SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Fluctuating semiflexible sheets dispersed in a fluid are simulated under simple shear flow. A dynamical crumpling transition is observed, flipping statistics are quantified, and rheological properties are estimated for dilute suspensions of sheets.
A series of dedicated experiments from DIII-D tokamak provide spatially and temporally resolved measurements of electron density and temperature, and multiscale and multichannel fluctuations over a wide range of conditions. Measurements of long wavelength density fluctuations in the type-I ELMing H-mode pedestal routinely revealed a coexistence of multiple instabilities that exhibit dramatic different dynamic behaviors as q 95 and temperature gradient are varied, apparently responsible for limiting pedestal temperature profiles. Two distinct frequency bands of density fluctuations are modulated with ELM cycle with frequency above 200kHz propagating in the electron diamagnetic direction in the lab frame (electron mode) and below 200kHz propagating in the ion diamagnetic direction (ion mode). The electron mode amplitude peaks near the electron temperature gradient region and increases with q 95 which seems to be correlated with the increased χ e at higher q 95 , similar to the characteristics expected for Micro-tearing Mode (MTM). At higher q 95 , during the inter-ELM period, the ion mode decays at later phase of the ELM cycle. Consistently, the poloidal correlation length of the ion mode is also found to reduce which suggests the possible E×B flow shear suppression of ion mode at later phase of the ELM cycle as the Er well recovers. In contrast, the electron mode grows during the ELM cycle and reaches saturation at around 50-60% of ELM period. Linear gyrokinetic simulations find the MTMs as the most unstable mode in the pedestal electron temperature gradient region. The higher q 95 and lower magnetic shear destabilize MTMs. These observations provide key insights of the underlying physics of multifield properties and rich dataset of experimental 'fingerprints' that enable new tests of the theoretical pedestal models and lead to developing a predictive model for pedestal formation on ITER and future burning plasma experiments.
SUMMARY Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson’s ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth’s structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.
Here, in this work, a priori analysis of machine learning (ML) strategies is carried out with the goal of data-driven wall modeling for large eddy simulation (LES) of gas turbine film cooling flows. High-fidelity flow datasets are extracted from wall-resolved LES (WRLES) of flow over a flat plate interacting with the coolant flow supplied by a single row of 7-7-7 shaped cooling holes inclined at 30 degrees with the flat plate at different blowing ratios (BR). The WRLES are performed using the high-order Nek5000 spectral element computational fluid dynamics (CFD) solver. Light gradient boosting machine (LightGBM) is employed as the ML algorithm for the data-driven wall model. Parametric tests are conducted to systematically assess the influence of a wide range of input flow features (velocity components, velocity gradients, pressure gradients, and fluid properties) on the accuracy of ML wall model with respect to prediction of wall shear stress. In addition, the use of spatial stencil and time delay is also explored within the ML wall modeling framework. It is shown that features associated with gradients of the streamwise and spanwise velocity components have a major impact on the prediction fidelity of wall model, while the effect of gradients of wall-normal velocity component is found to be negligible. Moreover, adding flow feature information from an x-y-z spatial stencil significantly improves the ML model accuracy and generalizability compared to just using local flow features from the matching location. Overall, highest prediction accuracy is achieved when both spatial stencil and time delay features are incorporated within the data-driven wall modeling paradigm.
Modifying asphalt is a potentially high-value application for reusing waste plastics because of the high-volume usage of asphalt in highway construction. However, simply blending hot plastics and asphalt encounters difficulties related to the poor solubility of polymers, which limits the formation of a swollen network with asphalt molecules. The polymer phases also tend to coalesce and separate from asphalt during high-temperature storage in static conditions. The present study developed an innovative process to stabilize waste plastics in asphalt and improve binder storage stability by using lightly pyrolyzed crumb rubber together with a chemical compatibilizer. Both polymers were extruded to produce a thermoplastic elastomer (TPE) for asphalt modification. The mechanical performance and chemical reactions of TPEs were characterized via tension test and Fourier transform infrared spectroscopy. The storage stability and rheological properties of modified binder blends were evaluated through laboratory asphalt stability test and dynamic shear rheometer test. Polymer phases and network structures were characterized through optical microscopy. It was found that the pyrolyzed and reactive extrusion process improved the rubber solubility and polymer interaction, and therefore the storage stability of modified binder blends. The co-existence of rigid plastic and soft rubbery regimes in an entangled network provided a promising pathway to improve the mechanical performance of asphalt binders in both high- and low-temperature domains.
