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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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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina

Current grain growth models have evolved to account for the relationship between grain boundary energy/mobility anisotropy and the five degrees of grain boundary character. However, the role of grain boundary networks on overall growth kinetics remains poorly understood. To experimentally investigate this problem, a highly textured Al 2 O 3 was fabricated by colloidal casting in a strong magnetic field to engineer a unique spatial distribution of grain boundary character. Microstructural evolution was quantified and compared to an untextured sample. From this comparison, a prevalence of (0001)/(0001) terminated grain boundaries with anisotropic networks were identified in the textured sample. These boundaries and their networks were found to be driving grain growth at a faster rate than predicted by models. These findings will allow better modelling of grain growth in real systems by experimentally exploring the impact thereon of grain boundary plane anisotropy and relative energy/mobility differences between neighboring boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Considering computational speed vs. accuracy: Choosing appropriate mesoscale RVE boundary conditions

Modeling a material’s microstructure using continuum theories allows for inspection of the relationship between coarse scale and fine scale behaviors. Computational limits generally require selection of a sub-volume from a bulk sample in order to directly model the microstructure. Boundary conditions are applied to the sub-volume to mimic the excluded bulk material. Appropriate selection of boundary conditions helps effectively determine the appropriate spatial scale required of the sub-volume. Applicable boundary conditions include direct displacement, periodic, and uniform traction. While direct displacement and periodic boundary conditions are commonly used, uniform traction boundary conditions have seen limited use due to rigid body stability issues in simulations of compression or shear deformation. A new application of uniform traction boundary conditions was developed through linear constraint equations, similar to approaches employed by direct displacement and periodic boundary conditions, to quench rigid body motions with minimal interference of the relative deformation of the model. These boundary conditions were tested by compressing several synthetically generated periodic microstructures using the finite element method. Evaluating the effective stiffness along the compression axis, the direct displacement boundary condition produced the stiffest response, whereas the uniform traction boundary condition produced the most compliant. Periodic boundary conditions produced the same response for all volumes analyzed and both the direct displacement and uniform traction boundary conditions trended toward the periodic response as the domain volume increased. Computational performance was also evaluated for each boundary condition using implicit and explicit solvers. Direct displacement boundary conditions presented the lowest computational cost of all of the boundary conditions followed by periodic then uniform traction. The computational expense of periodic and uniform traction boundary conditions limited the viable spatial scale and mesh resolutions able to be simulated. Selection of appropriate boundary conditions for specific uses need to be a balance between allowable computational expense and accuracy of the method. Techniques for evaluating which boundary conditions to use are discussed.

42 ENGINEERING↗
DOE OSTI2025

A fully-integrated lattice Boltzmann method for fluid–structure interaction

Here we present a fully-integrated lattice Boltzmann (LB) method for fluid–structure interaction (FSI) simulations that efficiently models deformable solids in complex suspensions and active systems. Our Eulerian method (LBRMT) couples finite-strain solids to the LB fluid on the same fixed computational grid with the reference map technique (RMT). An integral part of the LBRMT is a new LB boundary condition for moving deformable interfaces across different densities. With this fully Eulerian solid–fluid coupling, the LBRMT is well-suited for parallelization and simulating multi-body contact without remeshing or extra meshes. We validate its accuracy via a benchmark of a deformable solid in a lid-driven cavity, then showcase its versatility through examples of soft solids rotating and settling. The LBRMT achieves a spatial convergence rate between first-order and second-order for FSI simulations and is designed for low to intermediate Reynolds number flows with finite inertia at small Mach numbers. With simulations of complex suspensions mixing, we highlight the potential of the LBRMT for studying collective behavior in soft matter and biofluid dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Relative Permeability Scaling From Pore-Scale Flow Regimes

Relative permeability measurements are commonly fitted with the Corey and Brooks-Corey correlations. Despite such correlations fit experimental data generally well, they are mostly of an empirical nature. Here, we propose a semiempirical model to determine relative permeabilities of the wetting and the nonwetting phases in real 3-D porous media that accounts for pore-scale flow regimes. The starting point is the homogenization framework proposed by Picchi and Battiato (2018, https://doi.org/10.1029/2018WR023172), where the upscaling is conducted for different spatial distributions of the flowing phases in the capillary tube setting. First, we extend the approach to realistic media by allowing pore-scale flow regimes to coexist in a complex geometry while accounting for capillary and viscous limits in the dynamics. Then, we discuss the scaling behavior of normalized relative permeabilities in terms of the phases viscosity ratio and identify three classes which govern their scaling. We also derive an analytical expression for the fractional flow. Finally, we provide a detailed validation of the proposed model for both relative permeabilities and fractional flow against data from numerical simulations and experiments available in the literature. Lastly, the data set used for validation covers a wide range of systems, ranging from brine-CO 2 to oil-water flows. The equations derived capture well the trend of both numerical and experimental data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Estimation of the orientation of stress in the Earth’s crust without earthquake or borehole data

