Limiting velocities and transonic dislocations in Mg

To accurately predict the mechanical response of materials, especially at high strain rates, it is important to account for dislocation velocities in these regimes. Under these extreme conditions, it has been hypothesized that dislocations can move faster than the speed of sound. However, the presence of such dislocations remains elusive due to challenges associated with measuring these experimentally. In this work, molecular dynamics simulations were used to investigate the dislocation velocities for the basal edge, basal screw, prismatic edge, and prismatic screw dislocations in Mg in the sub-, trans-, and supersonic regimes. Our results show that only prismatic edge dislocations achieve supersonic velocities. Furthermore, this work demonstrates that the discrepancy between the theoretical limiting velocity and the MD results for Mg is due to its sensitivity to large hydrostatic stress around the dislocation core, which was not the case for fcc metals such as Cu.Comment: 7 pages, 4 figures; v2 clarifications and additional result

Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.Comment: 13 pages, 9 figures; v2 minor revisio

Structural disjoining potential for grain boundary premelting and grain coalescence from molecular-dynamics simulations

We describe a molecular dynamics framework for the direct calculation of the short-ranged structural forces underlying grain-boundary premelting and grain-coalescence in solidification. The method is applied in a comparative study of (i) a Sigma 9 120 degress twist and (ii) a Sigma 9 {411} symmetric tilt boundary in a classical embedded-atom model of elemental Ni. Although both boundaries feature highly disordered structures near the melting point, the nature of the temperature dependence of the width of the disordered regions in these boundaries is qualitatively different. The former boundary displays behavior consistent with a logarithmically diverging premelted layer thickness as the melting temperature is approached from below, while the latter displays behavior featuring a finite grain-boundary width at the melting point. It is demonstrated that both types of behavior can be quantitatively described within a sharp-interface thermodynamic formalism involving a width-dependent interfacial free energy, referred to as the disjoining potential. The disjoining potential for boundary (i) is calculated to display a monotonic exponential dependence on width, while that of boundary (ii) features a weak attractive minimum. The results of this work are discussed in relation to recent simulation and theoretical studies of the thermodynamic forces underlying grain-boundary premelting.Comment: 24 pages, 8 figures, 1 tabl

Perspectives on Novel Refractory Amorphous High-Entropy Alloys in Extreme Environments

Two new refractory amorphous high-entropy alloys (RAHEAs) within the W--Ta--Cr--V and W--Ta--Cr--V--Hf systems were herein synthesized using magnetron-sputtering and tested under high-temperature annealing and displacing irradiation using \textit{in situ} Transmission Electron Microscopy. While the WTaCrV RAHEA was found to be unstable under such tests, additions of Hf in this system composing a new quinary WTaCrVHf RAHEA was found to be a route to achieve stability both under annealing and irradiation. A new effect of nanoprecipitate reassembling observed to take place within the WTaCrVHf RAHEA under irradiation indicates that a duplex microstructure composed of an amorphous matrix with crystalline nanometer-sized precipitates enhances the radiation response of the system. It is demonstrated that tunable chemical complexity arises as a new alloy design strategy to foster the use of novel RAHEAs within extreme environments. New perspectives for the alloy design and application of chemically-complex amorphous metallic alloys in extreme environments are presented with focus on their thermodynamic phase stability when subjected to high-temperature annealing and displacing irradiation

Photonic Doppler velocimetry probe used to measure grain boundaries of dynamic shocked materials

Author Institution: Mission Support and Test Services, LLC; Los Alamos National LaboratorySlides presented at the 2018 Photonic Doppler Velocimetry (PDV) Users Workshop, Drury Plaza Hotel, Santa Fe, New Mexico, May 16-18, 2018

Strain-rate effects and dynamic behavior of high entropy alloys

The novel class of multicomponent alloys, also known as high-entropy alloys (HEAs) exhibits excellent properties under low strain-rate conditions. These are especially revealed in the high strength of nanocrystalline CoCrFeMnNi and AlNbTiV alloys, and in the high fracture toughness of AlCoCrCuFeNi and NbMoTaW alloys. Nevertheless, up to now, the dynamic behavior of these high-entropy alloys has not been investigated to the same extent as the quasi-static response. A significantly different mechanical response, such as spallation failure and shear localization, manifests itself when materials are subjected to dynamic loading. Shear localization is an essential precursor to shear failure; studies addressing retardation of its onset are important because of their relevance to applications such as armor for military use. The resistance to shear localization is associated with the extensive work hardening ability enabled by dislocation slip, twinning, and phase transformations which override thermal softening. Apart from shear localization, in contrast to the conventional fracture failure dictated by fracture toughness, spallation resulting from tensile pulses and involving propagating micro-cracks and/or micro-voids also plays an important role in dynamic performance. Although distinctive behaviors have also been reported for other conventional metallic materials under dynamic loading, the unique characteristics of HEAs warrant this review

Microstructure Based Failure Criterion For Ductile Materials

For ductile metals, the process of dynamic fracture occurs through nucleation, growth and coalescence of voids. The stress required to nucleate these voids is inferred from the velocimetry data (using the acoustic approach) and termed as the spall strength. This is a key parameter that is used to evaluate a material’s susceptibility to damage and failure. However, it is also well recognized that the dynamic parameters used to generate the shock state such as pulse duration, tensile strain-rate and peak stress coupled with material microstructure itself affect the material response in a complex manner. Yet, it is impossible to capture all this information by assessing only the spall strength measured from simple one-dimensional Photon Doppler Velocimetry measurements. Although, there exist widely used corrections proposed by Kanel et. al. that allow for the inclusion of some of these complexities into the measured spall strength but still does not take the microstructure into account. In this work, we propose another scheme for normalization of spall strength with a damage area to capture the complexities included in the damage and failure process especially pertaining to microstructure. We will also demonstrate the application of this scheme by applying to examples of materials such as Copper, Copper-24 wt%Ag, Copper-15 wt% Nb and additively manufactured 316L SS