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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO2 emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/Tair) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/Tair and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. Additionally, we found that the top drivers of LST/Tair, explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/Tair variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/Tair is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
NASA NTRS1982

Aspects of clear air turbulence severity forecasting and detection

Factors influencing the accuracy of the forecasts of incidences of clear air turbulence (CAT) are discussed, along with techniques for improved verification. Descriptive ranking terms for the intensity of CAT events, ranging from light to extreme, are developed, and meteorological parameters used for predictions are reviewed, including jetstream core location, vertical and horizontal wind shears, stable layers, tropopause height, trough speed, 500-mb vorticity, surface fronts, pressure centers and cyclogenesis, and wind speeds near mountain ridges. Methods of remote detection of CAT, particularly by using radiometry sensitive to the IR water vapor band, are noted to have had some success in detecting actual CAT events and decreasing false alarms. Statistical aspects of CAT encounter severity are discussed, including the establishment of confidence intervals for thresholds of detection of CATs of varying intensities.

Ehernberger, L. J.↗
NASA NTRS1987

Coronal mass ejections: The long-term variation of their occurrence rate and the solar wind mass flux

Solar Coronal Mass Ejections (CMEs) from the Sun are an important aspect of coronal physics, and a potentially important contributor to the solar wind mass flux. However, despite significant progress in studies of CMEs since their discovery in the early 70's, questions remain about their effects on the interplanetary medium. A study is done of the long-term variations of the occurrence rates of CMEs, of activity tracers related to CMEs, and of the solar wind particle flux. CMEs are most directly detected by scattered electron radiation in white light. To estimate their long-term occurrence frequency and their contributions to the in-ecliptic solar wind mass flux, observed CME rates must be corrected for instrumental duty cycles, detection efficiency out of the plane of the sky, mass detection thresholds, and geometrical considerations. These corrections are evaluated using data on solar CMEs from the spaceborne Skylab, SMM, and SOLWIND coronagraphs and on interplanetary plasma clouds from the HELIOS white light photometers. Variations in the CME rate and the contribution of CMEs to the solar wind mass flux are traced over nearly a complete solar activity cycle.

Webb, David F.↗
NASA NTRS1987

Gigarad-tolerant power switches and memory elements

A new class of silicon devices operating without p-n junctions has been studied for radiation hardness. The electric field in these devices is uniform over most of the device, thus causing high-voltage breakdown to increase with electrode spacing. Deep acceptor levels are created in the silicon by diffusing gold atoms into the lattice, or by electron radiation. These acceptor levels, near the center of the band gap, trap out electrons so that the low resistivity of the n-type doped wafer can be raised to intrinsic resistivity. Switching between a high-resistivity state at low currents and a low-resistivity state at high currents occurs at a definite threshold voltage, exhibiting characteristics similar to silicon-controlled rectifiers. Compensating 0.1 ohm-meter (10 ohm-cm) silicon wafers requires electron fluxes of 10 to the 23rd electrons per square meter (10 to the 19th electrons per square centimeter). Experiments demonstrating gamma tolerance to 1 Gigarad(Si) are described. Calculations for maximum tolerable neutron fluence are shown, and a phenomenological explanation for these results is presented.

Joyner, W. T.↗
NASA NTRS2008

Optimization of Angular-Momentum Biases of Reaction Wheels

RBOT [RWA Bias Optimization Tool (wherein RWA signifies Reaction Wheel Assembly )] is a computer program designed for computing angular momentum biases for reaction wheels used for providing spacecraft pointing in various directions as required for scientific observations. RBOT is currently deployed to support the Cassini mission to prevent operation of reaction wheels at unsafely high speeds while minimizing time in undesirable low-speed range, where elasto-hydrodynamic lubrication films in bearings become ineffective, leading to premature bearing failure. The problem is formulated as a constrained optimization problem in which maximum wheel speed limit is a hard constraint and a cost functional that increases as speed decreases below a low-speed threshold. The optimization problem is solved using a parametric search routine known as the Nelder-Mead simplex algorithm. To increase computational efficiency for extended operation involving large quantity of data, the algorithm is designed to (1) use large time increments during intervals when spacecraft attitudes or rates of rotation are nearly stationary, (2) use sinusoidal-approximation sampling to model repeated long periods of Earth-point rolling maneuvers to reduce computational loads, and (3) utilize an efficient equation to obtain wheel-rate profiles as functions of initial wheel biases based on conservation of angular momentum (in an inertial frame) using pre-computed terms.

