BurnMan is an open source mineral physics toolbox written in Python to determine seismic velocities for the lower mantle. BurnMan calculates the isotropic thermoelastic moduli by solving the equations-of-state for a mixture of minerals defined by the user. The user may select from a list of minerals applicable to the lower mantle included or easily define one of their own.
Release v1.2.0 (July 1, 2023) includes
- Two new helper functions:
These functions generate a complete list of reactions (forward and reverse) from either the stoichiometric matrix (a 2D numpy array containing the amount of component j in phase i), or from a list of formulae (as strings or dictionaries of elemental amounts).
- Solution and ElasticSolution objects are now instantiated with a SolutionModel object as a solution_model parameter. The use of the solution_type parameter has been removed completely, along with all of the optional parameters that were originally passed as parameters to SolutionModel.
- A generalised PolynomialSolution class. The non-ideal excesses in this model are polynomial functions of composition. This class can deal with arbitrarily high powers in endmember proportions. However, because the class internally converts the list of lists to numpy arrays, high powers of solutions with a large number of endmembers will create very large arrays (with order n_endmembers^(highest power) elements). This may significantly slow down calculations.
- Five new property modifier formulations, which can be specified with the names "debye", "debye_delta", "einstein", "einstein_delta" and "landau_slb_2022". These are based on the Debye and Einstein models of thermal energy and the Landau model of Stixrude and Lithgow-Bertelloni (2022). The heat capacity ("debye", "einstein") or entropy ("debye_delta", "einstein_delta") are based on the heat capacity of the respective thermal model, and reach a maximum at high temperature. The excess energy, entropy and heat capacity of these four modifiers are all zero at 0 K, and the excess volume is always zero.
- An implementation of the SLB2022 dataset.
- This version is associated with acceptance of the JOSS paper.
Online documentation: https://burnman.readthedocs.io/en/v1.2/
National Science Foundation 1135452