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Experiments in Fluids

, Volume 53, Issue 2, pp 319–330 | Cite as

Assessment of the derivative-moment transformation method for unsteady-load estimation

  • Ali Mohebbian
  • David E. Rival
Research Article

Abstract

It is often difficult, if not impossible, to measure the aerodynamic or hydrodynamic forces on a moving body. For this reason, a classical control-volume technique is typically applied to extract the unsteady forces. However, measuring the acceleration term within the volume of interest using particle image velocimetry (PIV) can be limited by optical access, reflections, as well as shadows. Therefore, in this study, an alternative approach, termed the derivative-moment transformation (DMT) method, is introduced and tested on a synthetic data set produced using numerical simulations. The test case involves the unsteady loading of a flat plate in a two-dimensional, laminar periodic gust. The results suggest that the DMT method can accurately predict the acceleration term so long as appropriate spatial and temporal resolutions are maintained. The major deficiency, which is more dominant for the direction of drag, was found to be the determination of pressure and unsteady terms in the wake. The effect of control-volume size was investigated, suggesting that larger domains work best by minimizing the associated error in the determination of the pressure field. When decreasing the control-volume size, wake vortices, which produce high gradients across the control surfaces, are found to substantially increase the level of error. On the other hand, it was shown that for large control volumes, and with realistic spatial resolution, the accuracy of the DMT method would also suffer. Therefore, a delicate compromise is required when selecting control-volume size in future experiments.

Keywords

Particle Image Velocimetry Control Volume Aerodynamic Force Control Surface Pressure Term 
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.

Notes

Acknowledgments

The authors would like to acknowledge the useful suggestions provided by the referees.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Mechanical and Manufacturing EngineeringUniversity of CalgaryCalgaryCanada

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