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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Elevating SolTrace's Capabilities for the Next Generation of Concentrating Solar Analysis

SolTrace is an open-source Monte Carlo ray tracing software developed at NREL. SolTrace can characterize concentrating solar thermal (CST) collector optical performance and is CST technology agnostic. Shown in Fig. 1, SolTrace is a foundational tool in NREL's CST system and component modeling suite. SolTrace's generic surface elements can flexibly model novel collector and receiver designs to predict spatial and temporal flux distributions - critical to understand for CST component design, performance prediction, and system integration. Since its initial development, SolTrace has over 1,650 references on Google Scholar, over 9,800 downloads since 2017, and has served the CST research and development community as a benchmark of 3rd party verification. SolTrace provides users with many options for defining surface shape and boundaries. However, SolTrace provides limited documentation which can result in a steep learning curve for new users. Additionally, SolTrace lacks the computational performance required to evaluate optical performance of a CST system over the course of a year and/or iteratively over design parameters in a timely manner. To address this, we are working towards a new release of SolTrace that enables increased computational throughput by implementing ray tracing acceleration structures and enabling GPU parallelization. Additionally, we are working to improve SolTrace's usability, accessibility, and maintainability by (1) automating solar position time-dependent simulation processes, (2) creating general CST collector templates of grouped elements, (3) updating the user interface to better visualize model inputs and outputs, and (4) creating a user support network through forums, "how to" videos, and documentation.

14 SOLAR ENERGY↗
NASA NTRS1981

A comparison of the height distributions of solar flare hard X-rays in thick target and thermal models

Using recent observations of solar hard X-ray bursts having a spatial resolution of a few arcsec, a computation is made of the theoretically predicted spatial variation of hard X-ray flux versus height in both thick target nonthermal and thermal models of solar hard X-ray bursts. The work on the thick target model revises previous results in this area by adopting a more realistic model for the flaring atmospheric structure and also by taking into account energy loss and scattering processes in the evolution of the nonthermal electron beam which have previously been neglected. It is noted that in the so-called 'thermal' model currently in vogue there is a substantial nonthermal bremsstrahlung component at all photon energies; it is further demonstrated that this nonthermal component results in a hard X-ray flux versus height distribution whose maximum brightness per unit length is, similarly to the thick target model, located in the chromosphere and not in the thermal source itself. The characteristics of the hard X-ray height distributions in both thick target and thermal models are summarized and compared.

Emslie, A. G.↗
NASA NTRS1982

Aerodynamic interactions with turbulent jet exhaust plumes

The importance of aerodynamic interactions associated with external flow-field effects on turbulent jet exhaust plume structure is discussed. A viscous/inviscid prediction technique is presented which combines the overlaid mixing and inviscid plume components of the JANNAF Standardized Plume Flow-Field (SPF) model with inviscid external flow and boundary-layer analyses for treating nozzle afterbodies at subsonic/transonic speeds. Validation of the technique via comparisons between predictions and experiment for cold-air jet plumes is presented. Predicted spatial temperature distributions for hot, nonafterburning plumes are presented and compared to results obtained from more simplified prediction techniques in order to assess the importance of the aerodynamic interactions associated with external boundary layers and pressure gradients. It is demonstrated that these interactions play a significant role in determining the near-field turbulent mixing and inviscid plume shock structure. The implication of these results to plume radiation predictions is discussed.

Wilmoth, R. G.↗
DOE OSTI2022

Rising CO 2 and warming reduce global canopy demand for nitrogen

Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO 2 and climate change. By extension, it has been suggested that declining carboxylation capacity (V cmax ) and leaf N content in enhanced-CO 2 experiments and satellite records signify increasing N limitation of primary production. We predicted Vcmax using the coordination hypothesis and estimated changes in leaf-level photosynthetic N for 1982–2016 assuming proportionality with leaf-level V cmax at 25°C. The whole-canopy photosynthetic N was derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for V cmax , and converted to annual N demand using estimated leaf turnover times. The predicted spatial pattern of V cmax shares key features with an independent reconstruction from remotely sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.27% yr -1 , while observed leaf (total) N declined by 0.2–0.25% yr -1 . Predicted global canopy N (and N demand) declined from 1996 onwards, despite increasing LAI. Leaf-level responses to rising CO 2 , and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

