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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Cloud-encounter and particle-concentration variabilities from GASP data

Summary statistics, tabulations, and variability studies are presented for cloud encounter and particle concentration data taken as part of the NASA global atmospheric sampling program. Cloud encounter was experienced in about 15 percent of the data samples; however, the percentage varies with season, latitude, and altitude (particularly distance from the tropopause). In agreement with classical storm models, the data show more clouds in the upper troposphere in anticyclones than in cyclones. The concentration of particles with a diameter greater than 3 micron also varies with time and location, depending primarily on the horizontal extent of cloudiness. Some examples of the application of the statistical data to the estimation of the frequency of cloud encounter and laminar flow loss to be expected on long range airline routes are also presented.

Nastrom, G. D.↗
DOE OSTI2023

Investigating the contribution of grown new particles to cloud condensation nuclei with largely varying preexisting particles – Part 1: Observational data analysis

This study employed multiple techniques to investigate the contribution of grown new particles to the number concentration of cloud condensation nuclei (CCN) at various supersaturation (SS) levels at a rural mountain site in the North China Plain from 29 June to 14 July 2019. On 8 new particle formation (NPF) days, the total particle number concentrations (N cn ) were 8.4 ± 6.1×10 3 cm -3 , which was substantially higher compared to 4.7 ± 2.6×10 3 cm -3 on non-NPF days. However, the CCN concentration (Nccn) at 0.2 % SS and 0.4 % SS on the NPF days was significantly lower than those observed on non-NPF days (p<0.05). This was due to the lower cloud activation efficiency of preexisting particles resulting from organic vapor condensation and smaller number concentrations of preexisting particles on NPF days. A case-by-case examination showed that the grown new particles only yielded a detectable contribution to N ccn at 0.4 % SS and 1.0 % SS during the NPF event on 1 July 2019, accounting for 12 % ± 11 % and 23 % ± 12 % of N ccn , respectively. The increased N ccn during two other NPF events and at 0.2 % SS on 1 July 2019 were detectable but determined mainly by varying preexisting particles rather than grown new particles. In addition, the hygroscopicity parameter values, concentrations of inorganic and organic particulate components, and surface chemical composition of different sized particles were analyzed in terms of chemical drivers to grow new particles. The results showed that the grown new particles via organic vapor condensation generally had no detectable contribution to N ccn but incidentally did. However, this conclusion was drawn from a small size of observational data, leaving more observations, particularly long-term observations and the growth of preexisting particles to the CCN required size, needed for further investigation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Evolution of Combustion-Generated Particles at Tropospheric Conditions

This paper describes particle evolution measurements taken in the Particulate Aerosol Laboratory (PAL). The PAL consists of a burner capable of burning jet fuel that exhausts into an altitude chamber that can simulate temperature and pressure conditions up to 13,700 m. After presenting results from initial temperature distributions inside the chamber, particle count data measured in the altitude chamber are shown. Initial particle count data show that the sampling system can have a significant effect on the measured particle distribution: both the value of particle number concentration and the shape of the radial distribution of the particle number concentration depend on whether the measurement probe is heated or unheated.

Tacina, Kathleen M.↗
NASA NTRS1974

Effects of interplanetary shock waves on energetic charged particles

Experimental data on the influence of interplanetary perpendicular and oblique shock waves on the ambient energetic protons are presented along with a theoretical analysis of the acceleration of particles in almost perpendicular shock waves. It was found that low-energy protons can be accelerated in perpendicular shock waves by repeated crossings of the shock front up to a maximum energy given by the product of their initial energy times the ratio of the magnetic fields. High-energy protons need to stay at the shock front for longer times than low-energy protons in order to reach the same relative energy gain. In the theoretical study of proton acceleration at almost perpendicular shock waves, it was found that protons reflected at shock waves with the angle between the upstream magnetic field and the shock normal greater than about 80 deg achieve large energy gains at the shock front. The larger this angle, the higher the energy gain. However, the reflection and energization of protons at these shock waves is not 'instantaneous', neither is it a one-step process: it is performed through repeated crossings of the shock front.

Sarris, E. T.↗
DOE OSTI2026

Irreducible Bhabha background in the detection of muonium-antimuonium conversion

Experiments such as MACS and the proposed MACE study muonium-antimuonium conversion by the energies of the final state 𝑒 ± . The 𝑒 + and 𝑒 − from an antimuonium decay tend to be nonrelativistic and relativistic, respectively, and vice versa for muonium. However, these 𝑒 ± can exchange their energies by hard Bhabha scattering, causing muonium to fake an antimuonium decay signal. We compute the rate for this background and find that, while negligible for MACE, it will become larger than the signal for conversion probabilities less than 10 −18 .

muonium↗
DOE OSTI2023

Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector

Measurements of electrons from 𝜈𝑒 interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectra is derived, and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of lost energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50 MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. Here, we develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the Compact Muon Solenoid (CMS) experiment. We show that parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