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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA/GEWEX SRB Surface Radiation Budget Fluxes Through the Prism of Weather States

The NASA/GEWEX Surface Radiation Budget Version 4 (SRB Rel4) product is now publicly available, covering the period from July 1983 through June 2017. This supersedes the SRB Rel3 (Stackhouse et al., 2011) product which has been used in the community for a wide variety of applications, including climate model validation, agriculture, solar energy, and architecture. SRB Rel4 uses the newly recalibrated and processed ISCCP HXS product as its primary input for cloud and radiance data, replacing ISCCP DX with a ninefold increase in pixel count (10km instead of 30km). This version retains a 1°x1° resolution but benefits from a much larger number of samples per grid box than the SRB Rel3. ISCCP also provides an atmospheric temperature and moisture dataset known as nnHIRS which we use here, along with Seaflux and Landflux surface and near-surface meteorological parameters. Rel4 incorporates several important algorithm improvements. These include recalculated shortwave (SW) atmospheric transmissivities and reflectivities yielding a somewhat less transmissive atmosphere. Both shortwave and longwave (LW) now also include variable aerosol composition and radiative properties, allowing for the use of a detailed aerosol history from the Max Planck Institute Aerosol Climatology (MACv1). LW and SW algorithms now produce pristine sky fluxes, allowing the aerosol flux effects to be quantified. For SW, ocean albedo and snow/ice albedo are improved from Release 3. Total solar irradiance is now variable, and reduced to an average of 1361 Wm-2. The radiative treatment of ice cloud is improved. For the LW, a climatological monthly varying spectral surface emissivity is added. Here we evaluate the SRB Rel4 top of atmosphere (TOA) and surface fluxes in the context of weather states and oceanic regions. Weather states (Tselioudis et al., 2013, Tselioudis et al., 2021) are defined by K-means clustering 2-D histograms of satellite-retrieved cloud optical depths and cloud top pressures. The most recent (Tselioudis et al., 2021) weather states derived from the International Satellite Cloud Climatology Project (ISCCP) H-series data results in eight cloud weather states and one clear sky weather state, for a total of nine weather states. We examine the statistics of SRB Rel4 fluxes for each weather state and a variety of relevant land and ocean regions. We compare to similar analyses performed on CERES EBAF and SYN1Deg flux products, and validate against Baseline Surface Radiometer Network (BSRN) land stations and Pacific Marine Environmental Laboratory (PMEL) ocean buoy data. It is demonstrated that the weather state concept is valuable when examining the comparative strengths and weaknesses of satellite radiative flux algorithms.

Atmospheric Radiation↗
DOE OSTI2020

Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure

Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. Quality volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume on freeways are typically collected through continuous count stations installed by state DOTs, while there is lack of traffic volume observability on off-freeway roads. The National Renewable Energy Laboratory (NREL), in Collaboration with the I-95 Corridor Coalition and the University of Maryland, extended its research into estimating volumes anywhere anytime from industry probe based data for off-freeway roads. NREL combined vehicle probe count data with several other data sets (speed, whether, roadway geometry, time-of-day, day-of-week, etc.) to estimate hourly volumes as well as AADTs. The research validated and demonstrated the machine learning model, namely XGBoost, using data collected from Pennsylvania, North Carolina, and Tennessee.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.

anode↗
NASA NTRS2017

A Comparison of the SOCIT and DebriSat Experiments

This paper explores the differences between, and shares the lessons learned from, two hypervelocity impact experiments critical to the update of Department of Defense (DOD) and National Aeronautics and Space Administration (NASA) satellite breakup models. The procedures as well as the processes of the fourth Satellite Orbital Debris Characterization Impact Test (SOCIT4) were analyzed and related to the ongoing DebriSat experiment. SOCIT4 accounted for about 90% of the entire satellite mass, but only analyzed approximately 59% with a total of approximately 4,700 fragments. DebriSat aims to recover and analyze 90% of the initial mass and to do so, fragments with at least a longest dimension of 2 mm are collected and processed. DebriSat's use of modern materials, especially carbon fiber, significantly increases the fragment count and to date, there are over 126,000 fragments collected. Challenges, such as procedures and human inputs, encountered throughout the DebriSat experiment are also shared. While, SOCIT4 laid the foundation for the majority of DebriSat processes, the technological advancements since SOCIT4 allow for more accurate, rigorous, and in-depth, procedures that will aid the update of satellite breakup models.

