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DOE OSTI2025

Mechanically and electrically tunable Rashba-Edelstein effect in ferroelectric semiconductors, CsGe⁢𝑋 3 (𝑋 = I, Br, Cl)

The ability of materials to convert charge current into spin current is fundamental to many spintronics applications. One means of realizing this conversion is via Rashba-Edelstein effect (REE). Using density functional theory simulations, we predict that REE can be induced in the recently discovered family of semiconducting ferroelectrics, CsGe⁢𝑋 3 (𝑋 = I, Br, Cl). The effect is quantified through Rashba-Edelstein coefficients, 𝜒 𝑥⁢𝑦 =−𝜒 𝑦⁢𝑥 , which are nonzero in valence and conduction bands. The largest values, obtained for CsGeI 3 , are 3.45 × 10 10 $\frac{ℏ}{Λ cm}$ and 0.97 × 10 10 $\frac{ℏ}{Λ cm}$ in the conduction and valence bands, respectively. The values are comparable to, and sometimes exceed, those of other inorganic materials, although the maximal values occur away from the band edges. The coefficients' sign couples to the direction of spontaneous polarization, offering opportunities for nonvolatile spin current manipulation via external electric field. Furthermore, these coefficients are highly tunable through strain engineering owing to strain-induced variations in spin textures and energy isosurfaces. Specifically, 𝜒 in the valence band of CsGeI 3 is enhanced to 3.61 × 10 10 $\frac{ℏ}{Λ cm}$ under 5% biaxial strain. Given this potential, we synthesized CsGeI 3 to validate our simulation structure and found excellent agreement between experiment and simulation, thereby allowing extrapolation of our findings to practically significant temperatures. Furthermore, our study identifies promising materials for nonvolatile, multifunctional spintronic applications.

Electronic structure↗

DOE OSTI2023

Toward accurate prediction of partial-penetration laser weld performance informed by three-dimensional characterization – Part II: μCT based finite element simulations

The mechanical behavior of partial-penetration laser welds exhibits significant variability in engineering quantities such as strength and apparent ductility. Understanding the root cause of this variability is important when using such welds in engineering designs. In Part II of this work, we develop finite element simulations with geometry derived from micro-computed tomography (μCT) scans of partial-penetration 304L stainless steel laser welds that were analyzed in Part I. We use these models to study the effects of the welds’ small-scale geometry, including porosity and weld depth variability, on the structural performance metrics of weld ductility and strength under quasi-static tensile loading. We show that this small-scale geometry is the primary cause of the observed variability for these mechanical response quantities. Additionally, we explore the sensitivity of model results to the conversion of the μCT data to discretized model geometry using different segmentation algorithms, and to the effect of small-scale geometry simplifications for pore shape and weld root texture. The modeling approach outlined and results of this work may be applicable to other material systems with small-scale geometric features and defects, such as additively manufactured materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Correlations between Texture Profile Analysis and Sensory Evaluation of Cured Largemouth Bass Meat (Micropterus salmoides)

Texture is an important factor in evaluating the quality of aquatic products. To evaluate the texture properties of cured large mouth bass, edible sodium chloride (0, 1.0, 2.0, and 3.0%) was smeared to the bass meat. Texture profile analysis (TPA) and sensory evaluation were performed to evaluate the quality of the cured samples, and the correlations of the indexes in the two methods were analyzed. Two principal components were obtained from the TPA indexes and sensory evaluation indexes, and the cumulative variance contribution rates were 73.87% and 72.99%, respectively. Results from the principal component analysis showed that the main indicators that affected the TPA were gumminess and springiness, while those that affected sensory evaluation were chewiness and adhesiveness. The TPA index and sensory evaluation could be effectively improved when the sodium chloride added to the bass meat was 1%. In the correlation analysis, sensory springiness was negatively correlated with TPA hardness ( P < 0.05 , r = −0.553) but positively correlated with TPA chewiness ( P < 0.05 , r = 0.596). After stepwise regression analysis, the prediction equation between the sensory springiness and TPA hardness was obtained as SSp=5.770−0.002Ha. These results provide a basis for predicting the quality of large mouth bass cured products.