Abstract Mechanical stress acting in the Earth’s crust is a fundamental property that is important for a wide range of scientific and engineering applications. The orientation of maximum horizontal compressive stress can be estimated by inverting earthquake source mechanisms and measured directly from borehole-based measurements, but large regions of the continents have few or no observations. Here we present an approach to determine the orientation of maximum horizontal compressive stress by measuring stress-induced anisotropy of nonlinear susceptibility, which is the derivative of elastic modulus with respect to strain. Laboratory and Earth experiments show that nonlinear susceptibility is azimuthally dependent in an anisotropic stress field and is maximum in the orientation of maximum horizontal compressive stress. We observe this behavior in the Earth—in Oklahoma and New Mexico, U.S.A, where maximum nonlinear susceptibility coincides with the orientation of maximum horizontal compressive stress measured using traditional methods. Our measurements use empirical Green’s functions and solid-earth tides and can be applied at different temporal and spatial scales.

58 GEOSCIENCES↗
DOE OSTI2021

Universality class for loopless invasion percolation models and a percolation avalanche burst model for hydraulic fracturing

Invasion percolation is a model that was originally proposed to describe growing networks of fractures. In this work, we describe a loopless algorithm on random lattices, coupled with an avalanche-based model for bursts. The model reproduces the characteristic b-value seismicity and spatial distribution of bursts consistent with earthquakes resulting from hydraulic fracturing (“fracking”). We test models for both site invasion percolation and bond invasion percolation. These have differences on the scale of site and bond lengths l. But since the networks are characterized by their large-scale behavior, l << L, we find small differences between scaling exponents. Though data may not differentiate between models, our results suggest that both models belong to different universality classes.

58 GEOSCIENCES↗
DOE OSTI2025

Validating Greater Sage-Grouse Individual-based Model (IBM) Tool (Final Report)

The project focused on validating the previously developed Greater Sage-Grouse Individual-based Model (GrSG IBM; LaGory et al. 2012, 2021). The objective was to transform this predictive, spatially and temporally explicit model into a portable resource to assist siting/resource managers in proactively assessing the cumulative impacts of wind energy development on the greater sage-grouse. Utilizing a bottom-up, individual-based approach, the GrSG IBM accounts for landscape context and species behavior, aiming to reduce uncertainty in estimating development impacts and support ecologically mindful land-based wind energy development. The validation effort covered approximately 6,540 km 2 near the Seven Mile Hill Wind Project in Wyoming. The GrSG IBM tool, built on the NetLogo platform (Tisue and Wilensky 2004), was executed over a 50-year period, with the analysis focusing on years following a 10-year initialization phase. Key results demonstrated the tool’s biological soundness across five key biological metrics: non-chick age class distribution (older than 10 weeks), adult sex ratio, life expectancy, population size, and overall population growth. For instance, the tool estimated that 58.6% of the non-chick population was reproductively immature, while the reference ranges from 51.4% to 57.8% (Patterson 1952, Rogers 1964). Experts confirmed the tool’s estimate was within a reasonable range for the species. The tool estimated average life expectancy of 1.43 years, while the reference ranges from 0.9 years to 1.1 years (Ammann 1957, Hamerstrom 1949). Experts also supported the model’s life-expectancy estimate as ecologically sound for the species in the study area. In terms of population change, the model estimated an annual shift between a 0.6% decline and a 1.0% increase over 50 years. While the reference suggests 2.9% annual decline in range-wide populations (Cortes et al. 2023), that includes many at-risk populations in South Dakota and Washington, for example. Our study area—in the northeastern part of Carbon County and western-edge of Albany County, Wyoming—is one of the remaining greater sage-grouse habitats supporting some of the most stable populations. Experts confirmed that the range of the annual population change spanning from a 0.6% decline to a 1.0% increase estimated by the tool was reasonable for our study area for this reason and confirmed that aligned with population estimates from existing studies on the greater sage-grouse and wind energy development in the study area (LeBeau et al. 2017a, Smith et al. 2024). Furthermore, the project showed that temporally explicit biological metrics generated by the GrSG IBM tool can complement the USGS’ Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET; Duchardt et al. 2021) by incorporating habitat restoration strategies into seasonal habitat suitability models to visualize population responses over time.

