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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Physics for neuromorphic computing

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. Here, we discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.

device physics↗
DOE OSTI2025

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

“Soft” muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb -1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements

Monitoring cutting forces for process control may be challenging because force measurements typically require invasive instrumentation. To remedy this situation, two new methods were recently developed to estimate cutting forces in real time based on the use of on-machine accelerometer measurements. One method uses machine learning, while another uses a physics-inspired data-driven approach, to generate a model that estimates cutting forces from on-machine accelerations. The estimated forces from both approaches were compared against cutting force data collected during various milling operations on several machine tools. The results reveal the advantages and disadvantages of each model to estimate real-time cutting forces.

Vogl, Greg↗
DOE OSTI2026

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]↗
NASA NTRS1994

Theoretical studies of spectroscopic problems of importance for atmospheric radiation measurements

Many of the instruments used to deduce the physical parameters of the Earth's atmosphere necessary for climate studies or for pollution monitoring (for instance, temperature versus pressure or number densities of trace molecules) rely on the existence of accurate spectroscopic data and an understanding of the physical processes responsible for the absorption or emission of radiation. During the summer, research was either continued or begun on three distinct problems: (1) an improved theoretical framework for the calculation of the far-wing absorption of allowed spectral lines; (2) a refinement of the calculation of the collision-induced fundamental spectrum of N2; and (3) an investigation of possible line-mixing effects in the fundamental spectrum of CH4. Progress in these three areas is summarized below. During the past few years, we have developed a theoretical framework for the calculation of the absorption of radiation by the far wings of spectral lines. Such absorption due to water vapor plays a crucial role in the greenhouse effect as well as limiting the retrieval of temperature profiles from satellite data. Several improvements in the theory have been made and the results are being prepared for publication. Last year we published results for the theoretical calculation of the absorption of radiation due to the dipoles induced during binary collisions of N2 molecules using independently measured molecular parameters; the results were in reasonable agreement with experimental data. However, recent measurements have revealed new fine structure that has been attributed to line-mixing effects. We do not think that this is correct, rather that the structure results from short-range anisotropic dipoles. We are in the process of including this refinement in our theoretical calculation in order to compare with the new experimental data. Subtle changes in the spectra of CH4 measured by researchers at Langley have also been attributed to line-mixing effects. By analyzing the same spectral lines we have attempted to verify or rule out possible line-mixing mechanisms. Due to the complexity and richness of the spectrum of this highly symmetric molecule, as well as the small magnitude of the effects, a detailed first-principle calculation of the mixing is a difficult problem. Before such a program is undertaken it is important to glean as much information as possible concerning the possible mechanisms by a systematic analysis of the existing data.

Tipping, Richard H.↗
NASA NTRS1987

Physical processes and infrared emission from the Cassiopeia A supernova remnant

IRAS 12-100 micron data on the Cas A remnant are presented, and various physical mechanisms and astrophysical sites that may contribute to the observed infrared emission are analyzed. The contributions of various sources of infrared emission to the IRAS fluxes are found to be small. The residual infrared emission is attributed to thermal emission from dust which is swept up by the expanding supernova blast wave and collisionally heated by the postshock X-ray emitting gas. The calculations are consistent with a shock velocity of 1800 km/s and a preshock gas density of about 2/cu cm. The mass of the swept-up gas is about 0.6 solar mass. An excess of 12 micron thermal emission in the spectrum of Cas A suggests the presence of very small particles in the preshocked gas.

Dwek, E.↗
NASA NTRS2001

Predictability of the California Current System

The physical and biological oceanography of the Southern California Bight (SCB), a highly productive subregion of the California Current System (CCS) that extends from Point Conception, California, south to Ensenada, Mexico, continues to be extensively studied. For example, the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program has sampled this region for over 50 years, providing an unparalleled time series of physical and biological data. However, our understanding of what physical processes control the large-scale and mesoscale variations in these properties is incomplete. In particular, the non-synoptic and relatively coarse spatial sampling (70km) of the hydrographic grid does not completely resolve the mesoscale eddy field (Figure 1a). Moreover, these unresolved physical variations exert a dominant influence on the evolution of the ecosystem. In recent years, additional datasets that partially sample the SCB have become available. Acoustic Doppler Current Profiler (ADCP) measurements, which now sample upper-ocean velocity between stations, and sea level observations along TOPEX tracks give a more complete picture of the mesoscale variability. However, both TOPEX and ADCP are well-sampled only along the cruise or orbit tracks and coarsely sampled in time and between tracks. Surface Lagrangian drifters also sample the region, although irregularly in time and space. SeaWiFS provides estimates of upper-ocean chlorophyll-a (chl-alpha), usually giving nearly complete coverage for week-long intervals, depending on cloud coverage. Historical ocean color data from the Coastal Zone Color Scanner (CZCS) has been used extensively to determine phytoplankton patterns and variability, characterize the primary production across the SCB coastal fronts, and describe the seasonal and interannual variability in pigment concentrations. As in CalCOFI, these studies described much of the observed structures and their variability over relatively large space and time scales.

