Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Twinning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An experimental and detailed kinetic modeling study of the pyrolysis and oxidation of allene and propyne over a wide range of conditions

Allene and propyne are important intermediates in the pyrolysis and oxidation of higher hydrocarbon fuels, and they are also a major source of propargyl radical formation, which can recombine into different C 6 H 6 isomers and finally produce soot. In a prior work (Panigrahy et al., “A comprehensive experimental and improved kinetic modeling study on the pyrolysis and oxidation of propyne”, Proc. Combust. Inst 38 (2021)), the pyrolysis, ignition , and laminar flame speed of propyne were investigated. To understand the kinetic features of initial fuel breakdown and oxidation of the two C 3 H 4 isomers, new measurements for allene pyrolysis and oxidation are conducted in the present paper at the same operating conditions as those studied previously for propyne. Ignition delay times of allene are measured using a high-pressure shock tube and a heated twin-opposed piston rapid compression machine in the temperature range 690– 1450 K at equivalence ratios of 0.5, 1.0 and 2.0 in ‘air’, and at pressures of 10 and 30 bar. Pyrolysis species measurements of allene and propyne are also performed using a gas chromatography integrated singlepulse shock tube in the temperature range 1000–1700 K at pressure of 2 and 5 bar. Furthermore, laminar flame speeds of allene are measured at elevated gas temperatures of 373 K at pressures of 1 and 2 bar for a wide range of equivalence ratios from 0.6 to 1.5. A newly updated kinetic mechanism developed for this study is the first model that can well reproduce all of the experimental results for both allene and propyne. It is observed that in the pyrolysis process, allene dissociates faster than propyne. Both isomers exhibit similar ignition delay times at high temperatures (>1000 K), while, at intermediate temperatures (770–1000 K) propyne is the faster to ignite, and at lower temperatures (< 770 K) allene becomes more reactive. Furthermore, laminar flame speeds for propyne are found to be slightly faster than those for allene under the conditions studied in this work

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Unraveling the role of EGR olefins at advanced combustion conditions in the presence of nitric oxide: Ethylene, propene and isobutene

We report the role of EGR (exhaust gas recirculation) olefinic constituents at advanced combustion conditions in the presence of nitric oxide is unraveled in this study through experimental and modeling efforts using a twin-piston rapid compression machine operating at a stochiometric fuel loading with 20% EGR by mass, pressures of 20 and 40 bar, and temperatures from 680 to 950 K. Five different levels of olefin addition, focusing on ethylene, propene and isobutene, with a fixed amount of NO at 70 ppm are doped into test mixtures of PACE-20, a multi-component gasoline surrogate, where olefin addition effects are characterized through changes in ignition times and heat release rates. Experiments indicate that all three EGR olefins inhibit autoignition reactivity and low-temperature heat release at Tc < 850 K, with isobutene exhibiting the greatest impact, while at Tc > 850 K, low ethylene and propene additions promote reactivity. A recently updated chemical kinetic model, with detailed gasoline/NO x interacting and olefin/NO x interacting chemistry incorporated, is adopted to simulate the experiments. Simulation results are somewhat inconsistent with the experiments, where the model captures the inhibiting effects of all olefins on first-stage ignition reactivity, while consistently predicting a promoting effect on main ignition reactivity. Sensitivity and rate of production analyses reveal that adding olefins greatly alters the role of the consuming pathways for the olefins and their primary derivatives at the initial stage of the oxidation process, particularly with the presence of NO, where the olefins and their derivatives interact with both NO x species such as NO 2 and other species such as OH and HO 2 . The olefin/NO x interactions are particularly pronounced with propene and isobutene addition, where these lead to increased ignition reactivity by facilitating NO production and are mostly responsible for the disagreement between the model and experiments. Further investigations of olefin interacting chemistry, particularly those with NOx species, are needed for chemistry models to accurately predict the complicated effects of EGR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Understanding the plasticity contributions during laser-shock loading and spall failure of Cu microstructures at the atomic scales

A hybrid atomic-scale and continuum modeling framework is used to study the microstructural evolution during the laser-induced shock deformation and failure (spallation) of copper microstructures. A continuum two-temperature model (TTM) is used to account for the interaction of Cu atoms with a laser in molecular dynamics (MD) simulations. The MD-TTM simulations study the effect of laser loading conditions (laser fluence) on the microstructure (defects) evolution during various stages of shock wave propagation, reflection, and interaction in single-crystal (sc) Cu systems. In addition, the role of the microstructure is investigated by comparing the defect evolution and spall response of sc-Cu and nanocrystalline (nc) Cu systems. The defect (stacking faults and twin faults) evolution behavior in the metal at various times is further characterized using virtual in situ selected area electron diffraction and x-ray diffraction during various stages of evolution of microstructure. Here, the simulations elucidate the uncertain relation between spall strength and strain-rate and the much stronger relation between the spall strength and the temperatures generated due to laser shock loading for the small Cu sample dimensions considered here.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Extended defects-enhanced oxygen diffusion in ThO 2