17 WIND ENERGY↗
DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗
NASA NTRS1982

Coupled flap-lag-torsional dynamics of hingeless rotor blades in forward flight

The aeroelastic stability and response problem of the coupled flap-lag-torsional dynamics of an isolated hingeless rotor blade in forward flight is considered. Linear, quasi-steady aerodynamics below stall is included. The spatial dependence of the partial differential nonliner equations of motion is discretized using a multimodal Galerkin method. The nonlinear time dependent equilibrium position (steady state response) about which the equations are linearized is obtained by solving a sequence of linear periodic response problems (quasilinearization). Results illustrating blade behavior in forward flight for both soft-in-plane and stiff-in-plane designs are presented. Quasilinearization provides a clear indication of the cases when nonlinear terms due to moderate deflections are important. The results indicate that nonlinearities affect system stability much more than system response. This implies that for obtaining blade vibrations and loads, approximate analytical models based on linearized formulations could be employed, whereas for determining blade stability a more accurate nonlinear analysis would be required.

Friedmann, P. P.↗
NASA NTRS1992

Creative-Dynamics Approach To Neural Intelligence

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Zak, Michail A.↗
DOE OSTI2024

Considering interplay between multiple physical phenomena to elucidate single crystal-like texture, phase transformations, and mechanical behavior of directed energy deposited SS316L

A widespread implementation of large scale additive manufacturing (AM) processes, such as wire arc-directed energy deposition (WA-DED) AM can transform the current manufacturing supply chain networks. Naturally, such implementation requires control of the microstructural attributes, such as texture and phase evolution in the processed alloys. Currently, the texture evolution in fusion-based AM (F-BAM) processes is majorly rationalized by the phenomena occurring only during solidification. However, such rationalization is insufficient for understanding the evolution of primary and secondary crystallographic orientations, and consequently, fails to offer a comprehensive understanding and control of overall texture in F-BAM processed alloys. To this end, we report a single crystal (SX)-like texture in WA-DED processed SS316L for the first time. Furthermore, we assess the physical phenomena that may lead to such unique microstructural evolution during WA-DED AM. Subsequently, using microstructural characterization spanning the build height and thermomechanical simulations we investigate the effect of competitive growth and epitaxial growth occurring during solidification and thermally induced plastic deformation occurring post solidification on the overall texture of WA-DED processed SS316L. A spatial variation in solidification pathway is also established and correlated with variation in undercoolings across the build. Tensile tests revealed a strong orientation dependence of deformation mechanisms with over 110% elongation to failure of specimens deformed along <011>. Such anisotropy is rationalized using Schmid's analysis of dislocation slip and deformation twinning. Importantly, overall, the mechanisms outlined in this work will facilitate an enhanced understanding and subsequent control of texture evolution, solidification behavior and mechanical behavior of WA-DED processed steels.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Crystallographic orientation and spatially resolved damage in a dispersion-hardened Al alloy

The in-situ neutron diffraction technique, in combination with both the full-field crystal elasto-viscoplastic finite element model and microstructural characterization, was used to study the deformation-induced damage anisotropy in a commercial Al alloy, subjected to uniaxial tensile and cyclic loading. The simulations capture well the crystallographic-orientation-dependent lattice strain behavior. The hard grains, e.g. those orientated with the <111> and <422> orientations parallel with the uniaxial loading direction (LD), feature large Taylor factors and seem more prone to form damage-related band structures. Their effective elastic moduli decrease with the accumulation of damage, which are different from the soft grains orientated with the <200> orientation along the LD. Correlation between the distribution of voids and that of the residual lattice strain developed after failure may exist. The maximum tensile type residual lattice strain observed after failure may be resulted from the band structure formed in the hardest <111> grains. It was revealed that the band structure triggered by the hard particles could be one of sources of damage. In addition, while the specimen was obviously damaged, a fast stress relief was evidenced after unloading from the tension, especially at the beginning of unloading. Our present investigations provide a novel method for exploring the damage mechanisms of polycrystalline materials during plastic deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Spatiotemporal Thermal Coupling in VO 2 Device Arrays

Correlated oxides such as VO 2 exhibit an electrically driven insulator–metal transition (IMT) that underlies their promise for neuromorphic and memory devices. Yet the IMT is not a uniform bulk process but a spatiotemporal phenomenon in which local heating nucleates filaments, contracts or dissolves them with the electric field, and couples to the environment. In this work, we directly image the VO 2 IMT dynamics by mid-wave infrared, thermography synchronized with electrical transport, resolving device temperature with micrometer spatial and microsecond temporal resolution. At the single-device level, we capture the full cycle of filament nucleation, contraction, and relaxation during current/voltage-driven resistive switching. At the array level, we show that heat propagates across etched gaps with an effective length scale of ∼131 µm, enabling cooperative behaviors among electrically isolated devices. Short-range distanced devices exhibit mutual filament attraction and sequential dissolution, while long-range distanced devices differentiate into distinct roles: drivers that initiate switching, cooperative responders that undergo assisted self-oscillations, and passive reporters that record the thermal field. Furthermore, these results reframe thermal crosstalk, long regarded as parasitic, as an intrinsic coupling channel and design principle for organizing collective switching behaviors, with direct implications for emergent circuit functionality in neuromorphic and unconventional computing architectures.

