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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Superposition principle and nonlinear response in spin glasses

The extended principle of superposition has been a touchstone of spin-glass dynamics for almost 30 years. Here the Uppsala group has demonstrated its validity for the metallic spin glass, CuMn, for magnetic fields H up to 10 Oe at the reduced temperature T r = T/T g = 0.95, where T g is the spin-glass condensation temperature. For H > 10 Oe, they observe a departure from linear response which they ascribe to the development of nonlinear dynamics. The thrust of this paper is to develop a microscopic origin for this behavior by focusing on the time development of the spin-glass correlation length, ξ(t, t w ; H). Here, t is the time after H changes, and t w is the time from the quench for T > T g to the working temperature T until H changes. We connect the growth of ξ(t, t w ; H) to the barrier heights Δ(t w ) that set the dynamics. The effect of H on the magnitude of Δ (t w ) is responsible for affecting differently the two dynamical protocols associated with turning H off (TRM, or thermoremanent magnetization) or on (ZFC, or zero-field-cooled magnetization). This difference is a consequence of nonlinearity based on the effect of H on Δ (t w ). Superposition is preserved if Δ(t w ) is linear in the Hamming distance Hd (proportional to the difference between the self-overlap q EA and the overlap q[Δ(t w )]). However, superposition is violated if Δ(t w ) increases faster than linear in Hd. We have previously shown, through experiment and simulation, that the barriers Δ(t w ) do increase more rapidly than linearly with Hd through the observation that the growth of ξ(t,t w ;H) slows down as ξ(t,t w ; H) increases. In this paper, we display the difference between the zero-field-cooled ξ ZFC (t, t w ; H) and the thermoremanent magnetization ξ TRM (t, t w ; H) correlation lengths as H increases, both experimentally and through numerical simulations, corresponding to the violation of the extended principle of superposition in line with the finding of the Uppsala Group.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

The use of price-based demand response as a resource in electricity system planning

Utilities have conducted IRP for decades to consider both supply- and demand-side resources to meet bulk power system needs. More recently, an increasing number of states are requiring regulated utilities to file plans that identify distribution system needs, including DERs that can avoid or defer certain types of traditional utility investments cost-effectively. Price-based demand response (DR) is an underutilized resource that could substantially contribute to load flexibility. We find that evaluation of price-based DR as a solution to meet identified electricity system needs is uncommon at both planning processes. Where price-based DR is considered as a solution, methodologies are often deficient in the characterization and treatment of this resource. Based on our analysis of utility filings and state requirements, this brief provides recommendations for improving consideration of price-based DR in the context of long-term planning for bulk power and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Improved Kelbg Potentials for Z > 1 and Application to Carbon Plasmas

In this work, we present a general form for the electron‐ion diffractive potential derived from the quantum pair density matrix and fit to the improved Kelbg potential for atomic numbers up to $Z = 54$. We apply classical molecular dynamics using the improved Kelbg potential for carbon with various forms of the Pauli potential to compute internal energies and pressures for hot, dense plasma conditions. Our results are compared to an equation of state model based on path integral Monte Carlo and density functional theory simulations to examine the extent to which the improved Kelbg potential reproduces the internal energy and pressure of carbon plasmas. The regions of validity for carbon agree generally with those derived previously for hydrogen once pressure ionization effects are incorporated. Based on our carbon results and previously published hydrogen studies, we discuss the general applicability and limitations of these potentials for equation of state studies in warm dense matter and high energy density plasmas.

general physics↗
NASA NTRS1981

Improvements in lake volume predictions using Landsat data

A cumulative error in the water balance budget for Lake Okeechobee produces a one million acre-foot discrepancy in the predicted water volume over a 4-year period. The major source of error appears to be complex shoreline marshes that comprise 20 percent of the lake surface. The water balance budget model presently treats these marshes as open water. Using Landsat data, the vegetation in the lake's littoral zone was classified multispectrally to provide a data base for determining water budget information. First, the acreage of a given plant species in the littoral zone was obtained with satellite data. Second, the surface area occupied by plants (which therefore could not be considered open water) was used to adjust the vegetation acreage giving an effective water surface. Based on this information, more detailed representations of evapotranspiration and total water surface (and hence total lake volume) could be provided to the water balance budget computation.

