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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 OSTI2022

Classification of Ultrasonic Weld Quality using Acoustic Signatures Acquired During Manufacture

Ultrasonic welding is a process is based on generating a solid-state bond between two metals by applying moderate pressure and high intensity sound waves (20-70 kHz frequencies) at their interface. During the solid-state process numerous material, surface, instrument, and environmental factors contribute to bond formation, strength, and durability. The inherent difficulty of measuring and controlling each of these factors has, to date, made predicting bond quality elusive. In this work, a Sonics model MWB20 ultrasonic spot welder with integral base, was used to produce a variety of welds of different metal foils under varying weld conditions. Acoustic measurements were recorded throughout the measurement process. Subsequent analysis of the signals using metrics that approximate the energy dispersed during the weld proved to successfully predict weld quality. Two metrics based on normalized energy differential and Renyi entropy were developed into Python and C++ scripts for direct analysis of weld acoustic data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling the differential rate for signal interactions in coincidence with noise fluctuations or large rate backgrounds

The characteristic energy of a relic dark matter interaction with a detector scales strongly with the putative dark matter mass. Consequently, experimental search sensitivity at the lightest masses will always come from interactions whose size is similar to noise fluctuations and low energy backgrounds in the detector. In this paper, we correctly calculate the net change in measured differential event rate due to low rate signal interactions that overlap in time with noise and backgrounds, accounting for both periods of time when the signal is coincident with noise/backgrounds and for the decreased amount of time in which only noise/backgrounds occur and we show that the introduction of random simulated signal events into the continuous raw data stream (a form of “salting”) provides a correct and practical implementation to estimate this net linear signal sensitivity. Unfortunately this does not apply to the situation with non negligible pileups as we show through explicit examples. Consequently, this exclusion technique should be complemented by less sensitive techniques that can separately exclude large rate, high pileup signal parameter space. An analysis threshold above the Gaussian-like noise component of the measured differential rate spectrum should also likely produce conservative limits. Though previous light mass dark matter searches did not correctly account for decreased background only live time effects in their signal sensitivity, conservative analysis choices likely protected their published limits from being nonconservative.

Li, Xinran↗
DOE OSTI2021

Structural analysis of the PTEN:P-Rex2 signaling complex reveals how cancer-associated mutations coordinate to hyperactivate Rac1

The dual-specificity phosphatase PTEN functions as a tumor suppressor by hydrolyzing PI(3,4,5)P 3 to PI(4,5)P 2 to inhibit PI3K-AKT signaling and cellular proliferation. P-Rex2 is a guanine nucleotide exchange factor for Rho GTPases and can be activated by Gβγ subunits downstream of G protein–coupled receptor signaling and by PI(3,4,5)P 3 downstream of receptor tyrosine kinases. The PTEN:P-Rex2 complex is a commonly mutated signaling node in metastatic cancer. Assembly of the PTEN:P-Rex2 complex inhibits the activity of both proteins, and its dysregulation can drive PI3K-AKT signaling and cellular proliferation. Here, using cross-linking mass spectrometry and functional studies, we gained mechanistic insights into PTEN:P-Rex2 complex assembly and coinhibition. We found that PTEN was anchored to P-Rex2 by interactions between the PDZ-interacting motif in the PTEN C-terminal tail and the second PDZ domain of P-Rex2. This interaction bridged PTEN across the P-Rex2 surface, preventing PI(3,4,5)P 3 hydrolysis. Conversely, PTEN both allosterically promoted an autoinhibited conformation of P-Rex2 and blocked its binding to Gβγ. In addition, we observed that the PTEN-deactivating mutations and P-Rex2 truncations combined to drive Rac1 activation to a greater extent than did either single variant alone. These insights enabled us to propose a class of gain-of-function, cancer-associated mutations within the PTEN:P-Rex2 interface that uncouple PTEN from the inhibition of Rac1 signaling.

D’Andrea, Laura↗
DOE OSTI2026

Model-independent measurement of the Higgs boson associated production with two jets and decaying to a pair of W bosons in proton-proton collisions at $\sqrt{s}=13$ TeV

A model-independent measurement of the differential production cross section of the Higgs boson decaying into a pair of W bosons, with a final state including two jets produced in association, is presented. In the analysis, events are selected in which the decay products of the two W bosons consist of an electron, a muon, and missing transverse momentum. The model independence of the measurement is maximized by employing a discriminating variable, developed through machine learning, that is agnostic to the signal hypothesis. The analysis is based on proton-proton collision data at $\sqrt{s}=13$ TeV collected with the CMS detector from 2016–2018, corresponding to an integrated luminosity of 138 fb −1 . The production cross section is measured as a function of the difference in azimuthal angle between the two jets. The differential cross section measurements are used to constrain Higgs boson couplings within the standard model effective field theory framework.

