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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
DOE OSTI2021

The Atacama Cosmology Telescope: Detection of Millimeter-wave Transient Sources

Here we report on the serendipitous discovery of three transient millimeter-wave sources using data from the Atacama Cosmology Telescope. The first, detected at R.A. = 273.8138, decl. = -49.4628 at ~50σ total, brightened from less than 5 mJy to at least 1100 mJy at 150 GHz with an unknown rise time shorter than 13 days, during which the increase from 250 mJy to 1100 mJy took only 8 minutes. Maximum flux was observed on 2019 November 8. The source's spectral index in flux between 90-150 GHz was positive, α = 1.5 ± 0.2. The second, detected at R.A. = 105.1584, decl. = -11.2434 at ~20σ total, brightened from less than 20 mJy to at least 300 mJy at 150 GHz with an unknown rise time shorter than 8 days. Maximum flux was observed on 2019 December 15. Its spectral index was also positive, α = 1.8 ± 0.2. The third, detected at R.A. = 301.9952, decl. = 16.1652 at ~40σ total, brightened from less than 8 mJy to at least 300 mJy at 150 GHz over a day or less but decayed over a few days. Maximum flux was observed on 2018 September 11. Its spectrum was approximately flat, with a spectral index of α = -0.2 ± 0.1. None of the sources were polarized to the limits of these measurements. The two rising-spectrum sources are coincident in position with M and K stars, while the third is coincident with a G star.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Long Range Plan: Dense matter theory for heavy-ion collisions and neutron stars

Since the release of the 2015 Long Range Plan in Nuclear Physics, major events have occurred that reshaped our understanding of quantum chromodynamics (QCD) and nuclear matter at large densities, in and out of equilibrium. The US nuclear community has an opportunity to capitalize on advances in astrophysical observations and nuclear experiments and engage in an interdisciplinary effort in the theory of dense baryonic matter that connects low- and high-energy nuclear physics, astrophysics, gravitational waves physics, and data science. This is a white paper prepared by a group of nuclear physicists during the 2023 LRP process.

Lovato, Alessandro↗
DOE OSTI2020

Sound Speed as a Candidate for Internal Temperature Monitoring During Solid Phase Processing of Materials

Internal temperature is an important parameter for understanding the solid phase processing (SPP) of materials and improving feedback control of these processes. Friction stir processes, shear assisted processing and extrusion (ShAPE ™ ), and ultrahigh velocity (UHV) cold spray are examples of such processes. Simple thermal models based on temperatures measured at the boundary of the deforming body cannot capture the dynamic nature of temperature generation within the deforming solid. One possibly way of understanding internal temperature is by measuring sound speed, which varies as a function of temperature for materials and directing ultrasonic wave propagation through selected material volumes of interest. In this project, longitudinal wave speed is estimated for aluminum 6061. Piezoelectric materials bonded to a specimen provided data from which longitudinal wave speed was estimated. A discussion evaluated the linear relationship of wave speed versus temperature and postulated how wave speed measurements could be used to estimate the internal temperature of selected material volumes for feedback during SPP of materials. Instrumentation, data and analysis, and a discussion are reviewed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct astrophysical tests of chiral effective field theory at supranuclear densities

Recent observations of neutron stars with gravitational waves and X-ray timing provide unprecedented access to the equation of state (EoS) of cold dense matter at densities difficult to realize in terrestrial experiments. At the same time, predictions for the EoS equipped with reliable uncertainty estimates from chiral effective field theory ( x EFT) allow us to bound our theoretical ignorance. In this work, we analyze astrophysical data using a nonparametric representation of the neutron-star EoS conditioned on x EFT to directly constrain the underlying physical properties of the compact objects without introducing modeling systematics. We discuss how the data alone constrain the EoS at high densities when we condition on x EFT at low densities. Here, we also demonstrate how to exploit astrophysical data to directly test the predictions of x EFT for the EoS up to twice nuclear saturation density, in order to estimate the density at which these predictions might break down. We nd that the existence of massive pulsars, gravitational waves from GW170817, and NICER observations of PSR J0030+0451 favor x EFT predictions for the EoS up to nuclear saturation density over a more agnostic analysis by as much as a factor of 7 for the quantum Monte Carlo (QMC) calculations used in this work. While x EFT predictions using QMC are fully consistent with gravitational-wave data up to twice nuclear saturation density, NICER observations suggest that the EoS stiffens relative to these predictions at or slightly above nuclear saturation density. Additionally, for these QMC calculations, we marginalize over the uncertainty in the density at which x EFT begins to break down, constraining the radius of a 1.4M $\bigodot$ neutron star to R 1.4 = $11.40^{+1.38}_{–1.04}$ ($12.54^{+0.71}_{–0.63}$) km and the pressure at twice nuclear saturation density to p(2n sat ) = $14.2^{+18.1}_{–8.4}$ ($28.7^{+15.3}_{–15.0}$) MeV=fm 3 with massive pulsar and gravitational-wave (and NICER) data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

SPT-3G D1: Constraints on inflationary gravitational waves with two years of SPT-3G data

