Assessing Convergence in Predictions of Periodic-Unsteady Flowfields
Abstract
Here we report on a method developed to determine the level of convergence in a predicted flowfield that is characterized by periodic-unsteadiness. The method relies on fundamental concepts from digital signal processing including the discrete Fourier transform, cross-correlation, and Parseval's theorem. Often in predictions of vane-blade interaction in turbomachines, the period of the unsteady fluctuations is expected. In this method, the development of time-mean quantities, Fourier components (both magnitude and phase), cross-correlations, and integrated signal power are tracked at locations of interest from one period to the next as the solution progresses. Each of these separate quantities yields some relative measure of convergence that is subsequently processed to form a fuzzy set. Thus the overall level of convergence in the solution is given by the intersection of these sets. It is shown that the method yields a robust determination of convergence. In addition, the method is useful for the detection of inherent unsteadiness in the flowfield, and as such it can be used to prevent design escapes.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2006
- Accession Number
- ADA456018
Entities
People
- E. A. Grover
- J. P. Clark
Organizations
- Air Force Research Laboratory