Hadron-Hadron Scattering↗
DOE OSTI2024

Detectability of neutrino-signal fluctuations induced by the hadron-quark phase transition in failing core-collapse supernovae

Here we introduce a systematic and quantitative methodology for establishing the presence of neutrino oscillatory signals due to the hadron-quark phase transition (PT) in failing core-collapse supernovae from the observed neutrino event rate in water- or ice-based neutrino detectors. The methodology uses a likelihood ratio in the frequency domain as a test-statistic; it is employed for quantitative analysis of neutrino signals without assuming the frequency, amplitude, starting time, and duration of the PT-induced oscillations present in the neutrino events and thus it is suitable for analyzing neutrino signals from a wide variety of numerical simulations. We test the validity of this method by using a core-collapse simulation of a 17 solar-mass star by Zha et al. [Astrophys. J. 911, 74 (2021) ]. Based on this model, we further report the presence of a PT-induced oscillations quantitatively for a core-collapse supernovae out to a distance of ~10 kpc, ~5 kpc for IceCube and to a distance of ~10 kpc, ~5 kpc, and ~1 kpc for a 0.4 Mt mass water Cherenkov detector. This methodology will aid the investigation of a future galactic supernova and the study of hadron-quark phase in the core of core-collapse supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Improved bound on nonlinear quantum mechanics using a cryogenic radio frequency experiment

There are strong arguments that quantum mechanics may be nonlinear in its dynamics. A discovery of nonlinearity would hint at a novel understanding of the interplay between gravity and quantum field theory, for example. As such, experiments searching for potential nonlinear effects in the electromagnetic sector are important. Here, in this study, we outline such an experiment, consisting of a stream of random bits (which were generated using Rigetti’s Aspen-M-3 chip) as input to an rf signal generator coupled to a cryogenic detector. Projective measurements of the qubit state, which is originally prepared in an equal superposition, serve as the random binary output of a signal generator. Thereafter, spectral analysis of the rf detector would yield a detectable excess signal predicted to arise from such a nonlinear effect. A comparison between the projective measurements of the quantum bits vs the classical baseline showed no power excess. This sets a new limit on the electromagnetic nonlinearity parameter |ε| ⪅ 1.15 x 10 -12 , at a 90.0% confidence level. This is the most stringent limit on nonlinear quantum mechanics thus far and an improvement by nearly a factor of 50 over the previous experimental limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the $L$-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9' beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake figure of merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is continuously transmitting a narrowband signal with an equivalent isotropic radiated power (EIRP) > 10 13 W. For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 10 16 W) is at most 3 × 10 -4 . Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Wavelength modulation laser-induced fluorescence for plasma characterization

Laser-Induced Fluorescence (LIF) spectroscopy is an essential tool for probing ion and atom velocity distribution functions (VDFs) in complex plasmas. VDFs carry information about the kinetic properties of species that is critical for plasma characterization. Accurate interpretation of these functions is challenging due to factors such as multicomponent distributions, broadening effects, and background emissions. Our research investigates the use of Wavelength Modulation (WM) LIF to enhance the sensitivity of VDF measurements. Unlike standard Amplitude Modulation (AM) methods, WM–LIF measures the derivative of the LIF signal. This approach makes variations in VDF shape more pronounced. VDF measurements with WM–LIF were investigated with both numerical modeling and experimental measurements. The developed model enables the generation of both WM and AM signals, facilitating comparative analysis of fitting outcomes. Experiments were conducted in a weakly collisional argon plasma with magnetized electrons and non-magnetized ions. Measurements of the argon ion VDFs employed a narrow-band tunable diode laser, which scanned the 4p 4 D 7/2 –3d 4 F 9/2 transition centered at 664.553 nm in vacuum. A lock-in amplifier detected the second harmonic WM signal, which was generated by modulating the laser wavelength with an externally controlled piezo-driven mirror of the diode laser. Finally, our findings indicate that the WM–LIF signal is more sensitive to fitting parameters, allowing for better identification of VDF parameters such as the number of distribution components, their temperatures, and velocities. In addition, WM–LIF can serve as an independent method to verify AM measurements and is particularly beneficial in environments with substantial light noise or background emissions, such as those involving thermionic cathodes and reflective surfaces.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Tale of Two Catchments: Causality Analysis and Isotope Systematics Reveal Mountainous Watershed Traits That Regulate the Retention and Release of Nitrogen