Here, we present a measurement of the 𝐵-mode polarization power spectrum of the cosmic microwave background anisotropies at 32 ≤ ℓ < 502 for three bands centered at 95, 150, and 220 GHz using data from the SPT-3G receiver on the South Pole Telescope. This work uses SPT-3G observations from the 2019 and 2020 winter observing seasons of a ∼1500 deg 2 patch of sky that directly overlaps with fields observed with the BICEP/Keck family of telescopes and covers part of the proposed Simons Observatory and CMB-S4 deep fields. Employing new techniques for mitigating polarized atmospheric noise, the SPT-3G data demonstrates a white noise level of 9.3 (6.7) μ⁢K−arcmin at ℓ ∼500 for the 95 GHz (150 GHz) data, with a 1/ℓ noise knee at ℓ = 128 (182). We fit the observed six auto- and cross-frequency 𝐵-mode power spectra to a model including lensed Λ⁢CDM 𝐵-modes and a combination of Galactic and extragalactic foregrounds. This work characterizes foregrounds in the vicinity of the BICEP/Keck survey area, finding foreground power consistent with that reported by the BICEP/Keck collaboration within the same region and a factor of ∼3 higher power over the full SPT-3G survey area. Using SPT-3G data over the BICEP/Keck survey area, we place a 95% upper limit on the tensor-to-scalar ratio of 𝑟 <0.25 and find the statistical uncertainty on 𝑟 to be 𝜎⁡(𝑟) = 0.067.

Zebrowski, J. A. [University of Chicago; Universit↗
DOE OSTI2021

Transmitted wave measurements in cold sprayed materials under dynamic compression

Spray-formed materials have complex microstructures which pose challenges for microscale and mesoscale modeling. To constrain these models, experimental measurements of wave profiles when subjecting the material to dynamic compression are necessary. The use of a gas gun to launch a shock into a material is a traditional method to understand wave propagation and provide information of time-dependent stress variations due to complex microstructures. This data contains information on wave reverberations within a material and provides a boundary condition for simulation. Here we present measurements of the wavespeed and wave profile at the rear surface of tantalum, niobium, and a tantalum/niobium blend subjected to plate impact. Measurements of the Hugoniot elastic limit are compared to previous work and wavespeeds are compared to longitudinal sound velocity measurements to examine wave damping due to the porous microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Constraints on Inflationary Gravitational Waves with Two Years of SPT-3G Data

We present a measurement of the $B$-mode polarization power spectrum of the cosmic microwave background anisotropies at 32 $\le$$\ell$$<$ 502 for three bands centered at 95, 150, and 220 GHz using data from the SPT-3G receiver on the South Pole Telescope. This work uses SPT-3G observations from the 2019 and 2020 winter observing seasons of a $\sim$1500 deg$^2$ patch of sky that directly overlaps with fields observed with the BICEP/Keck family of telescopes, and covers part of the proposed Simons Observatory and CMB-S4 deep fields. Employing new techniques for mitigating polarized atmospheric noise, the SPT-3G data demonstrates a white noise level of 9.3 (6.7) $\mu$K-arcmin at $\ell \sim 500$ for the 95 GHz (150 GHz) data, with a $1/\ell$ noise knee at $\ell$=128 (182). We fit the observed six auto- and cross-frequency $B$-mode power spectra to a model including lensed $\Lambda$CDM $B$-modes and a combination of Galactic and extragalactic foregrounds. This work characterizes foregrounds in the vicinity of the BICEP/Keck survey area, finding foreground power consistent with that reported by the BICEP/Keck collaboration within the same region, and a factor of $\sim$ 3 higher power over the full SPT-3G survey area. Using SPT-3G data over the BICEP/Keck survey area, we place a 95% upper limit on the tensor-to-scalar ratio of $r < 0.25$ and find the statistical uncertainty on $r$ to be $\sigma(r) = 0.067$.

Zebrowski, J. A. [Chicago U., KICP; Chicago U., As↗
DOE OSTI2023

Evaluation of a preliminary regional Earth model through comparison of synthetic and observed waveform data

In this report, we document the process related to developing a regional geologic model of a 605 x 1334 km area centered around Utah and encompassing surrounding states. This model is developed to test the effect that composition of a model has on the generation of synthetic data with the intent of using this information to improve upon full waveform moment tensor inversions. We compare observed data from three seismic events and five stations to the synthetic data generated by a preliminary model derived from a geologic framework model (GFM) developed by the USGS. The synthetic data and observed data comparisons indicate that our preliminary model performs well at smaller offset distances in the northern and central sections of the model. However, the southern stations consistently display synthetic data P- and S-wave arrival times that do not match the observed data arrival times, indicating that the velocity structure of the southern part of the model especially is inaccurate.

58 GEOSCIENCES↗
DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Selection rules and dynamic magnetoelectric effect of the spin waves in multiferroic Bi Fe O 3

Here, we report the magnetic-field dependence of the THz absorption and nonreciprocal directional dichroism spectra of BiFeO 3 measured on the three principal crystal cuts for fields applied along the three principal directions of each cut. From the systematic study of the light polarization dependence, we deduced the optical selection rules of the spin-wave excitations. Our THz data, combined with small-angle neutron scattering results showed that (i) an in-plane magnetic field rotates the q vectors of the cycloids perpendicular to the magnetic field and (ii) the selection rules are mostly determined by the orientation of the q vector with respect to the electromagnetic fields. We observed a magnetic field history-dependent change in the strength and the frequency of the spin-wave modes, which we attributed to the change of the orientation and the length of the cycloidal q vector, respectively. Finally, we compared our experimental data with the results of linear spin-wave theory that reproduces the magnetic-field dependence of the spin-wave frequencies and most of the selection rules, from which we identified the spin-polarization coupling terms relevant for the optical magnetoelectric effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

HERO WEC 2024 Hydraulic Configuration Deployment Data

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