Gervin, J. C.↗
NASA NTRS1992

Effect of surface tension on the onset of convection in a double-diffusive layer

The effect of surface tension on the stability of a double-diffusive layer is considered using linear stability analysis. The surface tension is assumed to vary linearly with temperature and solute concentration. The eigenvalue problem is solved by the Galerkin method. Results show that the predicted stability boundary based on Marangoni effects alone is completely altered in the presence of buoyancy effects induced by low gravity levels (about 10 exp -5 g). At reduced gravity levels, salt-finger instability may onset in the overstable mode due to the stabilizing effect of surface tension. Fluid properties in terms of the Prandtl and the Lewis numbers have a profound effect on the stability conditions; opposite stability characteristics are found in salt solutions and in molten metals.

Chen, C. F.↗
NASA NTRSDate not supplied

Molecular dynamics simulation of effects of solutes on dislocation propagation in Ni-based superalloys

Ni-based superalloys are used in the hot sections of jet turbine engines because of their high strength, stability and resistance to oxidation. The properties of these alloys can be further optimized by adding alloying elements. Therefore, a fundamental knowledge on the effect of different elements on properties of Ni-based superalloys is required. Molecular dynamics simulation could shed light here but its application is hindered by the absence of reliable and computationally cheap semi-empirical potential of the interatomic interaction for 4 and more element alloys. We will present a new Ni-Al-Cr-Nb Finnis-Sinclair (FS) potential specially designed to simulate the dislocation propagation from to  phase. In order to construct this potential, we designed a special algorithm to incorporate the data on element partitioning in the potential development procedure. For example, it is known from experiment, that Cr is mostly present in the gamma phase. Figure 1 shows a snapshot obtained after equilibration of the model of the Ni68Al17Cr15 alloy at T=1000 K using the hybrid Monte-Carlo (MC)/molecular dynamics (MD) simulation with the developed semi-empirical potential. One can clearly see that the Cr partitioning is in agreement with the experimental data. We will discuss the developed algorithm to incorporate the solute partition data in details. Using the developed semi-empirical potential, we first investigated the effect of anti-site defects in the  phase on the single dislocation propagation. It was found that the dislocation velocity increases with the increasing of the anti-site defect concentration. This effect was attributed to smaller number of Al-Al pairs forming during the dislocation migration in the presence of the anti-site defects. Next, we investigated the effect of Nb on the dislocation pair propagation in the Kolbe mechanism. It was found that the addition of Nb leads to considerable decrease in the dislocation propagation rate. This is in agreement with the experimental data on the effect of Nb on the creep resistance of the Ni-based superalloys. We will discuss the origin of this effect.

Mikhail I. Mendelev↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗
NASA NTRS2001

Relative effectiveness of kinetic analysis vs single point readings for classifying environmental samples based on community-level physiological profiles (CLPP)

The relative effectiveness of average-well-color-development-normalized single-point absorbance readings (AWCD) vs the kinetic parameters mu(m), lambda, A, and integral (AREA) of the modified Gompertz equation fit to the color development curve resulting from reduction of a redox sensitive dye from microbial respiration of 95 separate sole carbon sources in microplate wells was compared for a dilution series of rhizosphere samples from hydroponically grown wheat and potato ranging in inoculum densities of 1 x 10(4)-4 x 10(6) cells ml-1. Patterns generated with each parameter were analyzed using principal component analysis (PCA) and discriminant function analysis (DFA) to test relative resolving power. Samples of equivalent cell density (undiluted samples) were correctly classified by rhizosphere type for all parameters based on DFA analysis of the first five PC scores. Analysis of undiluted and 1:4 diluted samples resulted in misclassification of at least two of the wheat samples for all parameters except the AWCD normalized (0.50 abs. units) data, and analysis of undiluted, 1:4, and 1:16 diluted samples resulted in misclassification for all parameter types. Ordination of samples along the first principal component (PC) was correlated to inoculum density in analyses performed on all of the kinetic parameters, but no such influence was seen for AWCD-derived results. The carbon sources responsible for classification differed among the variable types with the exception of AREA and A, which were strongly correlated. These results indicate that the use of kinetic parameters for pattern analysis in CLPP may provide some additional information, but only if the influence of inoculum density is carefully considered. c2001 Elsevier Science Ltd. All rights reserved.

