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Magmatic shear bands#
This directory contains magmatic shear bands examples for Newtonian rheology (as defined in Spiegelman, 2003, Linear analysis of melt band formation by simple shear) and for non-linear/power-law rheology (as defined in Katz et al., 2006: The dynamics of melt and shear localization in partially molten aggregates).
Instructions for the magmatic shear bands testcase for Newtonian rheology#
The ASPECT input file for the test case is magmatic_shear_bands.prm.
The scripts ‘run_angle_melt_bands.sh’ and ‘plot_growth_over_angle.py’ can be used to generate data for the growth rate of plane wave melt bands, compare them to the rates predicted from linear stability analysis and plot them over the band angle. ‘run_angle_melt_bands.sh’ outputs the data to the file ‘plane_wave_melt_bands_angle’, and ‘plot_growth_over_angle.py’ plots them and saves them as ‘growth_rate_angle.pdf’.
The scripts ‘run_plane_melt_bands.sh’ and ‘plot_convergence.py’ can be used to generate data for the growth rate error of plane wave melt bands (compared to the rates predicted from linear stability analysis) and plot the error over the number of degrees of freedom. ‘run_plane_melt_bands.sh’ outputs the data to the files ‘plane_wave_melt_bands_8pi’ and ‘plane_wave_melt_bands_16pi’, and ‘plot_convergence.py’ plots them and saves the plot as ‘growth_rate_error.pdf’.
Instructions for the magmatic shear bands testcase for non-linear rheology#
The ASPECT input file for the test case is shear_bands.prm.
The python script ‘plot_band_angle.py’ can be used to plot the porosity field, the fourier transform of the porosity field, and the band angle distribution (of the last time step) for a single model, using a csv file generated by the ‘shear bands statistics’ postprocessor that can be specified as postprocessor in the ASPECT input file.
The python scripts ‘plot_hist_and_porosity.py’ and ‘plot_band_angle_dofs.py’ can be used to plot the porosity field of the last timestep of a shear bands model together with the histograms showing the distribution of band angles. In addition, ‘plot_hist_and_porosity.py’ will generate files that can be used as input for plotting the dominant band angle with ‘plot_band_angle_dofs.py’, which plots the mode of a lognormal distribution fitted to the distribution of band angles. Both scripts will use the data files present in the folder as input if run without arguments, and as default expect that there are data files present for 6…9 global refinements (shear_bands_7.csv … shear_bands_10.csv; shear_bands_7_adaptive.csv … shear_bands_10_adaptive.csv).