Cholesteric liquid crystals (CLCs) are compelling responsive materials with applications in next-generation sensing, imaging, and display technologies. While electric fields and surface treatments have been used to manipulate the molecular organization and, subsequently, the optical properties of CLCs, their response to controlled fluid flow has remained largely unexplored. Here, in this study, we investigate the influence of microfluidic flow on the structure of thermotropic CLCs that can exhibit structural coloration. We demonstrate that the shear forces that arise from microfluidic flow align the helical axis of CLCs; alignment is a prerequisite for harnessing the promising photonic properties of CLCs. Moreover, we show that microfluidic flow can generate non-equilibrium structures exhibiting photonic band gaps that are inaccessible in the stationary cholesteric phase. Our findings have implications for the use of CLCs in applications involving flow processing such as additive manufacturing.
Atmospheric acoustic waves transmit energy into the solid Earth through air-to-ground coupling. These waves are recorded by seismic sensors and provide insight into both atmospheric phenomena and subsurface properties. Interpreting these signals is often challenging because they are modulated by subsurface structure and the incidence angle of the acoustic wave. This study examines acoustic--seismic coupling generated by the 2012 Camp Minden Explosion, which was recorded by hundreds of seismoacoustic stations. We apply a novel technique to quantify the seismic particle motion, model coupled waves with a propagator matrix approach, and apply a Bayesian inversion to infer properties of the shallow subsurface. Our analysis reveals that prograde motion is widespread and focused in low shear-wave velocity regions, such as the Mississippi Embayment, and retrograde motion is more common in higher shear-velocity areas. Inversion results at some stations produce plausible subsurface models with strong waveform fits, while inversions at other sites are less successful. These results indicate prograde particle motion in air-to-ground coupled waves is more prevalent than previously recognized and may serve as a diagnostic for shallow velocity structure. Our comprehensive modeling and inversion framework provides a potential method to extract layered near-surface properties from acoustic-seismic coupling observations.
Viton® A is a semi-crystalline copolymer of polyvinylidene fluoride and hexafluoropropylene used in various engineering applications due to its mechanical properties and chemical inertness. In situ ultrasonic spectroscopy and x-ray radiography measurements were performed in a Paris–Edinburgh press to measure the pressure dependence of the transverse and longitudinal acoustic velocity of the fluoroelastomer A-500 from 2.7 to 5.7 GPa at 296 K. In addition, we performed high-pressure Brillouin scattering measurements to obtain acoustic velocities from ambient pressure to 5.7 GPa to supplement the ultrasonic measurements, especially at low pressures. The acoustic velocities were then used to calculate a pressure–volume (P–V) equation of state, the bulk and shear moduli, and the Poisson's ratio. These quantities are compared with the reported pressure-dependent properties of related polymers over this range of pressures.
Third-generation silicon carbide (SiC) composites reinforced by SiC fibers (Hi-Nicalon S [HNS] and Tyranno SA3 [SA3]) are attractive for use in next generation reactors owing to their high strength and chemical inertness at high temperatures, as well as enhanced radiation tolerance under neutron irradiation environments. To optimize composite performance, the interfacial mechanical properties of chemical vapor–infiltrated (CVI) SiC f /SiC composites are investigated in this effort by using a slant interface micropillar compression testing procedure. The micropillar test specimens, containing an inclined pyrolytic carbon (PyC) interphase, are prepared using a focused ion beam. The novel microcompression testing successfully quantifies the debond shear strength and internal friction coefficient of micropillar test samples by using the Mohr-Coulomb formulation. Furthermore, according to four types of SiCf/SiC composite microcompression test results, interfacial properties and debond mechanisms are significantly affected by the PyC layer thickness, the local bonding mechanism of the PyC interphase on the SiC fiber surface, and the surface roughness of fibers. Regardless of PyC thicknesses, SA3-reinforced CVI SiC f /SiC composites are found to have much higher debond shear strengths than HNS-reinforced SiC f /SiC CVI composites. By using this micropillar compression technique alongside analytical methods, we uncover new understandings of PyC interface properties. Additionally, the micropillar test results obtained are correlated with macroscopic mechanical properties of neutron-irradiated CVI SiCf/SiC composites.
Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.