Li, Meijin↗

DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens.We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Reaching the equilibrium state of the frustrated triangular Ising magnet Ca 3 Co 2 O 6

Ca 3 Co 2 O 6 is a frustrated magnet consisting of a triangular arrangement of chains of Ising spins. It shows regular magnetization steps versus magnetic field every 1.2 T that are metastable with very slow dynamics. This has puzzled the community for many years and given rise to numerous potential theories. Here we approach the problem by seeking the elusive magnetic equilibrium state at T=2 K. To this end, we explore two approaches: (1) bypassing the slow dynamics produced by changing fields by instead field-cooling directly to the target temperature and (2) quantum annealing in transverse magnetic fields. While we observe no measurable effect of the quantum annealing in fields up to 7 T, which is likely due to the large Ising anisotropy of Co spins in this material, we find that for the field cooling in longitudinal fields we achieve the predicted equilibrium 1/3 magnetization. We perform Monte Carlo simulations of the ground state phase diagram and we also simulate the quantum annealing process and find good agreement between experiment and theory. Thus we present an investigation of the elusive ground state properties of the canonical frustrated triangular system Ca 3 Co 2 O 6 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Chemically and electrically tunable spin polarization in ferroelectric Cd-based hybrid organic-inorganic perovskites

Density functional theory computations are used to predict the electronic structure of Cd-based hybrid organic-inorganic high-T C ferroelectric perovskites with TMCM-CdCl 3 being one representative. Here we report Rashba-Dresselhaus spin splitting in the valence band of these nonmagnetic compounds. Interestingly, we find in computations that the splitting is not necessarily sensitive to the polarization of the material but to the organic molecule itself which opens a way to its chemical tunability through the choice of the molecule. Further chemical tunability of spin splitting is shown to be possible through a substitution of Cl in the CdCl 3 chains as the valence band was found to originate from Cl-Cl weekly bonding orbitals. For example, the substitution of Cl with Br in TMCM-CdCl 3 resulted in a ten times increase of spin splitting. Furthermore, the spin polarization in these materials gives origin to persistent spin textures which are coupled to the polarization direction, and, therefore, can be controlled by the electric field. This is promising for spintronics applications.

36 MATERIALS SCIENCE↗

NASA NTRS1986

An experimental investigation of agglutinate melting mechanisms - Shocked mixtures of Apollo 11 and 16 soils

Mixtures of chemically contrasting lunar soils have been shocked at pressures ranging from 18.2-62.0 GPa. Other than the generation of impact melts, modal and textural changes caused by shock include destruction of pore space and fused soil clasts and conversion of plagioclase to maskelynite. The loss of the fused soil component in these runs indicates that low agglutinate contents in shocked and/or compacted regolith breccias cannot be considered by themselves to be evidence of formation from immature regolith. From the petrographic and chemical data it appears that the impact glass formed mainly from the fine fraction and the fused soil component in the target, with relatively minor contributions from the other coarse clasts. The impact glasses exhibit the same chemical enrichments and depletions as their corresponding fine fractions and plot on or near a mixing line between the bulk and fine fraction of the soil in which they were formed. From this as well as several other studies it appears that the fusion of the finest fraction model is valid and that it accurately predicts the chemical systematics of impact glass formed from lunar soil. In addition, fusion of agglutinates present in the target soil is an important process.

Simon, S. B.↗

NASA NTRS2009

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters- Lidard et al.,2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected ase co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations. In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins,". As described in Kumar et al., 2007, and demonstrated in Case et al., 2008, and Santanello et al., 2009, LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling the enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation as described in Peters-Lidard et al. (2008) and Santanello et al. (2007), who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs. LIS has also recently been demonstrated for multi-model data assimilation (Kumar et al., 2008) using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature. Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation. Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeoroogical modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems.

