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At least 523 records · Page 29

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NASA NTRS2022

Ultraviolet Spectropolarimetry: on the Origin of Rapidly Rotating B Stars

UV spectroscopy and spectropolarimetry hold the key to understanding certain aspects of massive stars that are largely inaccessible (or exceptionally difficult) with optical or longer wavelength observations. As we demonstrate, this is especially true for the rapidly-rotating Be and Bn stars, owing to their high temperatures, geometric asymmetries, binary properties, evolutionary history, as well as mass ejection and disks (in the case of Be stars). UV spectropolarimetric observations are extremely sensitive to the photospheric consequences of rapid rotation (i.e. oblateness, temperature, and surface gravity gradients), far beyond the reach of optical wavelengths. Our polarized radiative-transfer modelling predicts that with low-resolution UV spectropolarimetry covering 120–300 nm, and with a reasonable SNR, the inclination angle of a rapid rotator can be determined to within 5 degrees, and the rotation rate to within 1%. The origin of rapid rotation in Be/n stars can be explained by either single-star or binary evolution, but their relative importance is largely unknown. Some Be stars have hot sub-luminous (sdO) companions, which at an earlier phase transferred their envelope (and with it mass and angular momentum) to the present-day rapid rotator. Although sdO stars are small and relatively faint, their flux peaks in the UV making this the optimal observational wavelength regime. Through spectral modelling of a wide range of simulated Be/n+sdO configurations, we demonstrate that high-resolution high-signal-to-noise ratio UV spectroscopy can detect an sdO star even when ∼1,000 times fainter in the UV than its Be/n star companion. This degree of sensitivity is needed to more fully explore the parameter space of Be/n+sdO binaries, which so far has been limited to about a dozen systems with relatively luminous sdO stars. We suggest that a UV spectropolarimetric survey of Be/n stars is the next step forward in understanding this population. Such a dataset would, when combined with population synthesis models, allow for the determination of the relative importance of the possible evolutionary pathways traversed by these stars, which is also crucial for understanding their future evolution and fate.

Ultraviolet astronomy↗

DOE OSTI2014

Novel QCD Phenomena at the LHC: The Ridge, Digluon-Initiated Subprocesses, Direct Reactions, Non-Universal Antishadowing, and Forward Higgs Production

I discuss a number of novel tests of QCD at the LHC, measurements which can illuminate fundamental features of hadron physics. I also review the “Principle of Maximum Conformality” (PMC) which systematically sets the renormalization scale order-by-order in pQCD, eliminating an unnecessary theoretical uncertainty. The PMC allows LHC experiments to test QCD much more precisely, and the sensitivity of LHC measurements to physics beyond the Standard Model is increased.

Phenomenology-HEP,HEPPH↗

DOE OSTI2015

Midplane neutral density profiles in the National Spherical Torus Experiment

Atomic and molecular density data in the outer midplane of NSTX [Ono et al., Nucl. Fusion 40, 557 (2000)] are inferred from tangential camera data via a forward modeling procedure using the DEGAS 2 Monte Carlo neutral transport code. The observed Balmer-β light emission data from 17 shots during the 2010 NSTX campaign display no obvious trends with discharge parameters such as the divertor Balmer-α emission level or edge deuterium ion density. Simulations of 12 time slices in 7 of these discharges produce molecular densities near the vacuum vessel wall of 2–8 × 10{sup 17 }m{sup −3} and atomic densities ranging from 1 to 7 × 10{sup 16 }m{sup −3}; neither has a clear correlation with other parameters. Validation of the technique, begun in an earlier publication, is continued with an assessment of the sensitivity of the simulated camera image and neutral densities to uncertainties in the data input to the model. The simulated camera image is sensitive to the plasma profiles and virtually nothing else. The neutral densities at the vessel wall depend most strongly on the spatial distribution of the source; simulations with a localized neutral source yield densities within a factor of two of the baseline, uniform source, case. The uncertainties in the neutral densities associated with other model inputs and assumptions are ≤50%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1983

Helicopter rotor wake geometry and its influence in forward flight. Volume 1: Generalized wake geometry and wake effect on rotor airloads and performance