Oxygen self-diffusion is key to understanding stoichiometry and defect structures in oxide nuclear fuels. Experimentally, low activation-barrier oxygen migration was found in ThO 2 , a candidate nuclear fuel, possibly due to short-circuit diffusion mechanisms. Here, we perform extensive molecular dynamics simulations to show that various types of extended defects can enhance oxygen self-diffusion with a much-reduced activation barrier in ThO 2 . In this work, we consider extended defects including 1D (dislocation), 2D (grain boundary), and 3D (void) defects. Due to the distinct characteristics of each type of extended defect, the modulation of oxygen diffusion varies. Further, these results provide a quantitative description of oxygen transport, which is significantly enhanced within a close distance (nanometer scale) from the extended defects. Among all these considered defects, grain boundary, particularly the low-energy $Σ$3 twin boundary, exhibits the strongest effect on increasing oxygen transport.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers

This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. Here we introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a composite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗
DOE OSTI2026

Accelerating Laboratory-Based, All-Solid-State Battery Research and Development: Industrially Relevant Small-Batch Dry-Processing, Small and Low-Cost Test Fixtures, and Short-Tolerant Separators

This work presents improvements to mixing (for dry-processed electrodes), separator robustness, and cell testing jigs for all-solid-state batteries (ASSBs). These developments combine synergistically to enable rapid research and development of ASSB catholytes. A mechanical "kneader" is designed and built which emulates the mixing and fibrillation of poly-tetrafluoroethylene binder that occurs in industrially relevant twin-screw extruders. A modest improvement in the mixing and dispersion catholyte materials is observed using micro-resolution X-ray computed tomography (X-ray CT). Catholytes are paired with an In-metal anode and separated by a novel, dual-layer, polyaramid-fiber-supported, sulfide-solid electrolyte (Li6 PS5 Cl) separator. This separator achieves high short tolerance which enabled a 95% success rate across 20 attempted cells and to date over 100 successful cells have been built. ASSBs were tested in a 2032 coin-cell format. Simultaneous cycling of a large number of cells is further enabled by a compact and low cost (~ 38 USD per jig) pressure application jig of which 100 have been constructed to date. The utility of these advances is demonstrated through a brief study on the effect cathode-side conductive carbon and current collector type has on capacity and rate performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Self-consistent equilibrium and transport simulations for NSTX-U plasmas enhanced via machine learning surrogate models

The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.

Equilibrium and transport modeling↗
DOE OSTI2020

IUPAC-IUGS recommendation on the half-lives of 147 Sm and 146 Sm

In this paper, the IUPAC-IUGS joint Task Group “Isotopes in Geosciences” recommends a value of (106.25 ± 0.38) Ga for the half-life of 147 Sm, and a corresponding decay constant λ 147 = (6.524 ± 0.024) × 10 –12 a –1 , both with a coverage factor k = 2. For the extinct radionuclide 146 Sm two very different half-lives are used in the scientific community ( c . 68 and 103 Ma), to such a degree that no consensus value can be endorsed at present by the Task Group. Pending dedicated re-investigations it is recommended that papers using the 146 Sm decay to quantify the cosmo/geological evolution of (extra)terrestrial samples perform a twin set of calculations using both proposed half-lives.

146Sm↗
DOE OSTI2022

Hot-melt extrudability of amorphous solid dispersions of flubendazole-copovidone: An exploratory study of the effect of drug loading and the balance of adjuvants on extrudability and dissolution

The FDA-approved anthelmintic flubendazole has shown potential to be repositioned to treat cancer and dry macular degeneration; however, its poor water solubility limits its use. Amorphous solid dispersions may overcome this challenge, but the balance of excipients may impact the preparation method and drug release. Here, we evaluate the influence of adjuvants and drug loading on the development of an amorphous solid dispersion of flubendazole-copovidone by hot-melt extrusion. The drug, copovidone, and adjuvants (magnesium stearate and hydroxypropyl cellulose) mixtures were statistically designed, and the process was performed in a twin-screw extruder. The study showed that flubendazole and copovidone mixtures were highly extrudable, except when drug loading was high (>40%). Furthermore, magnesium stearate positively impacted the extrusion and was more effective than hydroxypropyl cellulose. The extruded materials were evaluated by modulated differential scanning calorimetry and X-ray powder diffraction, obtaining positive amorphization and physical stability results. Pair distribution function analysis indicated the presence of drug rich domains with medium-range order structure and no evidence of polymer-drug interaction. All extrudates presented faster dissolution (HCl, pH 1.2) than pure flubendazole, and both adjuvants had a notable influence on the dissolution rate. In conclusion, hot-melt extrusion may be a viable option to obtain stable flubendazole: copovidone amorphous dispersions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Three-dimensional modeling and simulations of single-crystal and bi-crystal titanium for high-strain-rate loading conditions