A study of the diffuse galactic gamma radiation

Assuming cosmic rays pervade the Galaxy, they necessarily produced high energy gamma-rays as they interact with the instellar matter and photons. The cosmic ray nucleon interactions five rise to gamma rays primarily through the decay of pi mesons, giving a unique spectrum with a maximum at approximately 68 MeV. Cosmic ray electrons produce gamma rays through bremsstrahlung, but with a markedly different energy spectral shape, one which decreases monotonically with energy. Cosmic ray electrons also interact with the interstellar starlight, optical and infrared photons, and the blackbody radiation through the Compton process. A model of galactic gamma ray production is discussed, and the predicted spatial distribution and energy spectra are presented. Considering the uncertainty in the point source contributions, the agreement between the theoretical predictions and the gamma ray data seems quite reasonable.

Fichtel, C. E.↗
DOE OSTI2025

Contact patterns in wild pigs ( Sus scrofa ) from GPS tracking reveal spatial and temporal dynamics of social behaviour

Wild pig ( Sus scrofa Linnaeus, 1758) social behaviour affects disease transmission and landscape-level population management. Recent research has incorporated analysis of social structure to better understand the risk of disease transmission in wild pigs, although the relationship between overall social structure of wild pigs remains unclear. Here, we seek to improve understanding of the spatial and temporal dynamics of wild pig social structure and contact rates to better inform management strategies. Using GPS tracking data, we measured home range overlap, and estimated contact rates of wild pigs at four study sites in the southern USA to identify pairwise social associations (i.e., contacts) based on synchronous movement. Contact rate was strongly associated with home range overlap, but exhibited substantial variation, especially at moderate levels of home range overlap. We found that female–female dyads had higher contact rates and longer duration phases of social association compared to female–male and male–male dyads. We found male–male dyads tended to experience social associations farther from their home range centers than female–female or female–male dyads. Social associations between wild pig dyads are highly dynamic in their spatial and temporal structure. Further, dyads with strong social associations still experience substantial time apart. Our findings highlight the challenges of predicting spatial and social associations in wild pig social pairs due to their dynamic social structure over space and time.

McIlraith, Jack R.↗
DOE OSTI2021

Estimating basis functions in massive fields under the spatial mixed effects model

Abstract Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Pazdernik, Karl↗
DOE OSTI2021

Reduced ecosystem resilience quantifies fine-scale heterogeneity in tropical forest mortality responses to drought

Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, in this study, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one- out cross-validation R 2 = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to “fingerprint” forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Spatial variations in ac susceptibility and microstructure for the YBa2Cu3O(7-x) superconductor and their correlation with room-temperature ultrasonic measurements

The spatial (within-sample) uniformity of superconducting behavior and microstructure in YBa2Cu3O(7-x) specimens over the pore fraction range of 0.10 to 0.25 was examined. The viability of using a room-temperature, nondestructive characterization method (ultrasonic velocity imaging) to predict spatial variability was determined. Spatial variations in superconductor properties were observed for specimens containing 0.10 pore fraction. An ultrasonic velocity image constructed from measurements at 1 mm increments across one such specimen revealed microstructural variation between edge and center locations that correlated with variations in alternating-current shielding and loss behavior. Optical quantitative image analysis on sample cross-sections revealed pore fraction to be the varying microstructural feature.

Roth, Don J.↗
NASA NTRS1991

Spatial variations in a.c. susceptibility and microstructure for the YBa2Cu3O(7-x) superconductor and their correlation with room-temperature ultrasonic measurements

The spatial (within-sample) uniformity of superconducting behavior and microstructure in YBa2Cu30(7-x) specimens over the pore fraction range of 0.10 to 0.25 was examined. The viability of using a room-temperature, nondestructive characterization method (ultrasonic velocity imaging) to predict spatial variability was determined. Spatial variations in superconductor properties were observed for specimens containing 0.10 pore fraction. An ultrasonic velocity image constructed from measurements at 1 mm increments across one such specimen revealed microstructural variation between edge and center locations that correlated with variations in alternating-current shielding and loss behavior. Optical quantitative image analysis on sample cross-sections revealed pore fraction to be the varying microstructural feature.