Peters-Lidard, Christa D.↗

NASA NTRS2012

A Functional Comparison of Lunar Regoliths and Their Simulants

Lunar regolith simulants are essential to the development of technology for human exploration of the Moon. Any equipment that will interact with the surface environment must be tested with simulant to mitigate risk. To reduce the greatest amount of risk, the simulant must replicate the lunar surface as well as possible. To quantify the similarities and differences between simulants, the Figures of Merit were developed. The Figures of Merit software compares the simulants and regolith by particle size, particle shape, density, and bulk chemistry and mineralogy; these four properties dictate the majority of the remaining characteristics of a geologic material. There are limitations to both the current Figures of Merit approach and simulants in general. The effect of particle textures is lacking in the Figures of Merit software, and research into this topic has only recently begun with applications to simulants. In addition, not all of the properties for lunar regolith are defined sufficiently for simulant reproduction or comparison; for example, the size distribution of particles greater than 1 centimeter and the makeup of particles less than 10 micrometers is not well known. For simulants, contamination by terrestrial weathering products or undesired trace phases in feedstock material is a major issue. Vapor deposited rims have not yet been created for simulants. Fortunately, previous limitations such as the lack of agglutinates in simulants have been addressed and commercial companies are now making agglutinate material for simulants. Despite some limitations, the Figures of Merit sufficiently quantify the comparison between simulants and regolith for useful application in lunar surface technology. Over time, the compilation and analysis of simulant user data will add an advantageous predictive capability to the Figures of Merit, accurately relating Figures of Merit characteristics to simulant user parameters.

Rickman, D.↗

DOE OSTI2025

Spin Dynamics of Triple-𝐐 Magnetic Orderings in a Triangular Lattice: Implications for Multi-𝐐 Orderings in General Two-Dimensional Lattices

Multi-𝐐 magnetic structures on two-dimensional (2D) lattices provide a key route to realizing topological physics in 2D magnetism. A major experimental challenge is to unambiguously confirm their formation by excluding the possibility of topologically trivial multidomain single- or double-𝐐 magnetic orders, which cannot be distinguished using conventional diffraction techniques. Here, we propose that long-wavelength spin dynamics offers a universal diagnostic for triangular lattices: Triple-𝐐 orders that preserve rotational symmetry and single- or double-𝐐 orders that break it exhibit qualitatively distinct anisotropies in their Goldstone-mode velocities, stemming from fundamental differences in their underlying spin configurations. We validate this concept using the metallic triangular-lattice antiferromagnet Co 0.325 ⁢TaS 2 , which hosts both a stripe-type single-𝐐 state and a triple-𝐐 tetrahedral ordering at different temperatures. Using inelastic neutron-scattering and spin dynamics simulations, we first refine the spin Hamiltonian by fitting the paramagnetic excitation spectra, allowing us to develop an unbiased model independent of magnetic ordering. We then show that the observed velocity profiles of the Goldstone modes agree with the high-temperature model’s predictions: markedly anisotropic for the single-𝐐 phase and near isotropic for the triple-𝐐 phase. Importantly, this contrast persists across various exchange parameters, highlighting its model-independent nature and suggesting potential applicability to other 2D lattice systems. Beyond the long-wavelength regime, we present a substantial discrepancy between the measured and simulated magnon spectra exclusively in the triple-𝐐 phase. We attribute this discrepancy to magnon energy renormalization arising from order-of-magnitude-enhanced magnon-magnon interactions in the triple-𝐐 phase, due to its noncollinear configuration. This work provides universal insight into the dynamical properties of topological multi-𝐐 magnetic orderings in 2D lattice structures, offering a broadly applicable diagnostic to distinguishing them from topologically trivial single- or double-𝐐 counterparts. The unequivocal confirmation of the triple-𝐐 structure in Co 0.325 ⁢TaS 2 further establishes it as a prominent material platform for exploring topological spin textures in the genuine 2D limit.

Park, Pyeongjae [Oak Ridge National Laboratory (OR↗

DOE OSTI2021

High resolution measurements reveal abiotic and biotic mechanisms of elevated nitric oxide emission after wetting dry soil

Wetting dry soil triggers emission of nitric oxide (NO), which can harm air quality and influence ecosystem N balance. NO released after wetting may be derived from two primary mechanisms: (1) abiotic decomposition of nitrite (NO 2 -) present in the dry soil and (2) biologically-mediated production of NO. The relative contributions of these mechanisms to NO emission are challenging to partition. To differentiate the two mechanisms, we made continuous observations of NO emission from a range of soils that varied in texture, pH, and exchangeable cations. Additionally, we coupled NO gas exchange measurements with automated, non-destructive high-resolution sampling of inorganic N species via microdialysis. We identified two distinct phases of NO production (1) an initial phase that reached its maximum less than 30 min after rewetting; and (2) a second phase lasting up to 36 h. The magnitude of the initial phase of NO production was correlated with the initial nitrous acid concentration (calculated from NO 2 - concentrations and soil pH) and soil exchangeable calcium. The second phase of NO emission coincided with a peak in NO 2 - that preceded NO 3 - accumulation. Together, these results suggest that NO emissions following wetting are defined by an initial pulse of abiotic NO formation mediated by soil acidity at mineral surfaces, followed by a prolonged pulse of NO emission driven by a lag between the biological processes of ammonia oxidation and nitrite oxidation. These results can inform mechanistic models of NO emissions from soils to improve predictions of the terrestrial N cycle and atmospheric chemistry.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Potential Skyrmion Host Fe(IO3)3: Connecting Stereoactive Lone-Pair Electron Effects to the Dzyaloshinskii-Moriya Interaction