NASA Center KSC↗
DOE OSTI2024

Ferroelectric FET-based context-switching FPGA enabling dynamic reconfiguration for adaptive deep learning machines

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)–based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Toward Engineering of Solution Microenvironments for the CO 2 Reduction Reaction: Unraveling pH and Voltage Effects from a Combined Density-Functional–Continuum Theory

Engineering the electrolyte microenvironment represents an attractive route to tuning the selectivity of electrocatalytic reactions beyond catalyst composition and morphology. However, harnessing the full potential of this approach requires understanding the interplay between voltage, electrolyte composition, and adsorbate binding within the electrical double layer, which is absent from the usual theoretical approaches. In this study, we apply a recently developed density functional theory (DFT)–continuum approach based on the effective screening medium method and reference interaction site model (ESM–RISM) to explore electrolyte effects with an enhanced description of the electrochemical interface. Furthermore, applying this method to the binding of CO adsorbates in potassium-containing electrolytes on copper, a problem of direct relevance to CO 2 electroreduction to value-added products, we show that the interdependence of voltage and pH leads to an unexpected change in adsorption site preference on Cu(001) terraces. Our findings highlight the often-overlooked importance of the electrical double-layer structure for predicting catalyst operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Anisotropy of nickel-base superalloy single crystals

The effects of crystal orientation on the mechanical properties of single crystals of the nickel-based superalloy Mar-M247 are investigated. Tensile tests at temperatures from 23 to 1093 C and stress rupture tests at temperatures from 760 to 1038 C were performed for 52 single crystals at various orientations. During tensile testing between 23 and 760 C, single crystals with high Schmid factors were found to be favorably oriented for slip and to exhibit lower strength and higher ductility than those with low Schmid factors. Crystals which required large rotations to become oriented for cross slip were observed to have the shortest stress rupture lives at 760 C, while those which required little or no rotation had the longest lives. In addition, stereographic triangles obtained for Mar-M247 and Mar-M200 single crystals reveal that crystals with orientations near the -111 had the highest lives, those near the 001 had high lives, and those near the 011 had low lives.

Mackay, R. A.↗
NASA NTRS2006

FETs Based on Doped Polyaniline/Polyethylene Oxide Fibers

A family of experimental highly miniaturized field-effect transistors (FETs) is based on exploitation of the electrical properties of nanofibers of polyaniline/ polyethylene oxide (PANi/PEO) doped with camphorsulfonic acid. These polymer-based FETs have the potential for becoming building blocks of relatively inexpensive, low-voltage, highspeed logic circuits that could supplant complementary metal oxide/semiconductor (CMOS) logic circuits. The development of these polymerbased FETs offers advantages over the competing development of FETs based on carbon nanotubes. Whereas it is difficult to control the molecular structures and, hence, the electrical properties of carbon nanotubes, it is easy to tailor the electrical properties of these polymerbased FETs, throughout the range from insulating through semiconducting to metallic, through choices of doping levels and chemical manipulation of polymer side chains. A further advantage of doped PANi/PEO nanofibers is that they can be made to draw very small currents and operate at low voltage levels, and thus are promising for applications in which there are requirements to use many FETs to obtain large computational capabilities while minimizing power demands. Fabrication of an experimental FET in this family begins with the preparation of a substrate as follows: A layer of silicon dioxide between 50 and 200 nm thick is deposited on a highly doped (resistivity 0.01 W.cm) silicon substrate, then gold electrodes/contact stripes are deposited on the oxide. Next, one or more fibers of camphorsulphonic acid-doped PANi/PEO having diameters of the order of 100 nm are electrospun onto the substrate so as to span the gap between the gold electrodes (see Figure 1). Figure 2 depicts measured current-versus-voltage characteristics of the device of Figure 1, showing that saturation channel currents occur at source-todrain potentials that are surprisingly low, relative to those of CMOS FETs. The hole mobility in the depletion regime in this transistor was found to be 1.4 10(exp -4) sq cm/(V.s), while the one-dimensional charge density at zero gate bias was estimated to be approximately one hole per 50 two-ring repeat units of polyaniline, consistent with the rather high channel conductivity (approx. 10(exp -3) S/cm). Reducing or eliminating the PEO content of the fibers is expected to enhance the properties of future versions of this transistor.

