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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 OSTI2016

A mobile test facility based on a magnetic cumulative generator to study the stability of the power plants under impact of lightning currents

The report presents the results of the development and field testing of a mobile test facility based on a helical magnetic cumulative generator (MCGTF). The system is designed for full-scale modeling of lightning currents to study the safety of power plants of any type, including nuclear power plants. Advanced technologies of high-energy physics for solving both engineering and applied problems underlie this pilot project. The energy from the magnetic cumulative generator (MCG) is transferred to a high-impedance load with high efficiency of more than 50% using pulse transformer coupling. Modeling of the dynamics of the MEG that operates in a circuit with lumped parameters allows one to apply the law of inductance output during operation of the MCG, thus providing the required front of the current pulse in the load without using any switches. The results of field testing of the MCGTF are presented for both the ground loop and the model load. The ground loop generates a load resistance of 2–4 Ω. In the tests, the ohmic resistance of the model load is 10 Ω. It is shown that the current pulse parameters recorded in the resistive-inductive load are close to the calculated values.

42 ENGINEERING↗
DOE OSTI2025

Levoglucosan data from five coastal streams impacted by the 2020 CZU Lightning Complex Fires, California, United States

This dataset includes levoglucosan data for five coastal California (United States) streams impacted by the 2020 CZU Lightning Complex Fires which burned from August 16th through September 22nd. Levoglucosan is a highly soluble and biolabile fraction of pyrogenic carbon. The five watersheds (San Lorenzo River, Pescadero Creek, Majors Creek, Laguna Creek, and Scott Creek) were impacted by the fires with watersheds experiencing a range of burn severity and extents. Grab samples were collected from each stream between October 2020 and May 2021, targeting both baseflow and event flow hydrologic conditions. Additional biogeochemistry data (i.e., organic and black carbon concentrations) can be found in a separate data package (https://doi.org/10.4211/hs.421c0226bb38460c8393d67fe0c4f802). This data package consists of one main data folder that contains (1) readme; (2) file-level metadata; (3) data dictionary; (4) field metadata with international generic sample numbers (IGSN); (5) methods codes; and (6) levoglucosan data. All files are .csv or .pdf.

2020 CZU Lightning Complex Fires↗
DOE OSTI2020

“Catastrophic Hail Storm & Lightning Characteristics From It” for NWSFO Amarillo June 19, 2020

The storm developed along an outflow boundary. Daytime heating helped break two capping inversions noted on the morning weather balloon launch from KAMA. The storm appears to have reached a 35-40 dbz threshold quickly around 15:20. Pantex’s LMA detects “source densities” at 15:20. The first intra-cloud (CC) strikes occur simultaneously at 15:26 just west of 34th & Soncy. The first cloud-to-ground (CG) lightning strike occurred 7 minutes later at15:33 just west of Jason’s Deli (34th and Coulter Shopping Center).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

2020 Virtual Theoretical Division Lightning Talk Series

All members of the Theoretical Division Community, students, staff members, group leaders, division management and other interested individuals are invited to come and support the various Lightning Talk presentations.

36 MATERIALS SCIENCE↗
NASA NTRS1997

Lightning in the Protoplanetary Nebula?

Lightning in the protoplanetary nebula has been proposed as a mechanism for creating meteoritic chondrules: enigmatic mm-sized silicate spheres formed in the nebula by the brief melting of cold precursors.

Lightning protoplanetary nebula meteoritic chondru↗
NASA NTRS2017

Characterizing the GOES-R (GOES-16) Geostationary Lightning Mapper (GLM) On-Orbit Performance

Two overlapping efforts help to characterize the GLM performance, the Post Launch Test (PLT) phase to validate the predicted pre-launch instrument performance and the Post Launch Product Test (PLPT) phase to validate the lightning detection product used in forecast and warning decision-making. This paper documents the calibration and validation plans and activities for the first 6 months of GLM on-orbit testing and validation commencing with first light on 4 January 2017. The PLT phase addresses image quality, on-orbit calibration, RTEP threshold tuning, image navigation, noise filtering, and solar intrusion assessment, resulting in a GLM calibration parameter file. The PLPT includes four main activities, the Reference Data Comparisons (RDC), Algorithm Testing (AT), Instrument Navigation and Registration Testing (INRT), and Long Term Baseline Testing (LTBT). Field campaigns are also designed to contribute valuable insights into the GLM performance capabilities. The PLPT tests each contribute to the beta, provisional, and fully validated GLM data.