Roth, Don J.↗
NASA NTRS2019

Spatiotemporal Monitoring of Hydrilla [Hydrilla verticillata (L. f.) Royle] to Aid Management Actions

Hydrilla is an invasive aquatic plant that has rapidly spread through many inland water bodies across the globe by outcompeting native aquatic plants. Drinking water supply, power generation, and recreational use of lakes are threatened by the spread of this invasive species. Moreover, hydrilla serves as a host for an epiphytic, toxic cyanobacteria known to be the causative agent of the neurodegenerative disease called avian vacuolar myelinopathy, which affects waterfowl, raptors, and amphibians. The early detection of hydrilla infestation is very important to reduce the costs associated with control and removal efforts of this invasive species. Therefore, this study was aimed at developing a rapid, frequent, and large-scale tool for monitoring and predicting spatial extent of hydrilla habitat. This was achieved by integrating in situ and Landsat 8 OLI satellite data for Lake J. Strom Thurmond, the largest US Army Corps of Engineers Lake east of the Mississippi River located on the Georgia/South Caroline border. The predictive model incorporated remote sensing reflectance, Secchi disk depth (SDD), light attenuation coefficient (Kd), maximum depth of colonization (Zc), and percentage of light available through water column (PLW). The model predicted ideal habitat for hydrilla featuring high SDD, low values of Kd, high Zc and PLW. Final analysis showed that hydrilla starts growing in April, reaches peak around October, begins retreating in the following months, and disappears in February. Additionally, physical meteorological factors were analyzed and were found to be closely associated with hydrilla extent. Management agencies can use these results to not only to plan future removal efforts, but also evaluate and adapt their current mitigation efforts.

Percentage of light available through the water co↗
DOE OSTI2024

BISON Simulated and Experimental Fission Product Release Comparisons from Reradiated AGR-3/4 Compacts During High Temperature Heating Tests

The fuel performance modeling code BISON was used to predict the release of fission products iodine-131 (131I), xenon-133 (133Xe), and krypton-85 (85Kr) from four re-irradiated AGR-3/4 fuel compacts containing tristructural isotropic (TRISO) coated particles during high-temperature isothermal heating tests. The AGR-3/4 fuel compacts were irradiated in the Advanced Test Reactor (ATR) as part of the third and fourth series of planned experiments to support the Advanced Gas Reactor (AGR) Program. They were subsequently stored and re-irradiated in the Neutron Radiography (NRAD) reactor for approximately five days and then stored for another five to eight days before being subjected to isothermal heating tests in the Fuel Accident Condition Simulation (FACS furnace) for 200 to 300 hours at temperatures between 1000°C and 1600°C to evaluate fission product release at elevated temperatures. New nuclide-specific fission product source term models for the three nuclides of interest were developed using the reactor multiphysics code Griffin and implemented into BISON to support this work. The new source term models were incorporated into coupled compact- and particle-scale BISON simulations, which predict spatially- and temporally-resolved radionuclide generation, radioactive decay, transport, and release throughout the entire irradiation history, including the initial ATR irradiation, NRAD re-irradiations, FACS heating tests, and intermediate periods spent in storage. The experimentally measured fission product release from the heating tests were compared to modeling release predictions calculated by BISON to evaluate how well the code compares to experimental results. Overall, the experimental measured and BISON predicted comparative release results varied but generally agreed to within 5 particle equivalents. Comparative release results identified general observations to take into consideration to help refine future models and reduce uncertainties associated with both the measurement results and predictive results. This includes developing new uranium oxycarbide (UCO) specific kernel diffusivities for the three isotopes examined to more accurately reflect the material properties of the fuel form. Deriving new diffusivities will aid in producing a more informed BISON model