Theofylaktos, Noulie↗
DOE OSTI2021

Overview and technology opportunities for thermochemically-produced bio-blendstocks

Global demand for transportation fuels is projected to increase 40% by 2040, and biomass-derived fuels (biofuels) play a crucial role in substituting fossil fuels and mitigating greenhouse gas emissions. Currently, biofuels are mainly consumed as blendstocks combined with petroleum-based fuels, and effective conversion technologies can address the quality challenges for offering standalone biofuels. Thermochemical conversion process is one of the most promising pathways among existing technologies for biofuel production. However, the major barriers are unwanted characteristics (e.g., thermal instability) of intermediate products, such as bio-oil, and required upgrading treatments for producing compatible fuels. Here, this study highlights the merits and critical challenges of thermochemical conversion and physicochemical upgrading technologies for bio-blendstock production from lignocellulosic biomass. The novelty of this study lies in potential directions for future research through both critical and systematic literature reviews, and the proposed intensified process for lignocellulosic-based fuel blendstocks production. It is concluded that recovery and fractionation strategies (e.g., quenching and stripping) can maximize process yields and add values in the efficient conversion pathways. Effective quenching can stop secondary free radical reactions and improve liquid yields over gas and solid yields. Stripping process can improve process yield, catalyst lifespan, and thermal stability. It is further concluded that physicochemical treatments are not as effective as thermochemical treatments, but have advantages of mild operating conditions and potential for integrated solutions in conjunction with other treatments.

09 BIOMASS FUELS↗
NASA NTRS1974

Effects of background event rate and critical signal amplitude on vigilance performance

Subjects monitored a display consisting of the repetitive presentation of pairs of movements of a bar of light. A neutral background event, for which no overt response was required, was a double deflection of 24 mm. The critical signal for detection was a longer deflection in the second movement within an event. Detection probability was greater for incremental excursions of 33% relative to 8.3% of the base movement. This effect was enhanced twofold when the event rate in which the signals were embedded was 21 as compared to 6 events/min. The results are considered in terms of the elicited observing rate hypothesis proposed by Jerison (1970).

Metzger, K. R.↗
DOE OSTI2024

Effect of Iron Contamination and Polysilicon Gettering on the Performance of Polysilicon‐Based Passivating Contact Solar Cells

Over the past decade, silicon solar cells with carrier-selective passivating contacts based on polysilicon capping an ultra-thin silicon oxide (commonly known as TOPCon or POLO) have demonstrated promising efficiency potentials and are regarded as an evolutionary upgrade to the PERC (passivated emitter and rear contact) cells in manufacturing. The polysilicon-based passivating contacts also exhibit excellent gettering effects that relax the wafer and cleanroom requirements to some extent. Here, in this work, we experimentally explore the impact of bulk iron contamination and polysilicon gettering on the passivation quality of the polysilicon/oxide structure and the resulting solar cells performance. Results show that both n- and p-type polysilicon/oxide passivating contacts are not affected by iron gettering, demonstrating robust and stable passivation quality. However, for a very high bulk iron contamination (1 × 10 13 cm −3 ), the accumulated iron in the p-type lightly boron-doped emitter in crystalline silicon would degrade the emitter saturation current density. This can cause a reduction in both open-circuit voltage and short-circuit current. Meanwhile, this very high iron content (1 × 10 13 cm −3 ) can further degrade the fill factor and temperature coefficient of the cells. On the other hand, for an initial iron content of 2 × 10 12 cm −3 , which should be well above the iron level in the current industrial Czochralski silicon wafers, the resulting cells demonstrate similar performance as the control group with no intentional iron contamination. This work brings attention to both the benefits of polysilicon gettering effects as well as the potential degradation due to the accumulation of metal impurities in the p-type emitter region.

14 SOLAR ENERGY↗