Miller, Arthur J.↗
NASA NTRS2018

Seeing Double: ASASSN-18bt Exhibits a Two-component Rise in the Early-time K2 Light Curve

On 2018 February 4.41, the All-Sky Automated Survey for SuperNovae (ASAS-SN) discovered ASASSN-18bt in the K2 Campaign 16 field. With a redshift of z=0.01098 and a peak apparent magnitude of B(max)=14.31, ASASSN-18bt is the nearest and brightest SNe Ia yet observed by the Kepler spacecraft. Here we present the discovery of ASASSN-18bt, the K2 light curve, and prediscovery data from ASAS-SN and the Asteroid Terrestrial-impact Last Alert System. The K2 early-time light curve has an unprecedented 30-minute cadence and photometric precision for an SN Ia light curve, and it unambiguously shows a ∼4 day nearly linear phase followed by a steeper rise. Thus, ASASSN-18bt joins a growing list of SNe Ia whose early light curves are not well described by a single power law. We show that a double-power-law model fits the data reasonably well, hinting that two physical processes must be responsible for the observed rise. However, we find that current models of the interaction with a nondegenerate companion predict an abrupt rise and cannot adequately explain the initial, slower linear phase. Instead, we find that existing published models with shallow 56Ni are able to span the observed behavior and, with tuning, may be able to reproduce the ASASSN-18bt light curve. Regardless, more theoretical work is needed to satisfactorily model this and other early-time SNe Ia light curves. Finally, we use Swift X-ray nondetections to constrain the presence of circumstellar material (CSM) at much larger distances and lower densities than possible with the optical light curve. For a constant-density CSM, these nondetections constrain ρ<4.5×10(exp 5)per cu.cm at a radius of 4×10(exp 15) cm from the progenitor star. Assuming a wind-like environment, we place mass loss limits of M˙ < 8 x 10(exp -6) M(ʘ)per yr for v(w)=100 km/s, ruling out some symbiotic progenitor systems. This work highlights the power of well-sampled early-time data and the need for immediate multiband, high-cadence follow-up for progress in understanding SNe Ia.

B. J. Shappee↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
DOE OSTI2025

High-count rate effects in event processing for XRISM/ Resolve X-ray microcalorimeter: I. Ground test

The spectroscopic performance of an X-ray microcalorimeter is compromised at high count rates. We utilize the Resolve X-ray microcalorimeter onboard the XRISM satellite to examine the effects observed during high-count rate measurements and propose modeling approaches to mitigate them. We specifically address the following instrumental effects that impact performance: CPU limit, pile-up, and untriggered electrical cross-talk. Experimental data at high count rates were acquired during ground testing using the flight model instrument and a calibration X-ray source. In the experiment, data processing not limited by the performance of the onboard CPU was run in parallel, which cannot be done in orbit. This makes it possible to access the data degradation caused by limited CPU performance. We use these data to develop models that allow for a more accurate estimation of the aforementioned effects. To illustrate the application of these models in observation planning, we present a simulated observation of GX 13+1. Understanding and addressing these issues is crucial to enhancing the reliability and precision of X-ray spectroscopy in situations characterized by elevated count rates.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle A into two photons, A → γ γ , is chosen as a benchmark decay. Lorentz boosts γ L = 60 – 600 are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using π 0 → γ γ decays in LHC collision data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Hydrology in the Age of Artificial Intelligence: From Fragmentation to Coherent Terrestrial Hydrosphere Science

The rapid rise of machine learning (ML) in hydrology has prompted debate about the discipline's scientific relevance. While ML often outperforms traditional models in streamflow prediction, we argue that this reflects a deeper limitation: persistent fragmentation of hydrological science itself. Narrow focus on isolated components has hindered the development of coherent, scale‐relevant understanding of the integrated terrestrial hydrosphere. This is illustrated, for example, by widely divergent estimates of groundwater–streamflow interactions and of water balance‐implied ongoing storage changes. We argue that hydrology's future lies not in choosing between ML and physics, but in integrating data‐driven and process‐based approaches to advance consistent, realistic, and societally relevant understanding of the terrestrial hydrosphere and its multifaceted roles in the Earth System.