Kagome metals are a unique class of quantum materials characterized by their distinct atomic lattice arrangement, featuring interlocking triangles and expansive hexagonal voids. These lattice structures impart exotic properties, including superconductivity, interaction-driven topological many-body phenomena, and magnetism, among others. The kagome metal CsM 3 Te 5 (where M = Ti, Zr, or Hf) exhibits both superconductivity and nontrivial topological electronic properties, offering a promising platform for exploring topological superconductivity. This study employs first-principles density functional theory calculations to systematically analyze the elastic, mechanical, vibrational, thermodynamic, and electronic properties of CsM 3 Te 5 (M = Ti, Zr, Hf). Our calculations reveal that the studied compounds—CsTi 3 Te 5 , CsZr 3 Te 5 , and CsHf 3 Te 5 —are ductile metals with elastic properties akin to the hexagonal Bi and Sb, with average elastic constants, including a bulk modulus of 27 GPa, a shear modulus of 11 GPa, and Young's modulus of 29 GPa. We observe peculiar dispersionless, flat, phonon branches in the vibrational spectra of these metals. Additionally, we thoroughly analyze the symmetries of the zone-center phonon eigenvectors and predict vibrational fingerprints of the Raman- and infrared-active phonon modes. The analysis of thermodynamic properties reveals the Einstein temperature for CsTi 3 Te 5 , CsZr 3 Te 5 , and CsHf 3 Te 5 to be 66, 54, and 53 K, respectively. Our orbital-decomposed electronic structure calculations reveal significant in-plane steric interactions and multiple Dirac band crossings near the Fermi level. We further investigate the role of spin-orbit coupling effect on the studied properties. Furthermore, this theoretical investigation sheds light on the intriguing quantum behavior of kagome metals.
In this study, X-ray photon correlation spectroscopy (XPCS) microrheology and conventional bulk rheology were performed on silica nanoparticle dispersions associated with battery electrolyte applications to probe the properties of these specific complex materials and to explore the utility of XPCS microrheology in characterizing nanoparticle dispersions. Sterically stabilized shear-thickening electrolytes were synthesized by grafting poly(methyl methacrylate) chains onto silica nanoparticles. Coated silica dispersions containing 5–30 wt % nanoparticles dispersed in propylene carbonate were studied. In general, both XPCS microrheology and conventional rheology showed that coated silica dispersions were more viscous at higher concentrations, as expected. The complex viscosity of coated silica dispersions showed shear-thinning behavior over the frequency range probed by XPCS measurements. However, measurements using conventional mechanical rheometry yielded a shear viscosity with weak shear-thickening behavior for dispersions with the highest concentration of 30% particles. Our results indicate that there is a critical concentration needed for shear-thickening behavior, as well as appropriate particle size and surface polymer chain length, for this class of nanoparticle-based electrolytes. The results of this study can provide insights for comparing XPCS microrheology and bulk rheology for related complex fluids and whether XPCS microrheology can capture expected macroscopic rheological properties by probing small-scale particle dynamics.
Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.
Small diameter (<100 μm) fibers (e.g. carbon fibers, Kevlar, and fiberglass) and wires (e.g. ultrafine copper and aluminum wires) are frequently used in many different engineering applications, such as for light weighting structures, electromagnetic shielding for aircraft/infrastructure/EVs, vibration damping, biological sensors, aerospace electrical devices, and electric windings just to name a few. Due to the manufacturing process, the fibers and wires are pulled and stretched to produce a preferential alignment. Therefore, thin fibers and wires typically display different properties along the length of the fiber as opposed to their cross section and many fibers/wires are considered transversely isotropic. The axial properties of fibers/wires can be ascertained via tensile testing of single-filaments or fiber tows, but the radial properties require much more effort to measure. Knowing these properties is important for the accurate prediction of micromechanical models and manipulation of fibers during micromanufacturing. In this paper, a new technique was developed to determine the transverse/shear moduli and strength of a material by conducting tensile tests of the material at increasing misalignment angles from the tensile axis. Due to the transversely isotropic nature of the material, the transverse/shear moduli and strength influence the experimental results recorded by the test machine to different degrees based on the amount of misalignment in the test setup. An equation was derived to determine the influence of each of the material properties based on the misalignment angle by manipulating the stiffness matrix for transversely isotropic materials using the transformation matrices. Then, curve fitted coefficients were used to identify the material properties. Here, the strengths were similarly determined by curve fitting an off-axis Tsai-Hill failure criteria to determine the influence of transverse, shear, and tensile strengths based on the complex loading condition provided by the off-axis tensile test. Zoltek Panex 35 carbon fibers were used to demonstrate this new technique and the determined properties were then compared to those obtained from nanoindentation and from literature. Fracture surfaces provide insight into the different failure mechanisms at various misalignment angles.