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes

Aerosol pH is a fundamental property of aerosols in terms of atmospheric chemistry and its impact on air quality, climate, and health. Precise estimation of aerosol pH in chemical transport models (CTMs) is critical for aerosol modeling and thus influences policy development that partially relies on results from model simulations. We report the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) simulated PM 2.5 pH over China during a period with heavy haze episodes in Beijing, and explore the sensitivity of the modeled aerosol pH to factors including emissions of nonvolatile cations (NVCs) and NH 3 , aerosol phase state assumption, and heterogeneous production of sulfate. We find that default WRF-Chem could predict spatial patterns of PM 2.5 pH over China similar to other CTMs, but with generally lower pH values, largely due to the underestimation of alkaline species (NVCs and NH 3 ) and the difference in thermodynamic treatments between different models. Increasing NH 3 emissions in the model would improve the modeled pH in comparison with offline thermodynamic model calculations of pH constrained by observations. In addition, we find that the aerosol phase state assumption and heterogeneous sulfate production are important in aerosol pH predictions for regions with low relative humidity (RH) and high anthropogenic SO 2 emissions, respectively. These factors should be better constrained in model simulations of aerosol pH in the future. Analysis of the modeled temporal trend of PM 2.5 pH in Beijing over a haze episode reveals a clear decrease in pH from 5.2 ± 0.9 in a clean period to 3.6 ± 0.5 in a heavily polluted period. The increased acidity under more polluted conditions is largely due to the formation and accumulation of secondary species including sulfuric acid and nitric acid, even though being modified by alkaline species (NVCs, NH 3 ). Our result suggests that NO 2 oxidation is unlikely to be important for heterogeneous sulfate production during the Beijing haze as the effective pH for NO 2 oxidation of S(IV) is at a higher pH of ~6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Abstract It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V

The elasto-plastic material behavior, material strength and failure modes of metals fabricated by additive manufacturing technologies are significantly determined by the underlying process-specific microstructure evolution. In this work a novel physics-based and data-supported phenomenological microstructure model for Ti-6Al-4V is proposed that is suitable for the part-scale simulation of laser powder bed fusion processes. The model predicts spatially homogenized phase fractions of the most relevant microstructural species, namely the stable β-phase, the stable α s -phase as well as the metastable Martensite α m -phase, in a physically consistent manner. In particular, the modeled microstructure evolution, in form of diffusion-based and non-diffusional transformations, is a pure consequence of energy and mobility competitions among the different species, without the need for heuristic transformation criteria as often applied in existing models. The mathematically consistent formulation of the evolution equations in rate form renders the model suitable for the practically relevant scenario of temperature- or time-dependent diffusion coefficients, arbitrary temperature profiles, and multiple coexisting phases. Due to its physically motivated foundation, the proposed model requires only a minimal number of free parameters, which are determined in an inverse identification process considering a broad experimental data basis in form of time-temperature transformation diagrams. Subsequently, the predictive ability of the model is demonstrated by means of continuous cooling transformation diagrams, showing that experimentally observed characteristics such as critical cooling rates emerge naturally from the proposed microstructure model, instead of being enforced as heuristic transformation criteria. Eventually, the proposed model is exploited to predict the microstructure evolution for a realistic selective laser melting application scenario and for the cooling/quenching process of a Ti-6Al-4V cube of practically relevant size. Numerical results confirm experimental observations that Martensite is the dominating microstructure species in regimes of high cooling rates, e.g., due to highly localized heat sources or in near-surface domains, while a proper manipulation of the temperature field, e.g., by preheating the base-plate in selective laser melting, can suppress the formation of this metastable phase.

Inverse parameter identification↗
DOE OSTI2022

Micromechanical Fields Associated with Irregular Deformation Twins in Magnesium

Understanding and controlling the development of deformation twins is paramount for engineering strong and stable hexagonal close-packed (HCP) Mg alloys. Actual twins are often irregular in boundary morphology and twin crystallography, deviating from the classical picture commonly used in theory and simulation. In this work, the elastic strains and stresses around irregular twins are examined both experimentally and computationally to gain insight into how twins develop and the microstructural features that influence their development. A nanoprecession electron diffraction (N-PED) technique is used to measure the elastic strains within and around a $\left\{ {10\overline{1}2} \right\}$ tensile twin in AZ31B Mg alloy with nm scale resolution. A full-field elasto-viscoplastic fast Fourier transform (EVP-FFT) crystal plasticity model of the same sub-grain and irregular twin structure is employed to understand and interpret the measured elastic strain fields. The calculations predict spatially resolved elastic strain fields in good agreement with the measurement, as well as all the stress components and the dislocation density fields generated by the twin, which are not easily obtainable from the experiment. The model calculations find that neighboring twins, several twin thicknesses apart, have little influence on the twin-tip micromechanical fields. Furthermore, this work reveals that irregularity in the twin-tip shape has a negligible effect on the development of the elastic strains around and inside the twin. Importantly, the major contributor to these micromechanical fields is the alignment of the twinning shear direction with the twin boundary.

36 MATERIALS SCIENCE↗