Painter, Scott L. [Oak Ridge National Laboratory (↗
DOE OSTI2024

On the anatomy of acoustic emission

Abrupt, local frictional fault failure comprises a displacement that is normally accompanied by acoustic emission (AE)—an impulsive elastic wave broadcast with an amplitude proportional to particle velocity. The aggregate of these displacements is the basic fault motion. In laboratory shear experiments, the examination of a sequence of laboratory earthquakes includes continuous measurements of fault motion and the associated AE that is broadcast. From these measurements, connections between the fault motion and cumulative sum of the AE amplitude can be identified. The composition of the AE broadcasts reveals inhomogeneity in the fault mechanical structure from which they arise. This inhomogeneity can be decomposed into a time invariant AE component and an articulated AE component. The articulated AE component serves as a “state of the fault diagnostic” that follows a distinctive pattern to fault failure. Thus, the articulated AE component can be used directly to monitor the state of the fault.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Characterization of Urbix Resources Natural Flake Graphite Material for Nuclear Applications (CRADA NFE-18-07340 Final Report)

Urbix Resources is a company specializing in graphite powder processing and purification. Urbix is looking to become the first domestic supplier of nuclear-grade graphite in the United States. This has led to a Cooperative Research and Development Agreement (CRADA) being established between the Oak Ridge National Laboratory (ORNL) and Urbix Resources, with the goal of characterizing a purified graphite feedstock supplied by Urbix Resources. The resulting data will then be used to determine the viability of a large-scale nuclear graphite production and qualification campaign. These characterization methods have been performed on the as-received graphite feedstock, a heat-treated batch of the feedstock, a milled batch of the feedstock, and a nuclear-grade graphite powder historically used in fuel production. The data from the heat-treated and milled feedstock will determine how different processing methods affect the powder’s physical properties, whereas the data from the nuclear grade graphite will provide information about the characteristics of a graphite previously accepted for nuclear applications. All of these powders have been characterized individually and as part of a resin-graphite blend (herein referred to as matrix) that has been used in past fuel experiments. The results of these experiments show that the Urbix powder and nuclear-grade graphite powder display notably different properties from one another in many cases, but this discrepancy may have been the result of the typical particle size of each powder. This hypothesis is supported by tests conducted on the milled powder, which showed a closer resemblance in properties to the nuclear-grade graphite than the as-received powder. The method for pressing the graphite matrix also had a noticeable effect on the measured properties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗
NASA NTRS1983

Observations of charging dynamics

Plasma data from the University of California, San Diego, Auroral Particles experiments on Applied Technology satellites 5 and 6 are used to investigate the dynamics of natural charging events. Both eclipse and daylight charging events are considered, and typical responses are illustrated by data from specific events. Two different physical processes are found to be involved in the charging process. One of these is straightforward: the spacecraft structure potential responds rapidly to changes in the environment, typically changing by hundreds of volts in a few seconds. The other process is more subtle: differential charging and potential barrier formation precede structural charging and determine the time scale; typically, tens of minutes are required for the potential to change by several hundreds of volts. The latter process is found to be predominately responsible for daylight charging on both spacecraft.

Olsen, R. C.↗
NASA NTRS1984

Magnetospheric topology of fields and currents

The interaction of the solar wind with the magnetosphere is examined from the standpoint of fundamental processes in physics and, whenever possible, observational data. A conceptual model is developed which incorporates several features of the magnetosphere that are still open to debate, such as open magnetic field lines in the polar cap and a boundary layer just inside the magnetopause. It is shown that the length of the magnetotail, the existence of the polar rain, the bursts of particles at the edge of the plasma sheet, and the poleward motion of auroral forms during the recovery phase of a magnetospheric substorm may all have a natural explanation with the proposed model. The one exception, however, is the topology of the electric field. The problems associated with reversal of the electric field within the magnetotail are examined.

Heikkila, W. J.↗