Lee, Clifford↗
NASA NTRS1991

Saltation threshold reduction due to the electrostatic agglomeration of fine particles

Particles between 80 and 110 microns in diameter are the most easily moved by the wind. As the particle size decreases below 60 microns, they are increasingly more difficult to move by surface winds, and a number of experiments were performed in an attempt to reduce the required wind velocity. These include: (1) the bombardment of a bed of fine particles by particles near the optimum size, the larger particles kicking the fine particles into the windstream where they are entrained; and (2) the electrostatic agglomeration of fine particles into sizes more easily saltated. The results of these experiments are discussed.

Leach, Rodman N.↗
DOE OSTI2020

Above-threshold multiphoton photoemission from noble metal surfaces

Exciting solids with intense femtosecond laser pulses prompts electrons of the interrogated material to respond in a highly nonlinear manner, as is evident in the emission of high-order harmonic radiation and photoelectrons with kinetic energies well above that of the driving photons. Such high-field interactions can be resolved, for example, in above-threshold multiphoton photoemission (ATP) spectroscopy. In this work, we interrogate the nonlinear photoelectric responses of the pristine copper, silver, and gold noble metal surfaces in (111) and (100) crystal orientations in the perturbative regime. Using multiphoton photoemission spectroscopy (mPP) excited by finely tuned optical fields, we characterize enhancement of the mPP and ATP yields from (111) surfaces in selected k||-momentum ranges when the occupied Shockley surface (SS) states are (near-)resonantly coupled by multiphoton transitions to image potential (IP) intermediate states in the excitation process. The ATP signal from the IP states of (111) surfaces is largely defined by their formation through polarization of SS electrons; this observation is contrasted with ATP experiments from the Ag(100) surface, for which the SS becomes an unoccupied resonance and the IP states can only be excited from bands with significantly more bulk character. In addition, based on the optical power and nonlinear order-dependent mPP spectra, we provide evidence for ATP being a one-step, rather than a sequential process, as previously postulated.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Long- and short-range correlations and their event-scale dependence in high-multiplicity pp collisions at $ \sqrt{s} $ = 13 TeV

Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at √s = 13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(Δη ~ 0) and long-range (1.6 < |Δη| < 1.8) in pseudorapidity are extracted on the near-side (Δ~ 0). They are reported as a function of transverse momentum (p T ) in the range 1 < p T < 4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-p T leading particles or jets for varying p T thresholds. The results demonstrate that the long-range “ridge” yield, possibly related to the collective behavior of the system, is present in events with high-p T processes as well. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement for the p T dependency, while overestimating the event-scale dependency.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Role of E × B Drift in Divertor Detachment Control via Boron Powder Injection on EAST

Here, the effects of B powder injection on plasma detachment about EAST discharge were studied by using SOLPS-ITER code package with the effects of E × B drifts considered. The simulation results show that plasma detachment occurs at the inner target in favourable toroidal magnetic field (B t ) direction at a relatively low B powder flow rate, one order of magnitude lower than that at the outer target. In a similar scenario with unfavourable B t , it is found that the detachment thresholds of B flow rate for both the inner and outer targets are close and of the same order as that for the outer target with favourable Bt. In favourable B t direction at B powder flow rate of 1.2 × 10 21 atoms/s, a localized, broadened high-density region is formed near the inner target benefitted by the injection location and the E × B drift, and a radiation-intensified zone, mostly contributed by B 1+ and B 2+ , occurs there. The E × B drift facilitates plasma detachment at the inner target and simultaneously amplifies the in–out divertor asymmetry. In addition, the simulation results with three different injection locations show that the injection from outer strike point leads to the lowest Z eff inside the separatrix and has an intermediate flow rate for detachment at the outer target, comparing with the X-point and upstream locations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Nanostructures evolution assessment and spectroscopic properties modification induced by electronic energy loss in KTaO 3 crystal