The morphological features of particles, notably shape anisotropy, critically influence the rheological properties of dense suspensions, spanning both natural and engineered systems. This work explores the potential of using shape memory particles to dynamically regulate suspension fluid flow through controllable shape transformations. First, we synthesize shape-memory particles with programmable anisotropy from liquid crystal elastomers, such that the stiffness and shapes of the particles can be tuned by manipulating temperature. Our findings reveal that suspensions from such particles exhibit significant tunability in shear thickening behavior, transitioning from discontinuous shear thickening to a Newtonian-like response within a narrow temperature range of 60 ° C. This capability to modulate rheological responses in situ presents an approach for addressing processing challenges in many applications where control over flow behavior is paramount. Furthermore, we also show that suspensions composed of these anisotropic particles can undergo physical aging, and evolve into a glassy state. This state can be escaped upon activation of the shape memory effect. This reversibility underscores the potential for using such materials to engineer systems that can enter or come out of kinetic arrest by leveraging internal mechanical responses to external stimuli. The insights gained here not only broaden our understanding of the interplay between particle geometry and suspension dynamics but also pave the way for leveraging ensembles of stimuli-responsive objects to precisely control collective behaviors in many-body systems.
A new technique called friction stir interlocking (FSI) was applied for lap-joining of AZ31 magnesium sheet and thermoset carbon fiber reinforced polymer (TS-CFRP) sheet. Instead of directly joining AZ31 and TS-CFRP, a series of magnesium interlocks were used to friction stir weld with AZ31 sheet to enable joining with TS-CFRP. Microstructural characterization of joint cross sections showed that a tool rotation rate of 800 rpm produced incomplete mixing of magnesium interlocks and the top AZ31 sheet, while 1200 rpm achieved sufficient material mixing to avoid unwelded regions. Mechanical property characterization showed that AZ31/TS-CFRP dissimilar joints failed through the magnesium interlock shanks during lap shear tensile testing, with a maximum normalized joint strength reaching ~100 MPa.
Supramolecular aqueous complexes of a synthesized zwitterionic surfactant, 2-(dimethyl(octadecyl)ammonio)acetate (stearyl betaine) and diethylenetriamine, have been found to display pH-tunable rheological properties without the need for a co-surfactant. For this supramolecular system, the dynamically changing interplay among the constituents is responsible for the rich phase behavior including hollow nanotubules, bilayers, and vesicles. A further hypothesis is that hollow nanotubular structure is highly desirable for achieving chain entanglement geometries with reduced mesh size. The rheological properties of the suspension were measured using a rotational rheometer. An analysis using zero-shear viscosities at various temperatures as well as differential scanning calorimetry(DSC) helped shed light on thermodynamic and phase transitions taking place. The morphology of the suspension was analyzed by using a combination of small angle x-ray scattering(SAXS) and atomic force microscopy(AFM). Large steady-shear viscosities (up to ~160 Pa.s) and viscoelastic behavior was observed in acidic conditions which diminished upon increasing the pH. A novel analysis consisting of measured thermodynamic quantities such as activation energy(E a ) and enthalpy of transition(ΔH T ) further allude to the rich pH-sensitive nanoarchitecture of the system. Rather than commonly observed wormlike micelles, hollow nanotubules, as long as 1.6 µm were observed at pH 4 which disintegrated to bilayer sheets and vesicles upon increasing pH. Finally, gaining a comprehensive understanding from all the results, a transition mechanism between different suspension architectures was proposed.
Hollow extrusions are used in many industries to make strong, lightweight, and affordable structures. Shear Assisted Processing and Extrusion (ShAPE) enables the extrusion of many alloys, often with noteworthy properties. In this investigation, ShAPE was used to extrude 1 and 2 mm wall tubes of aluminum alloy 6063 measuring 12 mm in diameter at extrusion speeds up to 3.8 m/min. This speed is 10 and 15 times faster than has been reported for any alloy using ShAPE and friction stir back extrusion, respectively. Increasing the extrusion speed from 0.7 to 3.8 m/s resulted in using 68% less process energy at steady state. Microstructural analysis and tensile testing were performed on as-extruded tubes and tubes with a direct T5 heat treatment. Grain refinement from 65 µm to below 20 µm was observed. Growth of macroscale Mg 2 Si strengthening precipitates was not observed. ShAPE processing resulted in recrystallization, and extrusion speed was shown to influence final extrudate texture. Here, transmission electron microscopy analysis revealed that the as-extruded microstructure was free of nanoscale ß" strengthening precipitates. Nanoscale ß" was prominent in direct-aged (T5) ShAPE tubes, similar to a slightly over-aged conventional T6 microstructure. As-extruded tubes had ultimate tensile strengths on par with conventional T5 extrusions and approximately double the total elongation. Tubes that underwent a T5 heat treatment had yield and ultimate strengths averaging 198 and 234 MPa, respectively, with an average total elongation of 11%. The tensile test results are comparable to a conventional T6 heat treatment, but without the solutionization heat treatment step. Mechanical properties either remained flat or improved with increased extrusion speed, which suggests that ShAPE can be scaled up to an industrial process to create energy- and economically efficient high-strength extrusions.