Engineering topics

Bennett, Kane C.

Publications and source records attributed to Bennett, Kane C..

4 records

DOE OSTI2024

An integrated EOS, pore‐crush, strength and damage model framework for near‐field ground‐shock

Abstract An integrated Equation of State (EOS) and strength/pore‐crush/damage model framework is provided for modeling near to source (near‐field) ground‐shock response, where large deformations and pressures necessitate coupling EOS with pressure‐dependent plastic yield and damage. Nonlinear pressure‐dependence of strength up to high‐pressures is combined with a Modified Cam‐Clay‐like cap‐plasticity model in a way to allow degradation of strength from pore‐crush damage, what we call the “Yp‐Cap” model. Nonlinear hardening under compaction allows modeling the crush‐out of pores in combination with a fully saturated EOS, that is, for modeling partially saturated ground‐shock response, where air‐filled voids crush. Attention is given to algorithmic clarity and efficiency of the provided model, and the model is employed in example numerical simulations, including finite element simulations of underground explosions to exemplify its robustness and utility.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Multiscale numerical investigation of ratchet growth damage effects in PBX 9502

This paper presents results of numerical experiments conducted on the high explosive PBX 9502 to investigate how recently observed grain-scale damage mechanisms of ratchet growth affect uniaxial compression measurements. Simulations are multiscale in the sense of directly resolving grains, pores, cracks, and grain-interfaces based upon scanning electron microscope (SEM) images of damaged and undamaged samples. The combined finite-discrete element method (FDEM) is utilized to resolve both grain-scale microfracture and elastoplastic deformation of solid grains. Pristine (undamaged) and damaged microstructures are compared in simulation of unconfined compression tests of the same material from the literature. Here, the simulation results show the observed microscale mechanisms of damage, specifically microfracture predominantly around and sometimes through grains and crack-associated pore growth, can well-explain the effective degradation of strength and stiffness observed in the laboratory measurements.

36 MATERIALS SCIENCE↗

DOE OSTI2023

An extended three-field principle to scale-bridge the granular micromechanics of polymer-bonded particulate materials

Observed phenomena of damage, viscoelasticity, and viscoplasticity in polymer-bonded particulate materials (PBPM), like polymer-bonded explosive (PBX), are accommodated in a scale-bridging constitutive model based on granular micromechanics. An extended three-field variational principle with min - max optimization problem is introduced to homogenize the bonded aggregates’ grain-scale contact mechanics. Similarly to prior applications of a three-field principle, volume constraints are invoked to embed microscale-to-macroscale correspondences for kinematic and static volumetric measures, like mean strain and its energy-conjugate stress invariant. Unlike prior applications, our three-field principle realizes a strain-driven model for effective constitutive behavior due to bonded intergranular contact. In conclusion, the resultant micro-sphere integration-based homogenization is demonstrated in comparison to existing publicly-available measurements to capture the extreme tension/compression constitutive asymmetry exhibited by PBPMs (specifically PBX-9501) during material softening.

42 ENGINEERING↗

DOE OSTI2022

Predicting Future Laboratory Fault Friction Through Deep Learning Transformer Models

Machine learning models using seismic emissions as input can predict instantaneous fault characteristics such as displacement and friction in laboratory experiments, and slow slip in Earth. Here, we address whether the seismic/acoustic emission (AE) from laboratory experiments contains information about future frictional behavior. The approach uses a convolutional encoder-decoder containing a transformer model in the latent space, similar to models used for natural language processing. We test the model limits using progressively larger AE input time windows and progressively larger output friction time windows. The results demonstrate that very near-term friction predictions are indeed contained in the AE signal, and predictions are progressively worse farther into the future. The future predictions by the model of impending failure in the near-term are remarkably robust. This first effort predicting future fault frictional behavior with machine learning will aid in guiding efforts for applications in Earth.

58 GEOSCIENCES↗