Modifications of micro/nanostructure and photoelectric properties under swift ion irradiation (645 MeV Xe 35+ ) of KTaO 3 crystal with varying electronic energy losses (7.2–31.4 keV/nm) and ion velocities (0.18–5.00 MeV/u) have been studied by combining experimental and calculated approaches. The i-TS calculations combined with molecular dynamics simulations are compared with the experimental observations, revealing the inner track fine structures from individual spherical defects to continuous ion tracks with core–shell morphologies, and a quantitative relationship, including the melting (0.42 eV/atom), damage (0.75 eV/atom), and amorphous (1.71 eV/atom) thresholds, is established to successfully predict the inner disorder/amorphous proportions and track damage morphologies. The surface nanostructures of nanohillocks (isolated or partial overlaps) and nanopits (serious overlaps) directly depend on the combined action of the deposited potential and kinetic energies of incident ions, which induce local melting and sublimation in the near-surface region. Owing to the decreased recombination and the increased separation efficiency between electrons and holes, the higher photogenerated charge carrier mobility enhanced the photocurrent effect, further optimizing the photoconductivity performance in Xe 35+ -irradiated KTaO 3 . Therefore, controlled defect engineering using the ion irradiation technique, as an effective strategy, could design tailored nanostructure systems and regulate photoelectric properties, further promoting the development of novel technological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A better way to define dark matter haloes

ABSTRACT Dark matter haloes have long been recognized as one of the fundamental building blocks of large-scale structure formation models. Despite their importance – or perhaps because of it! – halo definitions continue to evolve towards more physically motivated criteria. Here, we propose a new definition that is physically motivated, effectively unique, and parameter-free: ‘A dark matter halo is comprised of the collection of particles orbiting in their own self-generated potential’. This definition is enabled by the fact that, even with as few as ≈300 particles per halo, nearly every particle in the vicinity of a halo can be uniquely classified as either orbiting or infalling based on its dynamical history. For brevity, we refer to haloes selected in this way as physical haloes. We demonstrate that (1) the mass function of physical haloes is Press–Schechter, provided the critical threshold for collapse is allowed to vary slowly with peak height; and (2) the peak-background split prediction of the clustering amplitude of physical haloes is statistically consistent with the simulation data, with accuracy no worse than ≈5 per cent.

Astronomy & Astrophysics↗
NASA NTRS2010

The Characterization of Deep Convective Cloud Albedo as a Calibration Target Using MODIS Reflectances

There are over 25 years of historical satellite data available to climate analysis. The historical satellite data needs to be well calibrated, especially in the visible, where there is no onboard calibration on operational satellites. The key to the vicarious calibration of historical satellites relies on invariant targets, such as the moon, Dome C, and deserts. Deep convective clouds (DCC) also show promise of being a stable invariant or predictable target viewable by all satellites, since they behave as solar diffusers. However DCC have not been well characterized for calibration. Ten years of well-calibrated MODIS is now available. DCC can easily be identified using IR thresholds, where the IR calibration can be traced to the onboard black-bodies. The natural variability of DCC albedo will be analyzed geographically and seasonally, especially difference of convection initiated over land or ocean. Functionality between particle size and ozone absorption with DCC albedo will be examined. Although DCC clouds are nearly Lambertion, the angular distribution of reflectances will be sampled and compared with theoretical models. Both Aqua and Terra MODIS DCC angular models will be compared for consistency. Normalizing angular geostationary DCC reflectances, which were calibrated against MODIS, with SCIAMACHY spectral reflectances and comparing them to MODIS DCC reflectances will inspect the usage of DCC albedos as an absolute calibration target.