An analytic investigation to generalize wake geometry of a helicopter rotor in steady level forward flight and to demonstrate the influence of wake deformation in the prediction of rotor airloads and performance is described. Volume 1 presents a first level generalized wake model based on theoretically predicted tip vortex geometries for a selected representative blade design. The tip vortex distortions are generalized in equation form as displacements from the classical undistorted tip vortex geometry in terms of vortex age, blade azimuth, rotor advance ratio, thrust coefficient, and number of blades. These equations were programmed to provide distorted wake coordinates at very low cost for use in rotor airflow and airloads prediction analyses. The sensitivity of predicted rotor airloads, performance, and blade bending moments to the modeling of the tip vortex distortion are demonstrated for low to moderately high advance ratios for a representative rotor and the H-34 rotor. Comparisons with H-34 rotor test data demonstrate the effects of the classical, predicted distorted, and the newly developed generalized wake models on airloads and blade bending moments. Use of distorted wake models results in the occurrence of numerous blade-vortex interactions on the forward and lateral sides of the rotor disk. The significance of these interactions is related to the number and degree of proximity to the blades of the tip vortices. The correlation obtained with the distorted wake models (generalized and predicted) is encouraging.

Egolf, T. A.↗

DOE OSTI2013

A measurement of the energy and timing resolution of GlueX Forward Calorimeter using an electron beam

The performance of the GlueX Forward Calorimeter was studied using a small version of the detector and a variable energy electron beam derived from the Hall B tagger at Jefferson Lab. For electron energies from 110 MeV to 260 MeV, which are near the lower-limits of the design sensitivity, the fractional energy resolution was measured to range from 20% to 14%, which meets the design goals. The use of custom 250 MHz flash ADCs for readout allowed precise measurements of signal arrival times. The detector achieved timing resolutions of 0.38 ns for a single 100 mV pulse, which will allow timing discrimination of photon beam bunches and out-of-time background during the operation of the GlueX detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Non-classical crystallization in soft and organic materials

Classical nucleation and crystal growth theories describe how nuclei form, become stable after reaching a critical size and then enlarge through monomer attachment. More than two decades ago, non-classical pathways have been proposed for various types of (bio)molecules and materials, which can substantially alter the crystallization kinetics and outcomes. Direct observation of non-classical crystallization of inorganic nanomaterials, including metastable structure-mediated and particle attachment-based pathways that usually occur on the nanoscale, was enabled by in situ liquid-phase electron microscopy. However, it was not until recently that the crystallization dynamics of beam-sensitive soft materials were directly imaged with sufficient spatial resolution, and a level of microstructural understanding of defects and interfaces emerged. This article provides a high-level review of the non-classical crystallization pathways discovered in soft and organic materials and a forward-looking guide for future research. We first analyse how the characteristics of soft materials affect their crystallization pathways and kinetics. We then identify technical approaches to studying the crystallization trajectories of soft materials and discuss strategies to properly select and apply them to different systems. Breakthroughs made in understanding the crystallization of small organic molecules, (bio)macromolecules, colloids and reticular framework materials are examined. Lastly, we provide an outlook on the challenges in elucidating soft material crystallization pathways and the opportunities for assisting the design and synthesis of new materials and structures.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Full forward model of galaxy clustering statistics with AbacusSummit light cones

ABSTRACT Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r < 30h−1Mpc) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modelling pipeline for a wide range of summary statistics using the large high-fidelity AbacusSummit light cones that account for many systematic effects as well as remain flexible and computationally efficient to enable posterior sampling. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy–halo connection model using two separate summary statistics: the standard 2PCF and the novel k-th nearest neighbour (kNN) statistics, which are sensitive to correlation functions of all orders. We will demonstrate its strong constraining power on extended galaxy–halo connection models and cosmology in follow up papers. We expect this to become a powerful approach when applying to upcoming surveys such as DESI where we can leverage a multitude of summary statistics across a wide redshift range to maximally extract information from the non-linear scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗

DOE OSTI2025

A Privacy First Path Analysis using Clickstream Data

In the modern digital economy, data-driven decision making is crucial for effectively meeting the ever-evolving demands of consumer engagement and satisfaction. Clickstream data has become invaluable for understanding customer behavior, yet concerns over privacy and security persist, especially with some internet service providers profiting from its sale. This article introduces an innovative methodology that blends experiential learning with advanced cryptographic techniques, including differential privacy and graph analytics. The core objective of this methodology is to estimate Customer Lifetime Value (CLV) by analyzing clickstream data, achieving an average prediction accuracy of 92.4% in user engagement levels while ensuring user anonymity through Recency, Frequency, and Monetary (RFM) analysis. Our study introduces the concept of a “data depositor” and a privacy manager, employing the composition theorem to merge non-adaptive queries effectively. Privacy budgets (? = 1.0, d = 10-5), sensitivity-specific techniques, and data partitioning were applied. Randomization and noise addition protect data integrity, with special handling for categorical values. This approach, differing from prior studies, offers a 12.6% improvement in privacy-preserving targeting accuracy while maintaining strict confidentiality, presenting a novel path forward in data-driven decision-making.