High purity single crystal titanium (Ti) under shock wave loading is modeled under both one-dimensional and three-dimensional cylindrical conditions. Cylinder sizes of 10 μm and 20 μm radius are both considered in order to assess influence of boundary conditions. A thermodynamically consistent single crystal model for application to shock conditions is presented. The model accounts for the coupled non-linear elastic, dislocation slip, deformation twinning, and structural phase transformation response of the titanium material. Plate impact experiment results using a copper flyer are used to compare against the simulations for crystals oriented in [0001] and [10$\overline1$1] crystallographic directions. The one-dimensional and threedimensional simulations of the two differently oriented single crystals indicate differences between the one-dimensional and three-dimensional representation, especially for the [10$\overline1$1] oriented single crystal. This orientation breaks the relative orientation symmetry between the crystal and cylinder which otherwise exists for the [0001] oriented single crystal. A significant amount of heterogeneity in the field response of the[10$\overline1$1] oriented simulation was demonstrated due to the highly coupled nature of the deformation. A bi-crystal model composed of both the [0001] and [10$\overline1$1] orientations with the boundary between the two along the axis of the cylinder is also considered for a cylinder model size of 10 μm. The results indicate a strong interaction between the two grains that affects the ω phase volume fraction achieved relative to the single crystal calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure characterization and elastic-plastic self-consistent simulation studies of anisotropic deformation of β -tin

Uniaxial compression tests of 99.9% pure polycrystalline $β$-tin (Sn) were conducted at various strain rates (10 -3 /s, 10 -1 /s and 1/s) and temperatures (294 K and 193 K) to understand the effects of these variables on the stress–strain response, microstructure evolution, and stress relaxation behavior. Multiple Sn specimens were subjected to complex compressive loading/unloading/reloading paths at different strain rates. Specimens initially compressed at higher strain rates showed strain rate-dependent texture evolution and more pronounced relaxation upon reloading, as compared to those pre-strained at lower rates at room temperature (RT). Compression tests conducted at low temperature (193 K) revealed increased strength, similar to the enhanced strength observed when the strain rate was increased at RT. Neutron diffraction was employed to characterize the initial and final bulk textures of the RT specimens. Electron backscatter diffraction was utilized to examine the crystallographic grain orientation and morphology, thereby identifying the signatures of dislocation-mediated deformation, grain refinement, recrystallization, and twinning behaviors. Elastic-plastic self-consistent simulations were performed to investigate the deformation modes responsible for the strain rate-dependent macroscopic stress–strain response and texture evolution. Effects of crystallographic orientation on stress relaxation behavior was also examined. In conclusion, the model predictions are in reasonable agreement with experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimization of the microstructure and mechanical properties of electron beam welded high-strength medium-entropy alloy (NiCoCr) 94 Al 3 Ti 3

For this work, the weldability and effects of subsequent heat treatment on the microstructure and mechanical properties of the medium-entropy alloy (MEA) (NiCoCr) 94 Al 3 Ti 3 have been explored using electron beam welding (EBW). It was found that EBW joints consist of a supersaturated f.c.c. matrix with coarse, asymmetric columnar grains (17.7–24 μm). The fraction of twins and Σ3 n CSL boundaries in weld joints was only 10% of that in the base metal, a value which increased to 30% after aging at 800 °C. The aging also led to improvements in the hardness, tensile strength, and work hardening rate, a result of the precipitation of a high volume fraction (0.29–0.33) of ultrafine (15–17.4 nm dia.) coherent spherical L1 2 -structured particles. The contributions of the L1 2 nanoparticles and Hall-Petch strengthening to the yield strength were estimated to be 537–573 MPa and 105–123 MPa, respectively. The MEA shows excellent weldability and, after aging, good strength (YS~1098 MPa, UTS~1368 MPa) and elongation to failure (~11%) by ductile fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Activation of dislocations in Mg with solute Y

Mg-Y cast alloy shows excellent ductility (elongation to failure > 15%) compared with pure Mg and commercial Mg cast alloys. By monitoring the microstructure evolution during an in situ tensile test of a Mg-2.5 wt%Y alloy, we identify the activation of prismatic slip, which is rare in Mg. Synchrotron X-ray micro-beam Laue diffraction (μ-Laue) and transmission electron microscopy revealed the morphology of prismatic slip bands and individual dislocations. Density functional theory and molecular dynamics calculations indicate that solute Y can significantly reduce the stacking fault energy (SFE) along direction on prismatic plane in Mg lattice and thus facilitate the nucleation of dislocations during deformation. The presence of free dislocations in the Mg lattice can also lead to nucleation of {10–12} twins even under unfavorable geometric conditions.

36 MATERIALS SCIENCE↗