Magnetic skyrmions, which are topologically distinct magnetic spin textures, are gaining increased attention for their unique physical properties and potential applications in spintronic devices. Here we present a design strategy for skyrmion host candidates based on combinations of magnetic spin, asymmetric building units having stereoactive lone-pair electrons, and polar lattice symmetry. To demonstrate the viability of the proposed rational design principles, we successfully synthesized a Fe(IO 3 ) 3 polycrystalline sample and single crystals by using a new simplified low-temperature pathway, which is experimentally feasible for extending materials growth of transition metal iodates. Single crystal X-ray and powder synchrotron X-ray diffraction measurements demonstrated that Fe(IO 3 ) 3 crystallizes in the polar chiral hexagonal lattice with space group P63. The combined structural features of the macroscopic electric polarization along the c-axis stemming from the coalignment of the stereoactive lone-pairs of the IO 3 – trigonal pyramid and the magnetic Fe 3+ cation residing on the 3-fold rotation axis were selected to promote asymmetric exchange coupling. We find evidence of a predicted skyrmion phase at 14 K ≤ T ≤ 16 K and 2.5 T ≤ μ 0 H ≤ 3.2 T driven by a Dzyaloshinskii–Moriya (DM) interaction, a conclusion supported by the appreciable DM exchange and the zero-field spiral antiferromagnetic ground state of Fe(IO 3 ) 3 deduced from neutron diffraction experiments. The associated magnetic modulation wavelength of the putative skyrmions is expected to be short ~18 nm, comparable to the period of the DM-driven incommensurate order. This work links stereoactive lone-pair electron effects to enhanced DM interaction, demonstrating a new approach for chemical guidelines in the search for skyrmionic states of matter.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Impact of anisotropy on TRISO fuel performance

Manufacturing of tristructural isotropic (TRISO) particles involves the deposition of pyrolytic carbon (PyC) and silicon carbide (SiC) layers using the fluidized bed chemical vapor deposition (CVD) process. The CVD process is known to generate polycrystalline layers with crystallographic textures, which imparts anisotropic thermophysical properties to the layers. Past studies have shown the risk for particle failure increases with an increase in anisotropy. The limit beyond which the anisotropy of PyC layers becomes unacceptable due to failure risk has been identified as a high-priority knowledge gap. This work presents a first systematic study on the effects of anisotropic thermal and mechanical properties on TRISO fuel performance. This computational study, performed using the fuel performance code BISON, investigates how the anisotropy in elasticity and thermal properties affect the stresses, temperature, and failure of a TRISO particle. The influence of other factors, such as operating temperature and particle geometry on the anisotropy effects, also has been analyzed. The studies utilize the recently published anisotropic elasticity and thermal behavior models for TRISO PyC and SiC layers implemented using tensors with full anisotropic capability. The spherical TRISO particles with anisotropic properties were found to have greater maximum tensile stress and significantly higher failure probability than the spherical particles with isotropic properties. In conclusion, the fuel performance predicted using these recently developed models was found to be comparable with the performance obtained using the historical models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Control over Banded Morphologies and Circular Dichroism in Chiral Halide Perovskites