Ausay, E.↗
DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗
DOE OSTI2026

Operationally induced preferred basis in unitary quantum mechanics

The preferred-basis problem and the definite-outcome aspect of the measurement problem persist even if the detector is modeled unitarily, because experimental data are necessarily represented in a Boolean event algebra of mutually exclusive records whereas the theoretical description is naturally formulated in a noncommutative operator algebra with continuous unitary symmetry. This change of mathematical type constitutes the core of the 'cut': a structurally necessary interface from group-based kinematics to set-based counting. In the presented view the basis relevant for recorded outcomes is not determined by the system Hamiltonian alone; it is induced by the measurement mapping, i.e., by the detector channel together with the coarse-grained readout that defines an instrument. The probabilistic mapping is anchored in symmetry and measure theory: by Gleason-type uniqueness (Gleason for projections in $d>2$ and Busch's extension for Positive Operator-Valued Measures (POVMs) including $d=2$), the trace rule is the unique probability measure consistent with additivity over exclusive events and basis-independence of the unitary sector. A compact qubit--pointer model yields an induced unsharp POVM $E_\pm=\tfrac12(\id\pm η\,σ_z)$ with $η$ fixed by pointer resolution, displaying explicitly how the detector induces the relevant basis. Finally, nested-observer paradoxes are tightened into a non-composability lemma: joint assignment of outcome propositions is obstructed unless a joint instrument exists. This relocates the origin of randomness to the stochasticity of the transition rules.

Pronskikh, Vitaly [Fermilab] (ORCID:00000002518174↗
NASA NTRS2012

Color Choice is Everything - Impacts Color makes to the Lighting Environment

When contracts are let out to design multiple systems in a vehicle, it is a challenge to maintain integration between system leads. Designers on niche systems, like lighting and control panel design, often get caught up in the challenge of designing the light source or visual interface and fail to include time in their schedule to work with system architects on how their lighting system will be integrated. Additionally, behavioral scientists, industrial designers, and materials engineers get caught up with the materials and look of the system, but often fail to consider how the selection of their materials could affect the certification or performance of electronic devices like lighting systems. Additionally, computer modeling of the system architecture often assumes a perfect environment without the clutter of actual human use (dirt, stowage, crowding). As a result, lighting systems, and backlit displays run the risk of being overdesigned or under designed. Engineers making the assumption that because they have no input or there is no requirement on work surface reflectance, make the assumption that they can t count on good material choices and thus may install more lighting than is necessary. While having more lights may seem better, for a vehicle that is trying to conserve power, more lights may not be a good option. On the other hand, designers who made the opposite assumption and designed a lighting system that only produced just enough light, often wind up with a system that did conserve power, but didn t produce enough light. These situations are exasperated when the system starts to be used and the models are not perfect anymore. The lack of coordination and iterative design not only can impact lighting levels within an environment, but also can affect color perception. This is because, if materials do not represent a gradation of white or black, the material unevenly absorbs and reflects light at different wavelengths of the visual spectrum. The lighting designer may have built a light that meets light spectra requirements, but the eventual light reaching the human user may not be the spectra of light architects intended, if materials near the light source change the spectrum just by how much color is absorbed or reflected. With the recent findings concerning Circadian rhythm, where the spectra of light is extremely important for addressing crew sleep and wake cycles, system architects should pay considerable attention on the impact material choices have in changing the light spectrum in an environment. This presentation will show examples of how material choices impact the resulting illuminance, color spectrum, and power usage of an illuminated space. Its goal is to encourage system designers and planners to use more care in development of requirements and the verification of systems intended for the human visual interface.

Clark, Toni A.↗
DOE OSTI2022

Measuring binary fluidization of nonspherical and spherical particles using machine learning aided image processing

Abstract The binary fluidization of Geldart D type nonspherical wood particles and spherical low density polyethylene (LDPE) particles was investigated in a laboratory‐scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using particle image velocimetry (PIV). The wood particles orientation and velocity are measured using particle tracking velocimetry (PTV). A machine learning pixel‐wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.

Li, Cheng↗
DOE OSTI2024

Elucidating the Role of Dimensionality on the Electronic Structure of the Van der Waals Antiferromagnet NiPS 3

Abstract The sustained interest in investigating magnetism in the 2D limit of insulating antiferromagnets is driven by the possibilities of discovering, or engineering, novel magnetic phases through layer stacking. However, due to the difficulty of directly measuring magnetic interactions in 2D antiferromagnets, it is not yet understood how intra layer magnetic interactions in insulating , strongly correlated, materials can be modified through layer proximity. Herein, the impact of reduced dimensionality in the model van der Waals antiferromagnet NiPS 3 is explored by measuring electronic excitations in exfoliated samples using Resonant Inelastic X‐ray Scattering (RIXS). The resulting spectra shows systematic broadening of NiS 6 multiplet excitations with decreasing layer count from bulk down to three atomic layers (3L). It is shown that these trends originate from a decrease in transition metal‐ligand and ligand–ligand hopping integrals, and by charge‐transfer energy evolving from Δ = 0.83 eV in the bulk to 0.37 eV in 3L NiPS 3 . Relevant intralayer magnetic exchange integrals computed from the electronic parameters exhibit a decrease in the average interaction strength with thickness. This study underscores the influence of inter layer electronic interactions on intra layer ones in insulating magnets, indicating that magnetic Hamiltonians in few‐layer insulating magnets can greatly deviate from their bulk counterparts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Altered morphology and diffusivity of water confined in MXenes: Machine learning–accelerated computations combined with experiments

Nanoconfined water exhibits unique properties compared to bulk water due to limited quantities, frustrated hydrogen bonding, and surface interactions, which are fundamental for energy storage and transport applications. We integrate machine learning–accelerated ab initio molecular dynamics with x-ray diffraction (XRD) and inelastic neutron scattering (INS) to systematically analyze the thermodynamic and dynamic behavior of water confined between functionalized (-F, -O, and -OH) two-dimensional (2D) Ti 3 C 2 T x MXene layers. As water intercalates between layers, the interlayer spacing exhibits layer-dependent staging characteristics. The water polarization can be flipped by the count and morphology of intercalated molecules interacting with MXene surface groups, resulting in varying electrostatic potential profiles. On the basis of interfacial electrostatic potential, hydrogen bond lifetime, and molecular orientation, we establish a linear combination of exponential model describing water diffusivity. These computational insights align well with experimental x-ray and neutron measurements, suggesting strategies for tuning water morphology and transport by tailoring MXene surface chemistry and water content for electrochemical energy storage and nanofluidic applications.