validation↗
NASA NTRS2017

Utilizing ISS Camera Systems for Scientific Analysis of Lightning Characteristics and Comparison with ISS-LIS and GLM

Video and still frame images from cameras aboard the International Space Station (ISS) are used to inspire, educate, and provide a unique vantage point from low-Earth orbit that is second to none; however, these cameras have overlooked capabilities for contributing to scientific analysis of the Earth and near-space environment. The goal of this project is to study how geo referenced video/images from available ISS camera systems can be useful for scientific analysis, using lightning properties as a demonstration.

Lightning↗
NASA NTRS2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Computational models of the Earth System are critical tools for modern scientific inquiry. Effortstoward evaluating and improving errors in representations of physical and chemical processes inthese large computational systems are commonly stymied by highly nonlinear and complexerror behavior. Recent work has shown that these errors can be effectively predicted usingmodern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previousstudies to apply an interpretable A.I. technique to not only predict model errors but also movetoward understanding the underlying reasons for successful error prediction. We use XGBoostclassification trees and SHapley Additive exPlanations (SHAP) analysis to explore the errors inthe prediction of lightning occurrence in the NASA GEOS model, a widely used Earth SystemModel. This explainable error prediction system can effectively predict the model error andindicates that the errors are strongly related to convective processes and the characteristics ofthe land surface.

Artificial intelligence↗
NASA NTRS2023

High-Speed Video of Lightning Strikes to Communications Tower on 7 August 2023

NASA operates a high-speed camera (a MIRO M310 manufactured by Vision Research) on the fourth floor of the Operations and Checkout Building pointed at the communications tower that is in the Industrial Area of Kennedy Space Center. Figure 1 shows the location of the camera and tower. Figure 2 shows the camera view during daylight. On 7 August 2023 between 11 pm and 12 am eastern standard time, three lightning flashes to the tower were recorded.

lightning↗
DOE OSTI2016

Generalization of the lightning electromagnetic equations of Uman, McLain, and Krider based on Jefimenko equations

The fundamental electromagnetic equations used by lightning researchers were introduced in a seminal paper by Uman, McLain, and Krider in 1975. However, these equations were derived for an infinitely thin, one-dimensional source current, and not for a general three-dimensional current distribution. In this paper, we introduce a corresponding pair of generalized equations that are determined from a three-dimensional, time-dependent current density distribution based on Jefimenko's original electric and magnetic equations. To do this, we derive the Jefimenko electric field equation into a new form that depends only on the time-dependent current density similar to that of Uman, McLain, and Krider, rather than on both the charge and current densities in its original form. The original Jefimenko magnetic field equation depends only on current, so no further derivation is needed. We show that the equations of Uman, McLain, and Krider can be readily obtained from the generalized equations if a one-dimensional source current is considered. For the purpose of practical applications, we discuss computational implementation of the new equations and present electric field calculations for a three-dimensional, conical-shape discharge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Abstract Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The electric fields and “lightning jets” of the Sun and solar wind

A model of electric energization of the solar wind and corona is developed, including electro-magnetic particle effects precluded by traditional magneto-hydro (MHD) assumptions. Using standard 1D radial solar models for particle density and temperature, the core gravito-electric field is calculated; and the range of possible photospheric photo-electric fields is estimated. The extant DC field apparently arises from about 460 C of charge displacement, mainly caused by the immense solar energy flux pushing electrons outward. Energetically, this electric field can accelerate surface protons out of the 2 keV gravity well and up to the 4 keV energies observed in the fast solar wind. The electrical energy is released in pervasive, persistent “proton lightning jets,” which are proton beams, charge-neutralized by co-propagating electrons. The jets are formed by pinched “avalanche breakdown” of the weakly ionized photosphere, probably initiated on the down-welling edges of solar surface granulations. These energetic jets will glow as discrete filamentary surface spicules and will be observed in reflected solar light as the diffuse K-corona. Significantly, the total charge displacement and observed fast wind speed are quantitatively predicted by a novel “virial limit”: the (positive) electrical potential energy at r = 0 is limited in magnitude to the 10 keV gravitational well at r = 0.

Physics↗