Mittal M, Sadr R et al (2009) In-cylinder engine flow measurement using stereoscopic molecular tagging velocimetry (SMTV). Exp Fluids 46(2):277–284CrossRefGoogle Scholar
  13. Ramsey MC, Pitz RW (2011) Template matching for improved accuracy in molecular tagging velocimetry. Exp Fluids 51(3):811–819CrossRefGoogle Scholar
  14. Sadr R, Klewicki JC (2003) A spline-based technique for estimating flow velocities using two-camera multi-line MTV. Exp Fluids 35(3):257–261CrossRefGoogle Scholar
  15. Zheng QX, Klewicki JC (2000) A fast data reduction algorithm for molecular tagging velocimetry: the decoupled spatial correlation technique. Meas Sci Technol 11(9):1282–1288CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Mechanical and Aeronautical EngineeringClarkson UniversityPotsdamUSA

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