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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Upcycling of aluminum Twitch scrap via Shear Assisted Processing and Extrusion (ShAPE)

Aluminum is publicly perceived as recyclable, but mixed alloys and impurities in aluminum scrap require dilution via energy-intensive primary aluminum or downcycling to low-quality castings. In this study, cast billets of shredded aluminum scrap (Twitch) were blended with pre-consumer AA 6061, extruded into tubes via Shear Assisted Processing and Extrusion (ShAPE), and aged to T1 and T6 tempers. Microscopy reveals that ShAPE refined and distributed the deleterious AlFeSi phases. Furthermore, the Twitch extrusions had tensile properties comparable to AA 6061 yet without homogenizing or adding primary aluminum. Energy savings were 85% compared to conventional extrusion of primary aluminum alloys.

ShAPE↗
DOE OSTI2024

Microstructural refinement of an Al-Ce-Mg alloy via Shear Assisted Processing and Extrusion

Al-Ce alloys have attracted recent interest because of their high thermal stability due to the low solubility of Ce in the Al matrix. The Al 11 Ce 3 eutectic phase gives excellent strain hardening behavior and moderate high-temperature strength in the as-cast state. However, its strengthening effect is limited by its coarse as-cast structure. Therefore, alternative manufacturing methods such as additive manufacturing or equal channel angular pressing have been applied to refine the Al 11 Ce 3 phase to good effect. However, these techniques are both expensive and time-consuming. Therefore, this study aims to use Shear Assisted Processing and Extrusion (ShAPE), an emerging solid phase processing technique that is more easily scalable than the previously mentioned methods. ShAPE can produce useful cross-sections of an Al-8Ce-4Mg alloy while refining the Al 11 Ce 3 phase to produce a higher strength material. It was found that a low temperature ShAPE process can improve the room temperature yield strength by ~60 % compared to a binary Al-4Mg alloy. Additionally, the high-temperature yield strength of the Al-Ce alloys increased by 20%, with a simultaneous 15% improvement in ductility compared to the binary Al-Mg alloy. Finally, these results highlight the potential for ShAPE as a processing technique for Al-Ce alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Particle size and shape effect of Crumbler® rotary shear-milled granular woody biomass on the performance of Acrison® screw feeder: A computational and experimental investigation

Physical experiments and discrete element model (DEM) simulations are conducted to evaluate particle characteristics and operation parameter effects on screw feeding performance for rotary shear-milled Douglas fir. Three performance metrics are used: mass flow rate, shaft driving torque, and specific energy consumption. The impact of particle size, particle size distribution (PSD), shaft rotational speed (rpm), and hopper dimensions on the performance are investigated. All employed performance metrics reveal the superior flowability of the 2-mm particles in contrast to the larger 6-mm counterpart. Remarkably, wider PSD results in poorer flowability than the two mono-sized particles, proving the flowability enhancement achieved by narrower PSD of the woody feedstock. More importantly, DEM simulations unveil PSD-induced degradation in flowability is attributed to mechanical interlocking and particle segregation effects. Furthermore, higher shaft rpm causes higher mass flow rate at the cost of higher specific energy consumption due to viscous dissipation and changes in flow pattern.

09 BIOMASS FUELS↗
DOE OSTI2025

Sedimentation and shear-induced dynamics of spheroids in fluids with spatial viscosity variations

A generalized reciprocal theorem is used to relate the force and torque induced on a particle in an inertia-less fluid with small variation in viscosity to integrals involving Stokes flow fields and the spatial dependence of viscosity. These resistivity expressions are analytically evaluated using spheroidal harmonics and then used to obtain the mobility of the spheroid during sedimentation, and in linear flows, of a fluid with linear viscosity stratification. The coupling between the rotational and translational motion induced by stratification rotates the spheroid’s centerline, creating a variety of rotational and translational dynamics dependent upon the particle’s aspect ratio, κ, and the component of the stratification unit vector in the gravity direction, d g . Spheroids with 0.55 ⪅ κ ⪅ 2.0 exhibit the largest variety of settling behaviors. Interestingly, this range covers most microplastics and typical microorganisms. One of the modes include a stable orientation dependent only on κ and d g , but independent of initial orientation, thus allowing for the potential control of settling angles and sedimentation rates. In a simple shear flow, cross-streamline migration occurs due to the stratification-induced force generated on the particle. Similarly, a particle no longer stays at the stagnation point of a uniaxial extensional flow. While fully analytical results are obtained for spheroids, numerical simulations provide a source of validation. These simulations also provide additional insights into the stratification-induced force- and torque-producing mechanisms through the stratification-induced stress, which is not accessed in the reciprocal theorem-based analytical calculations.