coupling↗
DOE OSTI2026

Movement Models to Predict Low‐Altitude Flight of Soaring Birds Using Look‐Ahead Environmental Factors

Advances in fine-scale movement modeling of soaring birds can aid efforts to understand and resolve the impacts of anthropogenic activities on such birds. Soaring birds often rely on underlying terrain and low-altitude updrafts to govern their flights at rotor-swept altitudes (≤ 200 m above ground level), which puts them at risk of collision with wind turbines. We developed a data-driven Markov model at 1-s resolution that predicts the fine-scale flight behavior of golden eagles (Aquila chrysaetos) as a function of ecological covariates at the current location as well as those within an eagle's line of sight. We only considered ecological covariates that are readily available in real-time (ground elevation and wind conditions). Latent factors (age, sex, species, behavioral intent, migratory status) were intentionally left out of the model. We calibrated the model using golden eagle telemetry data collected in two different ecoregions of the United States. Given a starting location, the calibrated model simulates multiple stochastic 3D paths to produce a time-explicit and spatially explicit risk map of turbine collisions. We discovered an empirical relation between the rate of change of heading and the orographic updraft conditions within an eagle's line of sight. Our model performed most effectively when predicting predominantly-soaring flights at rotor-swept altitudes during wind conditions in which turbines are likely to be operational. The calibrated model could be used in concert with automated eagle detection and turbine curtailment technologies. Specifically, once an eagle is detected by those systems, our model could then provide accurate predictions of turbines the eagle is likely to interact with in the near term.

17 WIND ENERGY↗
DOE OSTI2019

Stabilizing Effect of High Pore Fluid Pressure on Slip Behaviors of Gouge-bearing Faults

We conducted experiments to investigate the influence of pore fluid pressure on the frictional strength and slip behavior of gouge bearing faults. Saw cut porous sandstone samples with a layer of gouge powders placed between the precut surfaces were deformed in the conventional triaxial loading configuration. A series of velocity-step tests were performed to measure the response of the friction coefficient to variations in sliding velocity. Pore volume changes were monitored during shearing of the gouge. Our results demonstrate that under constant effective pressure, increasing pore pressure stabilizes the frictional slip of faults with all four gouge materials including antigorite, olivine, quartz, and chrysotile. The stabilizing effect is the strongest in antigorite gouge, which shows an evolution of friction parameters from velocity-weakening toward velocity-strengthening behavior with increasing pore pressure. Experiments with controlled pore volume show that the pore volume reduction diminishes under high pore fluid pressures, implying an increasing dilation component at these conditions. The dilatant hardening mechanism can explain the observed strengthening. These results provide a possible explanation to the observed spatial correlation between slow slip events and high pore pressure in many subduction zones.

58 GEOSCIENCES↗
DOE OSTI2023

Orbital dependent complications for close vs well-separated electrons in diradicals

We investigate two limits in open-shell diradical systems: O3, in which the interesting orbitals are in close proximity to one another, and (C21H13)2, where there is a significant spatial separation between the two orbitals. In accord with earlier calculations, we find that standard density-functional approximations do not predict the open-shell character for the former case but uniformly predict the open-shell character for the latter case. We trace the qualitatively incorrect behavior in O3 predicted by these standard density functional approximations to self-interaction error and use the Fermi–Löwdin-orbital-self-interaction-corrected formalism to determine accurate triplet, closed-shell singlet, and open-shell broken-spin-symmetry electronic configurations. Analysis of the resulting many-electron overlap matrices allows us to unambiguously show that the broken-spin-symmetry configurations do not participate in the representation of the Ms = 0 triplet states and allows us to reliably extract the singlet–triplet splitting in O3 by analyzing the energy as a function of Fermi-orbital-descriptor permutations. The results of these analyses predict the percentage of open-shell character in O3, which agrees well with conventional wavefunction-based methods. While these techniques are expected to be required in cases near the Coulson–Fischer point, we find that they will be less necessary in diradical systems with well-separated electrons, such as (C21H13)2. Results based on energies from self-interaction-corrected generalized gradient, local density, and Hartree–Fock approximations and experimental results are in generally good agreement for O3. These results help form the basis for deriving extended Heisenberg-like Hamiltonians that are needed for descriptions of molecular magnets when there are competing low-energy electronic configurations.

Chemistry↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