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DEWPython: A Python Implementation of the Deep Earth Water Model and Application to Ocean Worlds

Chan, Andrew and Melwani Daswani, Mohit and Vance, Steven (2021) DEWPython: A Python Implementation of the Deep Earth Water Model and Application to Ocean Worlds. . (Unpublished)

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There are two main methods of calculating the thermodynamic properties of water and solutes: mass action (including the Helgeson-Kirkham-Flowers (HKF) equations of state and model) and Gibbs free energy minimization (e.g. Leal et al., 2016). However, in certain regions of pressure and temperature the HKF model inaccurately predicts the speciation and concentration of solutes (e.g. Miron et al., 2019). The Deep Earth Water (DEW) model uses a series of HKF-type equations to calculate the properties of water and solute concentrations at high temperatures (373 - 1473 K) and pressures (0.1 - 6 GPa) (e.g. Huang and Sverjensky, 2019; Pan et al., 2013; Sverjensky et al., 2014). The DEW model is synthesized in an Excel spreadsheet and calculates Gibbs energies of formation, equilibrium constants, and standard volume changes for reactions. Here we present an object-oriented Python implementation of the DEW model, called DEWPython. Our model expands on DEW by increasing model efficiency, streamlining the input process, and incorporating SUPCRT in-line. Additionally, our model builds in minerals and aqueous species from the thermodynamic database slop16.dat (Boyer, 2019) which would normally be calculated separately. We also present a set of reactions relevant to icy ocean world interiors calculated with the DEWPython. The favorability of these reactions indicates likely formation of certain organic species under extreme pressures relevant to ocean worlds.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemCode
Melwani Daswani, Mohit0000-0002-4611-3209
Vance, Steven0000-0002-4242-3293
Additional Information:Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). The Authors would like to acknowledge The Caltech Associates for their contribution to the SURF program which allowed this research to be completed. We thank Jessica Weber (JPL) and H. J. Cleaves (ELSI) for helpful discussions. MMD was partially supported by NASA grants NNH18ZDA001N-HW and NNH19ZDA001N-ECA. A part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). © 2021. All rights reserved. Chan coded DEWPython and wrote the manuscript. Melwani Daswani aided in writing the manuscript, resolving coding issues, provided scientific motivation, and revised the manuscript. Vance served as the advisor to the project and revised the manuscript.
Funding AgencyGrant Number
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Record Number:CaltechAUTHORS:20210622-210015152
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109535
Deposited By: Tony Diaz
Deposited On:23 Jun 2021 19:07
Last Modified:23 Jun 2021 19:07

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