Sandia Labs FY21 LDRD Annual Report


Predicting polymer foam deformation leads to accurate modeling for Sandia missions. Many national security applications use polymer foam in transportation, necessitating accurate, predictive modeling capabilities. The three large-deformation constitutive models for flexible polymer foams resulting from this LDRD project are unified across foam densities and based on microstructural and macroscopic data and solid polymer physics. The models vary in maturity: the most mature model, density-as-an-input, is now available for nuclear deterrence use; the anisotropic quasi-linear visco-elastic model incorporating foam anisotropy and solid polymer physics models require some implementation; while the data-driven machine-learned model is the least ready for technical use. In addition to the models, this project discovered the relationship between the evolution of microstructural descriptors and macroscopic deformation and created an extensive data set of large-deformation testing data for a variety of transportation foams, including in situ X-ray computed tomography during deformation at both Sandia and Argonne’s Advanced Photon Source. As a result of this work, the team submitted a patent filing and a technical advance for a high-force computed tomography-compatible load frame. The density- as-an-input model is currently being used to predict abnormal mechanical crash environments. The experimental approach led to material characterization and validation testing for numerous programs, and the models, associated data-science tools, and overall approach transformed Sandia’s ability to predict polymer foam deformation. (PI: Sharlotte Kramer)

An experimental test system is mounted on the centrifuge arm prior to combined environments testing at the Sandia National Laboratories Superfuge/Centrifuge Complex in Albuquerque, New Mexico. (Photo by Byron Demosthenous)



Made with FlippingBook Ebook Creator