Abstract Mountainous watersheds are characterized by variability in functional traits, including vegetation, topography, geology, and geomorphology, which determine nitrogen (N) retention, and release. Coal Creek and East River are two contrasting catchments within the Upper Colorado River Basin that differ markedly in total nitrate (NO 3 − ) export. The East River has a diverse vegetation cover, and sinuous floodplains, and is underlain by N‐rich marine shale. At 0.21 ± 0.14 kg ha −1 yr −1 , the East River exports ∼3.5 times more NO 3 − relative to the conifer‐dominated Coal Creek (0.06 ± 0.02 kg ha −1 yr −1 ). While this can partly be explained by the larger size of the East River, the distinct watershed traits of these two catchments imply different mechanisms controlling the aggregate N‐export signal. A causality analysis shows physical and biogenic processes were critical in determining NO 3 − export from the East River catchment. Stable isotope ratios of NO 3 − (δ 15 N NO3 and δ 18 O NO3 ) show the East River catchment is a strong hotspot for biogeochemical processing of NO 3 − at the hillslope soil‐saprolite. By contrast, the conifer‐dominated Coal Creek retained nearly all atmospherically deposited NO 3 − , and its export was controlled by catchment hydrological traits (i.e., snowmelt periods and water table depth). The conservative N‐cycle within Coal Creek is likely due to the abundance of conifer trees, and smaller riparian regions, retaining more NO 3 − overall and reduced processing prior to export. This study highlights the value of integrating isotope systematics to link watershed functional traits to mechanisms of watershed element retention and release.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database

The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.

Research & Experimental Medicine↗
DOE OSTI2024

Detectability of QCD phase transitions in binary neutron star mergers: Bayesian inference with the next generation gravitational wave detectors

We study the detectability of postmerger QCD phase transitions in neutron star binaries with next-generation gravitational-wave detectors Cosmic Explorer and Einstein Telescope. We perform numerical relativity simulations of neutron star mergers with equations of state that include a quark deconfinement phase transition through either a Gibbs or Maxwell construction. These are followed by Bayesian parameter estimation of the associated gravitational-wave signals using the nrpmw waveform model, with priors inferred from the analysis of the inspiral signal. We assess the ability of the model to measure the postmerger peak frequency $f$$^{peak}_{2}$ and identify aspects that should be improved in the model. We show that, even at postmerger signal to noise ratios as low as 10, the model can distinguish (at the 90% level) $f$$^{peak}_{2}$ between binaries with and without a phase transition in most cases. Phase-transition induced deviations in the $f$$^{peak}_{2}$ from the predictions of equation-of-state insensitive relations can also be detected if they exceed 1.6⁢σ. Our results suggest that next-generation gravitational wave detectors can measure phase transition effects in binary neutron star mergers. Furthermore, unless the phase transition is “strong,” disentangling it from other hadronic physics uncertainties will require significant theory improvements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

EXCLUSIVE NEUTRAL PION ELECTROPRODUCTION CROSS SECTION MEASUREMENTSWITHANEUTRALPARTICLE SPECTROMETER

Deep Virtual Compton Scattering (DVCS), the exclusive electron-proton scattering process ep ¿e'p'¿, provides access to generalized parton distributions (GPDs), which correlate information about the longitudinal momentum and transverse spatial structure of quarks inside the nucleon. Experiment E12-13-010 in Hall C at Jefferson Lab was designed to take high-precision measurements of the DVCS cross section over an extended kinematic range using the newly commissioned Neutral Particle Spectrometer (NPS). The NPS features a high-resolution electromagnetic calorimeter and a streaming data acquisition system optimized for operation at high luminosities. This thesis presents the detector and analysis work carried out to support the NPS DVCS program. In particular, it focuses on the hardware design, calibration, and performance of the calorimeter. A development of a waveform reconstruction analysis of the calorimeter signals enabled improved extraction of pulse amplitudes and times. The waveform analysis was also extended to operate in a multithreaded environment, substantially reducing processing time for large datasets. Analysis of exclusive neutral pion electroproduction events in the calorimeter gives a strong validation of the calorimeter’s performance and resolution. Together these developments establish a foundation for future analyses and extraction of the DVCS cross section and its use in constraining the GPDs.

