Results for “discrete dislocation dynamics”

NASA NTRS2014

Multiscale Analysis of Structurally-Graded Microstructures Using Molecular Dynamics, Discrete Dislocation Dynamics and Continuum Crystal Plasticity

A multiscale modeling methodology is developed for structurally-graded material microstructures. Molecular dynamic (MD) simulations are performed at the nanoscale to determine fundamental failure mechanisms and quantify material constitutive parameters. These parameters are used to calibrate material processes at the mesoscale using discrete dislocation dynamics (DD). Different grain boundary interactions with dislocations are analyzed using DD to predict grain-size dependent stress-strain behavior. These relationships are mapped into crystal plasticity (CP) parameters to develop a computationally efficient finite element-based DD/CP model for continuum-level simulations and complete the multiscale analysis by predicting the behavior of macroscopic physical specimens. The present analysis is focused on simulating the behavior of a graded microstructure in which grain sizes are on the order of nanometers in the exterior region and transition to larger, multi-micron size in the interior domain. This microstructural configuration has been shown to offer improved mechanical properties over homogeneous coarse-grained materials by increasing yield stress while maintaining ductility. Various mesoscopic polycrystal models of structurally-graded microstructures are generated, analyzed and used as a benchmark for comparison between multiscale DD/CP model and DD predictions. A final series of simulations utilize the DD/CP analysis method exclusively to study macroscopic models that cannot be analyzed by MD or DD methods alone due to the model size.

Saether, Erik↗

NASA NTRS2020

Toward an In-Depth Material Model for Cermet Nuclear Thermal Rocket Fuel Elements

The development and qualification of nuclear thermal propulsion (NTP) fuel element technologies would be aided by an in-depth model of material response and failure modes at operating conditions. Integrated computational materials engineering techniques have the potential to provide such a model, as demonstrated here through three case studies focused on a tungsten-uranium mononitride cermet fuel. The first case focuses on the erosion of tungsten (W, also named wolfram), a nominal coating/cladding material, in hot hydrogen. Ab initio techniques are used to calculate erosion rates and thermal expansion at NTP operating conditions. The second focuses on the stability of uranium mononitride (UN) fuels at high temperature and in the presence of hydrogen. Phase diagram techniques reveal potential instabilities and decomposition pathways at high hydrogen concentrations. The third focuses on using microstructure information to predict high temperature mechanical response and failure of tungsten, used in refractory cermet materials. Combined finite element and discrete dislocation dynamics techniques provide mechanical properties in agreement with experimental methods. The integration of these techniques for an all-encompassing material model is discussed.

cermet↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

The microstructure of metals is key to their mechanical properties. The types, density, composition and morphology of crystal defects all have pronounced impact on the properties. Changes to the microstructure occurring during processing and use can be very striking. The emerging technology additive manufacturing (AM) has the potential to improve performance by allowing optimized designs, but the process and environments can lead to unusual microscale features whose properties must be understood and characterized to enable higher technological readiness levels and application. Experimentally, an extensive evaluation of mechanical properties of 3D printed metals is a challenge, and anomalous effects related to the AM process add complexity. We present a new machine learning (ML) model predicting mechanical response based on dislocation mediated plasticity simulations. A large set of 3D discrete dislocation dynamics simulations with wide ranges of loading conditions is transformed to preprocessed data ready for training with the ML model. The trained model can predict the mechanical response of Mo30W for a given microstructure evolution, providing key information essential for optimization of AM processing.

Jaehyun Cho↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

The microstructure of metals is key to their mechanical properties. The types, density, composition and morphology of crystal defects all have pronounced impact on the properties. Changes to the microstructure occurring during processing and use can be very striking. The emerging technology additive manufacturing (AM) has the potential to improve performance by allowing optimized designs, but the process and environments can lead to unusual microscale features whose properties must be understood and characterized to enable higher technological readiness levels and application. Experimentally, an extensive evaluation of mechanical properties of 3D printed metals is a challenge, and anomalous effects related to the AM process add complexity. We present a new machine learning (ML) model predicting mechanical response based on dislocation mediated plasticity simulations. A large set of 3D discrete dislocation dynamics simulations with wide ranges of loading conditions is transformed to preprocessed data ready for training with the ML model. The trained model can predict the mechanical response of Mo30W for a given microstructure evolution, providing key information essential for optimization of AM processing.

Jaehyun Cho↗

NASA NTRS2012

Collisional Histories of Comets and Trojan Asteroids: Diopside, Magnesite, and Fayalite Impact Studies

Comets and asteroids have weathered dynamic histories, as evidenced by their rough surfaces. The Nice model describes a violent reshuffling of small bodies during the Late Heavy Bombardment, with collisions acting to grind these planetesimals away. This creates an additional source of impact material that can re-work the surfaces of the larger bodies over the lifetime of the solar system. Here, we investigate the possibility that signatures due to impacts (e.g. from micrometeoroids or meteoroids) could be detected in their spectra, and how that can be explained by the physical manifestation of shock in the crystalline structure of minerals. All impact experiments were conducted in the Johnson Space Center Experimental Impact Laboratory using the vertical gun. Impact speeds ranged from approx.2.0 km/s to approx.2.8 km/s. All experiments were conducted at room temperature. Minerals found in comets and asteroids were chosen as targets, including diopside (MgCaSi2O6, monoclinic pyroxene), magnesite (MgCO3, carbonate), and fayalite (FeSiO4, olivine). Impacted samples were analyzed using a Fourier Transform Infrared Spectrometer (FTIR) and a Transmission Electron Microscope (TEM). Absorbance features in the 8-13 m spectral region demonstrate relative amplitude changes as well as wavelength shifts. Corresponding TEM images exhibit planar shock dislocations in the crystalline structure, attributed to deformation at high strain and low temperatures. Elongating or shortening the axes of the crystalline structure of forsterite (Mg2SiO4, olivine) using a discrete dipole approximation model (Lindsay et al., submitted) yields changes in spectral features similar to those observed in our impacted laboratory minerals.

Lederer, S. M.↗