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On Measuring Divergence for Magnetic Field Modeling by Gilchrist, S. A.; Leka, K. D.; Barnes, G.; Wheatland, M. S.; DeRosa, M. L. is a scholarly article available to read on EtoBox.

What is On Measuring Divergence for Magnetic Field Modeling about?

A physical magnetic field has a divergence of zero. Numerical error in constructing a model field and computing the divergence, however, introduces a finite divergence into these calculations. A popular metric for measuring divergence is the average fractional flux $\langle |f_{i}| \rangle$. We show that $\langle |f_{i}| \rangle$ scales with the size of the computational mesh, and may be a poor measure of divergence because it becomes arbitrarily small for increasing mesh resolution, without the divergence actually decreasing. We define a modified version of this metric that does not scale with mesh size. We apply the new metric to the results of DeRosa et al. (2015), who measured $\langle |f_{i}| \rangle$ for a series of Nonlinear Force-Free Field (NLFFF) models of the coronal magnetic field based on solar boundary data binned at different spatial resolutions. We compute a number of divergence metrics for the DeRosa et al. (2015) data and analyze the effect of spatial resolution on these metrics using a non-parametric method. We find that some of the trends reported by DeRosa et al. (2015) are due to the intrinsic scaling of $\langle |f_{i}| \rangle$. We also find that different m

Author

Gilchrist, S. A.; Leka, K. D.; Barnes, G.; Wheatland, M. S.; DeRosa, M. L.

Published

2020

Language

EN