Survey and analysis of multiresolution methods for turbulence data
Abstract
This paper compares the effectiveness of various multiresolution geometric representation methods, such as Bspline, Daubechies, Coiflet and Dualtree wavelets, curvelets and surfacelets, to capture the structure of fully developed turbulence using a truncated set of coefficients. The turbulence dataset is obtained from a Direct Numerical Simulation of buoyancy driven turbulence on a 512^{3} mesh size, with an Atwood number, A = 0.05, and turbulent Reynolds number, Re_{t} = 1800, and the methods are tested against quantities pertaining to both velocities and active scalar (density) fields and their derivatives, spectra, and the properties of constant density surfaces. The comparisons between the algorithms are given in terms of performance, accuracy, and compression properties. The results should provide useful information for multiresolution analysis of turbulence, coherent feature extraction, compression for large datasets handling, as well as simulations algorithms based on multiresolution methods. In conclusion, the final section provides recommendations for best decomposition algorithms based on several metrics related to computational efficiency and preservation of turbulence properties using a reduced set of coefficients.
 Authors:

 Univ. of California, Davis, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Univ. of California, Davis, CA (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1254843
 Alternate Identifier(s):
 OSTI ID: 1358985
 Report Number(s):
 LAUR1520966
Journal ID: ISSN 00457930; PII: S004579301500362X
 Grant/Contract Number:
 AC5206NA25396
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Computers and Fluids
 Additional Journal Information:
 Journal Volume: 125; Journal Issue: C; Journal ID: ISSN 00457930
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; Turbulence; wavelet; curvelet; surfacelet; Bspline wavelet; multiresolution
Citation Formats
Pulido, Jesus, Livescu, Daniel, Woodring, Jonathan, Ahrens, James, and Hamann, Bernd. Survey and analysis of multiresolution methods for turbulence data. United States: N. p., 2015.
Web. doi:10.1016/j.compfluid.2015.11.001.
Pulido, Jesus, Livescu, Daniel, Woodring, Jonathan, Ahrens, James, & Hamann, Bernd. Survey and analysis of multiresolution methods for turbulence data. United States. doi:10.1016/j.compfluid.2015.11.001.
Pulido, Jesus, Livescu, Daniel, Woodring, Jonathan, Ahrens, James, and Hamann, Bernd. Tue .
"Survey and analysis of multiresolution methods for turbulence data". United States. doi:10.1016/j.compfluid.2015.11.001. https://www.osti.gov/servlets/purl/1254843.
@article{osti_1254843,
title = {Survey and analysis of multiresolution methods for turbulence data},
author = {Pulido, Jesus and Livescu, Daniel and Woodring, Jonathan and Ahrens, James and Hamann, Bernd},
abstractNote = {This paper compares the effectiveness of various multiresolution geometric representation methods, such as Bspline, Daubechies, Coiflet and Dualtree wavelets, curvelets and surfacelets, to capture the structure of fully developed turbulence using a truncated set of coefficients. The turbulence dataset is obtained from a Direct Numerical Simulation of buoyancy driven turbulence on a 5123 mesh size, with an Atwood number, A = 0.05, and turbulent Reynolds number, Ret = 1800, and the methods are tested against quantities pertaining to both velocities and active scalar (density) fields and their derivatives, spectra, and the properties of constant density surfaces. The comparisons between the algorithms are given in terms of performance, accuracy, and compression properties. The results should provide useful information for multiresolution analysis of turbulence, coherent feature extraction, compression for large datasets handling, as well as simulations algorithms based on multiresolution methods. In conclusion, the final section provides recommendations for best decomposition algorithms based on several metrics related to computational efficiency and preservation of turbulence properties using a reduced set of coefficients.},
doi = {10.1016/j.compfluid.2015.11.001},
journal = {Computers and Fluids},
number = C,
volume = 125,
place = {United States},
year = {2015},
month = {11}
}
Web of Science
Works referencing / citing this record:
Data Reduction Techniques for Simulation, Visualization and Data Analysis: Survey on Scientific Data Reduction Techniques
journal, March 2018
 Li, S.; Marsaglia, N.; Garth, C.
 Computer Graphics Forum, Vol. 37, Issue 6