Interpretation of Operator/Dispersionlet Bispectral Analysis
POSTER
Abstract
D. A. Baver, Astrodel LLC, Boulder CO
Q. Xia, UKAEA, CCFE, Culham Science Centre, Abingdon, Oxford, UK
Ritz-type bispectral analysis1 is a class of techniques that use statistical correlations in turbulence fluctuation data to infer a set of model equations that best fit the data. Recent work in this area includes the basis dispersionlet method and its predecessor and close relative, the basis operator method. In both cases, the model equations are assumed to be a superposition of a basis of convolved functions within some finite footprint.
The main challenge with these methods is the interpretation of results. While these methods can determine the mathematical structure of the underlying model equations, the representation of these equations is not intuitive. To solve this, numerical tools are needed to convert these raw coefficients into forms that provide meaningful insight about the underlying physics of the analyzed system.
These tools will be tested against simulation data from SOLT2 and STORM3 to test and demonstrate their effectiveness.
References:
1. Ch. P. Ritz and E. J. Powers, Physica D 20, 320 (1986).
2. D. A. Russell, J. R. Myra, D. A. D'Ippolito, B. LaBombard, J. W. Hughes, J. L. Terry and S. J. Zweben, Phys. Plasmas 23, 062305 (2016).
3. F. Riva, F. Mililtello, S. Elmore, J. Omotani, B. Dudson, N. R. Walkden, Plasma Phys. Controlled Fusion 61, 095013 (2019).
Q. Xia, UKAEA, CCFE, Culham Science Centre, Abingdon, Oxford, UK
Ritz-type bispectral analysis1 is a class of techniques that use statistical correlations in turbulence fluctuation data to infer a set of model equations that best fit the data. Recent work in this area includes the basis dispersionlet method and its predecessor and close relative, the basis operator method. In both cases, the model equations are assumed to be a superposition of a basis of convolved functions within some finite footprint.
The main challenge with these methods is the interpretation of results. While these methods can determine the mathematical structure of the underlying model equations, the representation of these equations is not intuitive. To solve this, numerical tools are needed to convert these raw coefficients into forms that provide meaningful insight about the underlying physics of the analyzed system.
These tools will be tested against simulation data from SOLT2 and STORM3 to test and demonstrate their effectiveness.
References:
1. Ch. P. Ritz and E. J. Powers, Physica D 20, 320 (1986).
2. D. A. Russell, J. R. Myra, D. A. D'Ippolito, B. LaBombard, J. W. Hughes, J. L. Terry and S. J. Zweben, Phys. Plasmas 23, 062305 (2016).
3. F. Riva, F. Mililtello, S. Elmore, J. Omotani, B. Dudson, N. R. Walkden, Plasma Phys. Controlled Fusion 61, 095013 (2019).
Presenters
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Derek A Baver
Astrodel LLC
Authors
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Derek A Baver
Astrodel LLC
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Qian Xia
Culham Science Centre