Tang, Jiawei [Southeast Univ., Nanjing (China)] (O↗
DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Shot-noise-induced lower temperature limit of the nonneutral plasma parallel temperature diagnostic

Abstract We develop a new algorithm to estimate the temperature of a nonneutral plasma in a Penning-Malmberg trap. The algorithm analyzes data obtained by slowly lowering a voltage that confines one end of the plasma and collecting escaping charges, and is a maximum likelihood estimator based on a physically-motivated model of the escape protocol presented in (Beck in Measurement of the magnetic and temperature dependence of the electron-electron anisotropic temperature relaxation rate. PhD thesis, 1990). Significantly, our algorithm may be used on single-count data, allowing for improved fits with low numbers of escaping electrons. This is important for low-temperature plasmas such as those used in antihydrogen trapping. We perform a Monte Carlo simulation of our algorithm, and assess its robustness to intrinsic shot noise and external noise. The assumptions in this paper allow for a lower bound for measurable plasma temperatures of approximately $3\,\mathrm{K}$ 3 K for plasmas of length $1\,\mathrm{cm}$ 1 cm , with approximately 100 particle counts needed for an accuracy of $\pm 10 \%$ ± 10 % .

Zhong, Adrianne (ORCID:0000000162618736)↗
DOE OSTI2026

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Solving Inverse Radiation Transport Problems with Multi-Sensor Data in the Presence of Correlated Measurement and Modeling Errors

Inverse radiation transport focuses on identifying the configuration of an unknown radiation source given its observed radiation signatures. The inverse problem is traditionally solved by finding the set of transport model parameter values that minimizes a weighted sum of the squared differences by channel between the observed signature and the signature predicted by the hypothesized model parameters. The weights are inversely proportional to the sum of the variances of the measurement and model errors at a given channel. The traditional implicit (often inaccurate) assumption is that the errors (differences between the modeled and observed radiation signatures) are independent across channels. Here, an alternative method that accounts for correlated errors between channels is described and illustrated using an inverse problem based on the combination of gamma and neutron multiplicity counting measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

Limits on the density of neutral gas within 100 parsecs from observations of the soft X-ray background

Observations are presented in two soft X-ray bands, the Be band (0.077-0.111 keV) and the B-prime (0.105-0.188 keV), for nine directions in the sky. The ratio of count rates in these two bands remains constant as the rates vary by a factor of three, even though the effective interstellar absorption cross sections in the bands differ by a factor of about 3.5. For a model in which the bulk of the observed soft X-ray emission originates in a uniform low-density region surrounding the sun, the constant ratio between the band rates places an upper limit on the amount of neutral material that can be homogeneously mixed with the X-ray-emitting gas. The 2 sigma upper limit on the H I column density over an average path through the local emitting region is 6.6 x 10 to the 18th/sq cm. If the average path length is about 100 pc, then clouds similar to the one in which the sun is embedded could still have a filling factor as large as 25 percent.

Juda, M.↗
NASA NTRS1993

The Hubble Space Telescope Snapshot Survey. III - Further observations in search of gravitationally lensed quasars

We present new HST Snapshot Survey observations of 163 high-luminosity z greater than 1 quasars, bringing the total number observed to date in the survey to 347 quasars. Each exposure was searched for evidence on gravitational lensing. Among the newly observed quasars, six have additional point sources at separations of 2-6 arcsec, but in all six cases ground-based data indicate that gravitational lensing is not involved. No point sources were found at smaller separations. Counting the one new lens candidate the survey has uncovered and the known lenses excluded from the sample, the observed frequency of lensing of quasars by galaxies is 5/351. This frequency is in the range predicted by models with a vanishing cosmological constant, but an order of magnitude lower than expected in models strongly dominated by a cosmological constant.

Maoz, D.↗
NASA NTRS1991

Phase space distribution of halo particles and detection of WIMPS

We calculate the velocity distribution of halo particles using realistic models for the density profiles of the Galactic disk, spheroid, and halo components. We get the resulting velocity distribution, which is different from the Maxwellian distribution of isothermal halo. We investigate the consequences concerning the time averaged count rate and the amplitude of the annual modulation in WIMPS detection experiments. Compared to the previous estimate based on the Maxwellian distribution, the time averaged count rate is up to 40 percent larger. On the other hand, the amplitude of the annual modulation is up to a factor of two larger.

Ryu, Dongsu↗