Geophysical and Geological Flows: Stratified flows↗
DOE OSTI2021

Evolution of an arched magnetized laboratory plasma in a sheared magnetic field

Arched magnetized structures are a common occurrence in space and laboratory plasmas. Results from a laboratory experiment on spatio-temporal evolution of an arched magnetized plasma ( $\beta \approx 10^{-3}$ , Lundquist number $\approx 10^{4}$ , plasma radius/ion gyroradius $\approx 20$ ) in a sheared magnetic configuration are presented. The experiment is designed to model conditions relevant to the formation and destabilization of similar structures in the solar atmosphere. The magnitude of a nearly horizontal overlying magnetic field was varied to study its effects on the writhe and twist of the arched plasma. In addition, the direction of the guiding magnetic field along the arch was varied to investigate its role in the formation of either forward- or reverse-S shaped plasma structures. The electrical current in the arched plasma was well below the current required to make it kink unstable. A significant increase in the writhe of the arched plasma was observed with larger magnitudes of overlying magnetic field. A forward-S shaped arched plasma was observed for a guiding magnetic field oriented nearly antiparallel to the initial arched plasma current, while the parallel orientation yielded the reverse-S shaped arched plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Radiative and continuum dampings of reversed shear Alfvén eigenmodes and perturbative analysis limitations for tokamaks

A careful theoretical analysis of the excitation of Alfvén eigenmodes (AEs), such as TAE (toroidicity-induced AE) and RSAE (reversed shear AE), by superalfvenic energetic particles is required for reliable predictions of energetic ion relaxation in present day fusion experiments. This includes the evaluation of different AE damping mechanisms including radiative and continuum dampings which are the focus of this study. A recent comprehensive benchmark of different eigenmode solvers including gyrokinetic, gyrofluid and hybrid magenetohydrodynamics (MHD) has shown that employed models may have deficiencies when addressing some of them (Taimourzadeh et al., Nucl. Fusion, vol. 59, 2019, 066006). Here, in this paper, we are studying the radiative and continuum dampings of RSAEs in details which were missing in hybrid NOVA/NOVA-C calculations to prepare a NOVA-C package with a substantial upgrade. Both dampings require the finite Larmor radius (FLR) corrections to AE mode structures to be accounted for. Accurately calculating different damping rates and understanding their parametric dependencies, we resolve the limitation coming out of the perturbative approach. In particular, here, the radiative damping is included perturbatively, whereas the continuum damping is computed non-perturbatively. Our comparison leads to the conclusion that the non-perturbative treatment of the unstable RSAE modes is needed to find the agreement with the gyrokinetic calculations. We expect that the RSAE mode structure modification plays a dominant role in determining the RSAE stability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The shear Alfvén continuum of quasisymmetric stellarators

The shear Alfvén wave (SAW) continuum plays a critical role in the stability of energetic particle-driven Alfvén eigenmodes. We develop a theoretical framework to analyze the SAW continuum in three-dimensional (3-D) quasisymmetric magnetic fields, focusing on its implications for stellarator design. By employing a near-axis model and degenerate perturbation theory, the continuum equation is solved, highlighting unique features in 3-D configurations, such as the interactions between spectral gaps. Numerical examples validate the theory, demonstrating the impact of flux-surface shaping and quasisymmetric field properties on continuum structure. The results provide insights into optimizing stellarator configurations to minimize resonance-driven losses of energetic particles. This work establishes a basis for incorporating Alfvénic stability considerations into the stellarator design process, demonstrated through optimization of a quasihelical configuration to avoid high-frequency spectral gaps.

fusion plasma↗
DOE OSTI2025

Mesoscale Modeling of Hydrogels Under Frictional Shear Stress

Hydrogels are three-dimensional networks of hydrophilic polymers often used as a simplified model of hydrated biological materials, from cartilaginous joints to the ocular tear film. However, the lubrication mechanisms of hydrogels remain poorly understood, partly due to their complex polymeric structure, which creates blurred interfaces during sliding that are challenging to study experimentally. In this study, we employ dissipative particle dynamics (DPD) to investigate the frictional behavior of a polymeric hydrogel network sliding against a solid wall in an explicit viscous solvent. This computational approach enables us to model hydrodynamic interactions and mesoscale polymer dynamics, capturing key aspects of hydrogel friction. Our simulations reveal that hydrogel friction is governed by the interplay between polymer relaxation and viscous shear, characterized by the Weissenberg number (Wi). At low Wi, friction coefficient remain nearly constant, dominated by polymer relaxation. However, at higher Wi, friction is dominated by viscous drag within a near-wall solvent layer, leading to a linear increase in friction coefficient with Wi. Furthermore, our results demonstrate an inverse relationship between the friction coefficient and the applied normal load, consistent with experimental observations. This work provides new insights into the fundamental tribological properties of hydrogels, shedding light on the micromechanics of hydrogel friction. Improving our understanding of hydrogel structure and dynamics under friction advances our knowledge of the mechanisms regulating biological lubrication in health and disease.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows

Deep neural networks (DNNs) are developed from a data set obtained from the dynamic Smagorinsky model to emulate the subgrid-scale (SGS) viscosity (ν sgs ) and diffusivity (κ sgs ) for turbulent stratified shear flows encountered in the oceans and the atmosphere. These DNNs predict ν sgs and κ sgs from velocities, strain rates, and density gradients such that the evolution of the kinetic energy budget and density variance budget terms is similar to the corresponding values obtained from the original dynamic Smagorinsky model. These DNNs also compute ν sgs and κ sgs ~2–4 times quicker than the dynamic Smagorinsky model resulting in a ~2–2.5 times acceleration of the entire simulation. This study demonstrates the feasibility of deep learning in emulating the subgrid-scale (SGS) phenomenon in geophysical flows accurately in a cost-effective manner. In a broader perspective, deep learning-based surrogate models can present a promising alternative to the traditional parameterizations of the subgrid-scale processes in climate models.

58 GEOSCIENCES↗
DOE OSTI2020

Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. In this study we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1 m and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33 MPa and flow rates up to 5 L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

58 GEOSCIENCES↗
DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗
DOE OSTI2021

Shear Alfvén Waves Driven by Magnetic Reconnection as an Energy Source for the Aurora Borealis

Previous studies have concluded that the Hall magnetic field structures generated during magnetic reconnection are carried away by kinetic Alfvén waves. Here, we apply a kinetic simulation with an ion/electron mass ratio closer to its natural value and find that open boundary conditions and much reduced damping rates permit the shear Alfvén waves (SAWs) to become the main carrier of wave energy. Further, for magnetotail reconnection, these SAWs provide efficient transport of wave energy, enhancing the energy input for the Aurora Borealis by orders of magnitude above previous estimates.

58 GEOSCIENCES↗
DOE OSTI2021

Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.

58 GEOSCIENCES↗
DOE OSTI2022

Monitoring Seasonal Shear Wave Velocity Changes in the Top 6 m at Garner Valley in Southern California With Borehole Data

Abstract Subsurface structures play important roles in seismic ground motion, crustal hydrology, stability of the built environment, and more. Constraining temporal changes of subsurface shear wave velocity ( V S ) can provide useful information to all these topics and the growing field of hydrological monitoring with seismic velocity. Using borehole records at Garner Valley, CA, we estimate seasonal subsurface V S variations from impulse response functions (IRFs) of earthquake data (2005–2018) along with IRFs and cross‐correlation of cross‐hole experiment data (2015–2018). The inferred V S variations are up to ∼25% in the top 6 m and ∼10% at 2–5 m in depth. The V S variations correlate strongly with the water table depth changes, suggesting that the changes are mostly due to fluctuations of pore pressure in the shallow material. The shallow velocity changes alter the near‐surface conditions, can affect seismic hazard estimation, and may be improperly attributed to deeper processes without careful analysis.

58 GEOSCIENCES↗
DOE OSTI2024

4D Electrical Resistivity Imaging of Stress Perturbations Induced During High–Pressure Shear Stimulation Tests

Fluid flow through fractured media is typically governed by the distribution of fracture apertures, which are in turn governed by stress. Consequently, understanding subsurface stress is critical for understanding and predicting subsurface fluid flow. Although laboratory–scale studies have established a sensitive relationship between effective stress and bulk electrical conductivity in crystalline rock, that relationship has not been extensively leveraged to monitor stress evolution at the field scale using electrical or electromagnetic geophysical monitoring approaches. In this paper we demonstrate the use time–lapse 3–dimensional (4D) electrical resistivity tomography to image perturbations in the stress field generated by pressurized borehole packers deployed during shear–stimulation attempts in a 1.25 km deep metamorphic crystalline rock formation.

58 GEOSCIENCES↗
DOE OSTI2026

The Role of Normal Stress and Shear Stress Heterogeneity in the Inferred Depth‐Independence of Stress Drop

Earthquake stress drops are inferred to be independent of source depth, contradicting linear scaling predictions for earthquakes as frictional stick‐slip instabilities that assume increasing fault normal stress due to overburden. Here, we examine the scaling between averaged stress drops and increasing normal stress for simulated earthquake sequences in continuum rate‐and‐state fault models. The models produce a weaker dependence of stress drop on normal stress than the linearity of simple friction, which can be well‐fit by a sublinear power‐law. This result is more prominent when the fault dimension is much larger than nucleation scales. In such cases, the averaged behavior of ruptures is dominated by rupture propagation conditions, reflecting more heterogeneous shear stress conditions. As natural faults can be considerably larger than the smallest earthquakes they host, such a weaker scaling between averaged rupture conditions and normal stress may partially explain the lack of an inferred depth‐dependence of earthquake stress drops.

earthquake source↗