Doelling, David R.↗
NASA NTRS2015

A Summary of the Lateral Cutoff Analysis and Results from Nasa's Farfield Investigation of No-Boom Thresholds

In support of the ongoing effort by the National Aeronautics and Space Administration (NASA) to bring supersonic commercial travel to the public, NASA, in partnership with other industry organizations, conducted a flight research experiment to analyze acoustic propagation at the lateral edge of the sonic boom carpet. The name of the effort was the Farfield Investigation of No-boom Thresholds (FaINT). The research from FaINT determined an appropriate metric for sonic boom waveforms in the transition and shadow zones called Perceived Sound Exposure Level, established a value of 65 dB as a limit for the acoustic lateral extent of a sonic boom's noise region, analyzed change in sonic boom levels near lateral cutoff, and compared between real sonic boom measurements and numerical predictions.

acoustics↗
DOE OSTI2022

Characterization of the Superconducting Microwave Properties of Aluminum Manganese

A microwave kinetic inductance detector (MKID) is a superconducting pair breaking detector that offers a number of unique advantages for realizing large-format arrays of ultra-sensitive detectors, such as inherent multiplexibility and relative ease of fabrication. With the detection threshold being set by the Cooper pair binding energy, and correspondingly, the superconducting critical temperature (T c ), typically well-understood MKID materials such as aluminum (Al) present a lower limit on the operating frequency. Aluminum manganese (Al-Mn) is a promising candidate material for MKIDs because it can be fabricated with nearly identical processing as pure Al, but allows for control of the T c , with varying levels of Mn doping or post-deposition heat treatment. In this study, we present initial results from an early characterization of AlMn using a series of lumped-element superconducting microwave resonators, including measurements of T c , internal quality factor, and noise performance over a range of Mn doping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Thickness-Tunable Self-Assembled Colloidal Nanoplatelet Films Enable Ultrathin Optical Gain Media

We propose and demonstrate construction of highly uniform, multilayered superstructures of CdSe/CdZnS core/shell colloidal nanoplatelets (NPLs) using liquid interface self-assembly. These NPLs are sequentially deposited onto a solid substrate into slabs having monolayer-precise thickness across tens of cm(2) areas. Because of near-unity surface coverage and excellent uniformity, amplified spontaneous emission (ASE) is observed from an uncharacteristically thin film having 6 NPL layers, corresponding to a mere 42 nm thickness. Furthermore, systematic studies on optical gain of these NPL superstructures having thicknesses ranging from 6 to 15 layers revealed the gradual reduction in gain threshold with increasing number of layers, along with a continuous spectral shift of the ASE peak (similar to 18 nm). These observations can be explained by the change in the optical mode confinement factor with the NPL waveguide thickness and propagation wavelength. This bottom-up construction technique for thickness-tunable, three-dimensional NPL superstructures can be used for large-area device fabrication.

Erdem, Onur↗
DOE OSTI2021

Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate

The spread in global mean precipitation among climate models is explored in two ensembles using the complementary perspectives of surface evaporation and energy budgets. Models with higher global mean precipitation have stronger oceanic evaporation, driven by drier near-surface air. The drier surface conditions occur alongside increases in near-surface temperature and moisture at 925 hPa, which point to stronger boundary layer mixing. Correlations suggest that the degree of lower tropospheric mixing explains 18%–49% of the intermodel precipitation variance. To test this hypothesis, the degree of mixing is indirectly varied in a single-model experiment by adjusting the relative humidity threshold that controls low-cloud fraction. Indeed, increasing lower tropospheric mixing results in more global mean precipitation. Energetically, increased precipitation rates are associated with more downwelling longwave radiation to the surface and weaker sensible heat fluxes. These results highlight how lower-tropospheric processes must be better constrained to reduce the precipitation discrepancy among climate models.

58 GEOSCIENCES↗
DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