Frequency and Monetary (RFM) analysis↗

DOE OSTI2018

Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment

Here, an understanding of the relative stability of surface facets is crucial to develop predictive models of catalyst activity and to fabricate catalysts with a controlled morphology. In this work, we present a systematic density functional theory study of the effect of lattice strain and CO environment on the surface formation energies of Cu, Pt, and Ni. First, we show that both compressive and tensile lattice strains favor the formation of stepped versus low-index terraces such as (111) and (100). Then, we investigate the effect of the CO environment using configurations of CO at various coverages, determined using a greedy, systematic approach, inspired by forward stepwise feature selection. We find that the CO environment favors stepped facets on Ni, Cu, and Pt. These trends are illustrated with the corresponding equilibrium Wulff shapes at various strains and CO pressures. In general, the surface energies of the studied transition metals are highly sensitive to strain and CO coverage, which should be considered when rationalizing trends in the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Effect of growth temperature on ballistic electron transport through the Au/Si(001) interface

Ballistic electron emission spectroscopy was used to investigate electron transport through Au/Si(001) Schottky diodes grown at 35 °C and 22 °C. Aside from a decreased Schottky height, this small increase in temperature introduced an energy dependent scattering component, which was absent in the samples grown at 22 °C. These differences may be attributed to the increased amount of Au-Si intermixing at the interface. Despite the non-epitaxial nature of the growth technique, strong evidence was found in both sets of samples that indicated the presence of a forward-focused current subject to some degree of parallel momentum conservation at the interface. This evidence was present in all samples grown at 35 °C, but was only observed in those samples grown at 22 °C when the Au films were 10 nm or thicker. This sensitivity to growth temperature could account for discrepancies in previous studies on Au/Si(001)

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Machine learning $\mathbb{Z}_2$ quantum spin liquids with quasiparticle statistics

After decades of progress and effort, obtaining a phase diagram for a strongly correlated topological system still remains a challenge. Although in principle one could turn to Wilson loops and long-range entanglement, evaluating these nonlocal observables at many points in phase space can be prohibitively costly. With growing excitement over topological quantum computation comes the need for an efficient approach for obtaining topological phase diagrams. In this report we turn to machine learning using quantum loop topography (QLT), a notion we have recently introduced. Specifically, we propose a construction of QLT that is sensitive to quasiparticle statistics. We then use mutual statistics between the spinons and visons to detect a $\mathbb{Z}_2$ quantum spin liquid in a multiparameter phase space. We successfully obtain the quantum phase boundary between the topological and trivial phases using a simple feed-forward neural network. Furthermore, we demonstrate advantages of our approach for the evaluation of phase diagrams relating to speed and storage. Such statistics-based machine learning of topological phases opens new efficient routes to studying topological phase diagrams in strongly correlated systems.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Bayesian characterization and forward propagation of the uncertainty in thermodynamic models for redox active materials

Demonstrating the thermodynamic efficiency of hydrogen conversion processes using various materials is a critical step in developing new technologies for storing concentrated solar energy, and is largely accomplished by using a thermodynamic model derived from experimental data. A main goal of this project is to calculate the uncertainty of the thermodynamic efficiency by calculating the uncertainty of the components that feed into the efficiency. Many different models and data sets were used to test the workflow. First, the models were fit to the data using a Bayesian Inference and a method called Markov Chain Monte Carlo (MCMC), which found the maximum a priori parameters, and a posterior probability distribution of the parameters. Next, the different models were compared to each other using model evidence values. It was found that for cleaner data sets, overfitting had not yet been reached, and the most complicated model was ideal, but on the noisier data sets, the less complex models were favored because the more complicated models resulted in overfitting. Next, forward propagation was used to calculate the enthalpy change and its associated uncertainty. A few variations on the models were tried, such as fitting in a different variable, producing negligible or negative effects on the fits of the models. Thus, the original models were used. A sensitivity analysis was performed, and used to calculate the model error. On the cleaner data sets, there was very minimal experimental noise, and thus, all resulting error was from the model. With consideration of the model error, the models fit the data very well, and the simpler model had a high model error, as expected. All these components will then be used to calculate the thermodynamic efficiency of the different materials.

