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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Measurement of the dependence of the hadron production fraction ratio $f_\mathrm{s} / f_\mathrm{u}$ and $f_\mathrm{d} / f_ \mathrm{u}$ on B meson kinematic variables in proton-proton collisions at $\sqrt{s}$ = 13 TeV

The dependence of the ratio between the B$_\mathrm{s}^0$ and B$^+$ hadron production fractions, $f_\mathrm{s} / f_\mathrm{u}$, on the transverse momentum ($p_\mathrm{T}$) and rapidity of the B mesons is studied using the decay channels B$_\mathrm{s}^0$$\to$ J$/\psi\,\phi$ and B$^+$$\to$ J$/\psi$ K$^+$. The analysis uses a data sample of proton-proton collisions at a center-of-mass energy of 13 TeV, collected by the CMS experiment in 2018 and corresponding to an integrated luminosity of 61.6 fb$^{-1}$. The $f_\mathrm{s} / f_\mathrm{u}$ ratio is observed to depend on the B $p_\mathrm{T}$ and to be consistent with becoming asymptotically constant at large $p_\mathrm{T}$. No rapidity dependence is observed. The ratio of the B$^0$ to B$^+$ hadron production fractions, $f_\mathrm{d} / f_\mathrm{u}$, measured using the B$^0$$\to$ J$/\psi$ K$^{*0}$ decay channel, is found to be consistent with unity and independent of $p_\mathrm{T}$ and rapidity, as expected from isospin invariance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A variable X-ray chopper system for phase-sensitive detection in synchrotron X-ray scanning tunneling microscopy

An ultra-high-vacuum compatible X-ray chopper system has been designed, constructed and integrated into the XTIP beamline at the Advanced Photon Source at Argonne National Laboratory. The XTIP beamline can operate at soft X-ray energies from 400 eV to 1900 eV while providing a focused beam down to about 10 µm × 10 µm into the synchrotron X-ray scanning tunneling microscopy (SX-STM) endstation instrument. The X-ray chopper is a critical component for separating topographic information from chemical information in SX-STM through phase-sensitive current detection. Depending on the experimental needs, the modulation frequency can be controlled from 100 Hz to 10 kHz. In addition, the chopper system is fully bakeable and can achieve a base pressure of 10 -10 mbar. Facilities for active water cooling have been designed, but passive cooling through copper braids has been shown to be sufficient at standard chopping frequencies. Using an Fe/Al 2 O 3 /CoAl(111) sample, the separation of the SX-STM current into a chemical component and a stable feedback signal is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Multi-Stack Variable Stiffness Magnetic Torsion Spring for a Wave Energy Converter

This paper presents the design of a multi-stack high torque adjustable stiffness torsional magnetic spring for use in a wave energy converter. The torsional magnetic spring has a ±45 degrees linear stroke length with peak torque of 823 N⋅m. A 3-D magnetostatic finite element analysis parameter sizing sweeping analysis was performed and the peak energy density for the selected torsion spring was computed to be 12.6 J/kg. The required torque was increased by using a unique seven-stage multi-stacking design. The stiffness of magnetic torsion spring is adjusted through the axial translation of the inner rotor. The presented design is shown to be capable of providing both positive and negative stiffness with equal characteristics and has a very high degree of linearity. The experimental prototype assembly and test-setup used to verify the torsional spring performance is presented.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