Kerver, Mitchell [Old Dominion Univ., Norfolk, VA ↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reliable machine prognostic health management in the presence of missing data

Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in-service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time-series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data-driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one-hot health state indicators are appended to the historical time series so that the model learns end-of-life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Furthermore, experiments on multivariate time-series datasets collected from real-world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long-term prognostics.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Comparative Analysis of Imaging and Measurements of Micrometer-Scale Fracture Aperture Fields Within a Heterogeneous Rock Using PET and X-ray CT

Knowledge of the spatial distribution of fracture apertures is essential for reliable characterization of flow and transport processes in fractured systems and for better understanding of physicochemical matrix–fracture interactions. Here, we propose and test two image-based methods, thereby extending the current experimental capabilities to characterize aperture size distribution in structurally heterogeneous geologic porous media noninvasively. The first approach utilizes an inversion method based on the dataset acquired from positron emission tomography (PET) and the second approach considers an extension of the classic missing attenuation technique that relies on clinical X-ray computed tomography (X-ray CT). Independent sets of imaging experiments are conducted on a fractured basalt core with heterogeneous matrix properties and aperture distributions to compare the two methodologies. A repeat of each experiment is conducted to verify the proposed workflows. The performance of these two imaging techniques is systematically evaluated through the analysis of signal-to-noise ratio, minimum fracture size detectability, and measurement errors. While both approaches provide a reliable estimation of fracture aperture distributions, PET yields a signal-to-noise ratio that is substantially higher than the corresponding X-ray CT measurements. Furthermore, uncertainties of the aperture values for PET are considerably lower ($\bar {\sigma}_{\text {d}} = 15\%$) compared to those obtained from X-ray CT ($\bar {\sigma}_{\text {d}} = 29\%$), allowing for the detection of minimum aperture sizes of 20 $\mu$m with 70% confidence level. Finally, these approaches provide key experimental tools for better understanding dynamic hydromechanical fracture properties in geologic systems.

58 GEOSCIENCES↗
DOE OSTI2020

Extracting a mixing parameter from 2D radiographic imaging of variable-density turbulent flow

We extract a suitably averaged fluctuating density from the two-dimensional radiographic image of a flow. The X-ray attenuation is given by the Beer–Lambert law which exponentially damps the incident beam intensity by a factor proportional to the density, opacity and thickness of the target. By making reasonable assumptions for the mean density, opacity and effective thickness of the target flow, we estimate the density fluctuation contribution to the attenuation. The extracted density fluctuations averaged across the thickness of the flow in the direction of the beam may be used to form the density–specific-volume correlation $b$. In a statistical description of variable-density turbulence, $b$ quantifies the degree of mixedness. The ability to extract a measure of mixedness from experimental data would be a powerful tool that could be used in the validation of mix models. The scheme proposed is tested for DNS data computed for variable density buoyancy-driven mixing. We quantify the deficits in the extracted value of $b$ due to target thickness, Atwood number and modeled signal noise. This analysis justifies using the proposed scheme to infer the mix parameter from thin targets at moderate to low Atwood numbers. Furthermore, to illustrate how the scheme might be used in a practical problem, we demonstrate its application to a radiographic image of counter-shear flow obtained from experiments at the National Ignition Facility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The exit-wave power-cepstrum transform for scanning nanobeam electron diffraction: robust strain mapping at subnanometer resolution and subpicometer precision

Scanning nanobeam electron diffraction (NBED) with fast pixelated detectors is a valuable technique for rapid, spatially resolved mapping of lattice structure over a wide range of length scales. However, intensity variations caused by dynamical diffraction and sample mistilts can hinder the measurement of diffracted disk centers as necessary for quantification. Robust data processing techniques are needed to provide accurate and precise measurements for complex samples and non-ideal conditions. Here we present an approach to address these challenges using a transform, called the exit wave power cepstrum (EWPC), inspired by cepstral analysis in audio signal processing. The EWPC transforms NBED patterns into real-space patterns with sharp peaks corresponding to inter-atomic spacings. We describe a simple analytical model for interpretation of these patterns that cleanly decouples lattice information from the intensity variations in NBED patterns caused by tilt and thickness. With tracking the inter-atomic spacing peaks in EWPC patterns, strain mapping is demonstrated for two practical applications: mapping of ferroelectric domains in epitaxially strained PbTiO 3 films and mapping of strain profiles in arbitrarily oriented core-shell Pt-Co nanoparticle fuel-cell catalysts. The EWPC transform enables lattice structure measurement at sub-pm precision and sub-nm resolution that is robust to small sample mistilts and random orientations.

42 ENGINEERING↗