Chiral halide perovskites (c-HPs) merge the chirality of organic cations with the semiconducting properties of metal halide frameworks, creating a family of chiral semiconductors with tunable chiroptoelectronic behavior. Here, we describe the impact of periodic banded morphologies of textured c-HP ( R/S -NEA) 2 PbBr 4 films (NEA = 1- (1-naphthyl)ethylammonium) on their chiroptical behavior. Due to the interplay between the crystalline and glassy phases, the c-HP film growth is driven by rhythmic precipitation, producing a distinctive controllable radial banded pattern with the ( R/S -NEA) 2 PbBr 4 inorganic planes oriented parallel to the substrate. The banded morphology can be controlled, as evidenced by the growth temperature dictating the ridge-to-ridge spacing as well as the density of banded regions. The resulting circular dichroism (CD) spectral shape, intensity, and polarity vary in a seemingly random manner across processing conditions. However, these spectral features can be explained by considering key features of the banded morphology, such as refraction of the incident light due to surface morphology, birefringence, and stacked, rotated crystallites. These effects cannot be canceled by averaging front and back CD spectra of c-HP films, and our model incorporating these effects reproduces all observed CD spectra remarkably well. The control over the c-HP morphology and prediction capabilities of our CD modeling leads to further understanding of this class of semiconductors and the possibility of exploiting structural features for light polarization control akin to enhanced metamaterials.

14 SOLAR ENERGY↗

DOE OSTI2022

Crystal plasticity modeling of strain-induced martensitic transformations to predict strain rate and temperature sensitive behavior of 304 L steels: Applications to tension, compression, torsion, and impact

This paper advances crystallographically-based Olson-Cohen (direct γ → α’) and deformation mechanism (indirect γ→ε→α’) phase transformation models for predicting strain-induced austenite to martensite transformation. Here, the advanced transformation models enable predictions of not only strain-path sensitive, but also of strain-rate and temperature sensitive deformation of polycrystalline stainless steels (SSs). The deformation of constituent grains in SSs is modeled as a combination of anisotropic elasticity, crystallographic slip, and phase transformation, while the hardening is based on the evolution of dislocation density and explicit shifts in phase fractions. Such grain-scale deformation is implemented within the meso-scale elasto-plastic self-consistent (EPSC) homogenization model, which is coupled with the implicit finite element (FE) method to provide a constitutive response at each FE integration point for solving boundary value problems at the macro-scale. Parameters pertaining to the hardening and transformation models within FEEPSC are calibrated and validated on a suite of data including flow curves and phase fractions for monotonic compression, tension, and torsion as a function of strain-rate and temperature for wrought and additively manufactured (AM) SS304L. To illustrate the potential and accuracy of the integrated multi-level FE-EPSC simulation framework, geometry, mechanical response, phase fractions, and texture evolution are simulated during gas-gun impact deformation of a cylinder and quasi-static tension of a notched specimen made of AM SS304L. Details of the simulation framework, comparison between experimental and simulation results, and insights from the results are presented and discussed.

304L steels↗

DOE OSTI2023

Atomistic simulations and machine learning of solute grain boundary segregation in Mg alloys at finite temperatures

Understanding solute segregation thermodynamics is the first step in investigating grain boundary (GB) properties, such as strong yttrium (Y) effects on grain growth and texture evolution in micro-scale polycrystalline magnesium (Mg) alloys. To estimate the average GB segregation behavior in low-solute-concentration Mg alloys (e.g., 2 at.% Y), a state-of-the-art spectral approach is applied based on a per-site segregation energy spectrum for Y solute atoms at zero K obtained from molecular statistics (MS) simulations of ~10 4 GB sites in Mg symmetric tilt GBs (STGBs). Although selected MS simulation results are consistent with verification by density functional theory (DFT) calculations, estimates of average segregation tendency based on the zero-K energy spectrum deviate from experimental observations. To resolve this problem, thermodynamic integration (TI) methods based on molecular dynamics (MD) simulations are used to determine the per-site segregation free energies of Y at representative GB sites, which show contributions beyond harmonic approximations can be important for certain GB sites at high temperatures. A surrogate model of per-site segregation free energy is constructed from a small set of TI data points using stacking cross-validation regressors and physics-informed descriptors. This model is applied to predict the Y segregation free energy spectra for thousands of GB sites in Mg STGBs with uncertainty quantification. Finally, the average segregation tendency predicted by the spectral approach based on the free energy spectra agrees well (within the uncertainty range) with experimental observations for micro-scale polycrystalline Mg alloys at typical thermomechanical processing temperatures (500 ~ 800 K), where thermodynamic equilibrium states are likely to be achieved due to fast diffusion.

Atomistic simulations↗