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Title: A novel machine learning-based framework to predict the anisotropic mechanical properties in soft materials using anisotropic indentation
Award ID(s):
1761432
NSF-PAR ID:
10332995
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2022 Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C2022)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract

    Current models for elastic geobarometry have been developed with the assumption that the host and/or inclusion minerals are elastically isotropic. This assumption has limited applications of elastic thermobarometry to mineral inclusions contained in cubic quasi‐isotropic host minerals (e.g., garnet). Here, we report a new elastic model that takes into account the anisotropic elastic properties and relative crystallographic orientation (RCO) of a host‐inclusion system where both minerals are noncubic. This anisotropic elastic model can be used for host‐inclusion elastic thermobarometric calculations provided that the RCO and elastic properties of both the host and inclusion are known. We then used this anisotropic elastic model to numerically evaluate the effects of elastic anisotropy and RCO on the strains and stresses developed in a quartz inclusion entrapped in a zircon host after exhumation from known entrapmentPTconditions to roomPTconditions. We conclude that the anisotropic quartz‐in‐zircon elastic model is suitable for elastic thermobarometry and may be widely applicable to crustal rocks. Our results demonstrate that isotropic elastic models cannot be used to determine the entire strain state of an elastically anisotropic inclusion contained in an elastically anisotropic host mineral, and therefore may lead to errors on estimated remnant inclusion pressures.

     
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