14 SOLAR ENERGY↗

NASA NTRS1993

Exploiting orbital effects for short-range extravehicular transfers

The problem studied in this paper is that of using Simplified Aid for Extravehicular Activity (EVA) Rescue (SAFER) to carry out efficient short-range transfers from the payload bay of the Space Shuttle Orbiter to the vicinity of the underside of the vehicle, for instance for inspection and repair of thermal tiles or umbilical doors. Trajectories are shown to exist, for the shuttle flying noise forward and belly down, that take the astronaut to the vicinity of the underside with no thrusting after the initial push-off. However, these trajectories are too slow to be of practical interest, as they take roughly an hour to execute. Additionally, they are quite sensitive to errors in the initial push-off rates. To overcome both of these difficulties, trajectories are then studied which include a single in-flight impulse of small magnitude ( in the range 0.1 - 0.4 fps). For operational simplicity, this impulse is applied towards the Orbiter at the moment when the line-of -sight of the EVA crewmember is tangential to the underside of the vehicle. These trajectories are considerably faster than the non-impulsive ones: transit times of less than 10 minutes are achievable. Furthermore, the man-in-the-loop feedback scheme used for impulse timing greatly reduces the sensitivity to initial velocity errors. Finally, similar one-impulse trajectories are also shown to exist for the Orbiter in a gravity-gradient attitiude.

Williams, Trevor↗

NASA NTRSDate not supplied

Opposed Flame Spreading Along a Structured PMMA Sample in Exploration Atmosphere Under Microgravity

As part of SAFFIRE V experiments on the ISS supply spacecraft CYGNUS, flame propagation along different surface structures on a PMMA sample was investigated in opposed flow. The sample was 200 x 400 mm (length x width) with a thickness of 10 mm and contained ribs of different widths (1-10 mm), each 3 mm high, on both sides, arranged in the flow direction. The total thickness of the specimen was thus 16 mm for the ribs. For the first 360 s after ignition with a Kanthal wire, the flow velocity was 20 cm/s in the opposed direction. Then the flow velocity was reduced to 5 cm/s for a duration of 300 s before it was switched off to terminate the experiment. The experimental pressure was 761 hPa and the oxygen concentration was 26.9 vol %. These conditions correspond to atmospheric conditions envisioned for future exploration missions. It was found that the flames ignited downstream propagated forward along the rib edges surprisingly fast with up to 0.88 mm/s (1 mm rib width). Also, it was observed that the wider the rib, the slower the propagation. The widest rib (10 mm) already showed two largely independent edge flames. Unintentionally, the experiment showed how sensitive the flames are to small perturbations of the flow field, as remnants of a preceding and upstream experiment disturbed the incoming flow stratification partially.

microgravity↗

NASA NTRSDate not supplied

Opposed flame spreading along a structured PMMA-sample in exploration atmosphere under microgravity

fire safety↗

NASA NTRS2020

Implementation of Multidomain Unified Forward Operators (UFO) Within the Joint Effort for Data Assimilation Integration (JEDI): Ocean Applications

The Joint Effort for Data assimilation Integration (JEDI) is a collaborative development led by the Joint Center for Satellite Data Assimilation (JCSDA) in conjunction with NASA, NOAA and the Department of Defense (NAVY and Air Force). The (Sea-Ice Ocean and Coupled Assimilation) SOCA as one of the JCSDA projects, focuses on the application of JEDI to marine data assimilation. One of the goals of SOCA is to make use of surface-sensitive radiances to constrain sea-ice and upper ocean fields (e.g., salinity, temperature, sea-ice fraction, sea-ice temperature, etc.). The first elements toward an ocean/atmosphere coupled data assimilation capability within JEDI, with a focus on supporting and developing the assimilation of radiance observations sensitive to the ocean and atmosphere has been implemented. The direct radiance assimilation of surface sensitive microwave radiances focusing on Global Precipitation Measurement (GPM) Imager (GMI) for the SST Constraint and Soil Moisture Active Passive (SMAP) for the Sea Surface Salinity (SSS) has been the main focus. Also, in UFO the capability to calculate the cool skin layer depth and skin temperature has been implemented similar to the GEOS-5. It has been tested with GMI sea surface temperature retrievals. This is important because Satellite and in-situ observations of the Sea-Surface Temperature (SST) show high variability, including a diurnal cycle and very thin, cool skin layer in contact with the atmosphere, and Incorporating a realistic skin SST is essential for atmosphere-ocean coupled data assimilation.

Unified Forward Operators (UFO)↗