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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 OSTI2022

Differential Measurements of Jet Substructure and Partonic Energy Loss in Au+Au Collisions at √ sNN = 200 GeV

The STAR collaboration presents jet substructure measurements related to both the momentum fraction and the opening angle within jets in p+p and Au+Au collisions at √sNN =200GeV. The substructure observables include SoftDrop groomed momentum fraction (zg), groomed jet radius (Rg), and subjet momentum fraction (zSJ) and opening angle (θSJ). The latter observable is introduced for the first time. Fully corrected subjet measurements are presented for p+p collisions and are compared to leading-order Monte Carlo models. The subjet θSJ distributions reflect the jets leading opening angle and are utilized as a proxy for the resolution scale of the medium in Au+Au collisions. We compare data from Au+Au collisions to those from p+p which are embedded in minimum-bias Au+Au events in order to include the effects of detector smearing and the heavy-ion collision underlying event. The subjet observables are shown to be more robust to the background than zg and Rg. We observe no significant modifications of the subjet observables within the two highest-energy, back-to-back jets, resulting in a distribution of opening angles and the splittings that are vacuumlike. We also report measurements of the differential dijet momentum imbalance (AJ) for jets of varying θSJ. We find no qualitative differences in energy loss signatures for varying angular scales in the range 0.1< θSJ<0.3, leading to the possible interpretation that energy loss in this population of high-momentum dijet pairs, is due to soft medium-induced gluon radiation from a single color charge as it traverses the medium.

Abdallah, M. S.↗
DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Global and local polarization of Λ hyperons in Au+Au collisions at 200 GeV from STAR

New results on global polarization of Λ hyperons in Au+Au collisions at √ s NN = 200 GeV reveal non-zero signal on the order of a few tenths of a percent. Compared with lower energy results, the current measurement shows that the polarization decreases at higher collision energy, the trend being well reproduced by theoretical calculations. The polarization is found to be larger in more peripheral collisions, as well as in the in-plane than in the out-of-plane direction. The signal seems to depend on the asymmetry between positive and negative charge in each event, which may indicate an influence of the axial current induced by the magnetic field. In addition, the first measurement of a local polarization along the beam direction was performed. The results show a quadrupole modulation relative to the second-order event plane, as expected from the elliptic flow.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Recent Results and Methods on Higher Order and Off-diagonal Cumulants of Identified Net particle Multiplicity Distributions in Au+Au Collisions at STAR

We present recent results on the cumulants of net-particle distributions in Au+Au collisions at the STAR experiment. Beam energy dependence of net-Delta cumulants and off-diagonal cumulants are measured. These measurements provide additional constraints on the chemical freeze-out conditions. The sixth-order cumulant of net-charge and net-proton distributions is measured at psNN =200 GeV to search for the signal of the crossover phase transition. Finally, an unfolding technique is developed and applied to the net-proton distributions at 19.6 GeV to account for the non-binomial effects of the detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

PHENIX results on centrality dependence of yields and correlations in d plus Au collisions at root s(NN)=200 GeV

PHENIX has measured the transverse momentum (pT) spectra and two particle angular correlations for high pT particles in d+Au collisions at psNN=200 GeV using the RHIC Year-2008 run data. The azimuthal angle correlations for two particles with a large rapidity gap exhibit a ridge-like structure. Using the pi-0s reconstructed in the EMCal, we have successfully extended the pT reach of the correlation up to 8 GeV/c. We find that the azimuthal anisotropy of hadrons found at low pT persists up to 6 GeV/c with a significant centrality and pT dependence, similar to what was observed in A+A collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

PHENIX Probing QCD Matter Through Heavy Flavor and Quarkonium at RHIC

Recent results from the PHENIX experiment on heavy flavors and quarkonia production in p + Al, p + Au, d + Au, and Au + Au collision systems at psNN = 200 GeV are summarized. The results are carried out by the measurements of the nuclear modification factors and elliptic flow. The nuclear modification factors measurements give insight into the energy loss of heavy quarks in the quark-gluon plasma medium along their path lengths. The elliptic flow measurements are a good tool to investigate the coupling of heavy quarks with the medium. The measurements are presented as a function of centrality, rapidity, and transverse momentum. The interpretations of the results in light of our current theoretical models, and comparison to LHCb and ALICE measurements are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