Results for “source location” — Page 10

NASA NTRS1979

Evidence for a variable far-infrared source in NGC 6334

NGC 6334 has been mapped with a 40-250 micron photometer with 1 arcmin resolution. Six sources of far-infrared radiation have been detected. The second-strongest source was not detected in an earlier (2.7 years) 40-350 micron survey of the same region. This source is interpreted as a variable far-infrared source. The new source, located at the position of OH and H2O maser sources, is extended (0.7 arcmin FWHM) and has a bolometric luminosity of 190,000 solar luminosities and may represent a hitherto unobserved transient stage of protostellar collapse.

Mcbreen, B.↗

NASA NTRS1990

Source of O mode radio emissions from the dayside of Uranus

During the inbound trajectory toward Uranus, the Planetary Radio Astronomy instrument on Voyager 2 observed narrow-band smooth (n-smooth) emission at frequencies centered near 60 kHz and O-mode emission (the dayside source) in a frequency range narrowly confined around 160 kHz. Assuming empirical models of the plasma density for the dayside magnetosphere of Uranus, and using cold plasma theory together with observational constraints, ray-tracing calculations are performed to determine the source location of the O-mode emission. The dayside source appears to originate along magnetic field lines with a footprint near the north magnetic pole. Sources of nightside high-frequency broadband smooth (b-smooth) emission observed by Voyager after encounter are believed to exist near the conjugate footprint of these same field lines. This would indicate that the particle population supplying the free energy source has energies at least as high as a few keV.

Menietti, J. D.↗

DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗

NASA NTRS1981

Variable positron annihilation radiation from the galactic center region

HEAO 3 Cosmic Gamma-Ray Spectrometer evidence is presented for the existence of a time-varying, unshifted, narrow 511 keV line emission from the vicinity of the galactic center. Although uncertainties exist regarding the spatial extent of the features as well as its centroid, all data are consistent with emission from a single point source located at the galactic center. This interpretation would require a source luminosity of 2 x 10 to the 37th ergs/sec, and a positron annihilation rate of about 10 to the 43rd/sec. It is concluded that a variable source of positrons which could generate such an annihilation figure might be a massive black hole at the galactic center, as has been suggested by IR observations.

Riegler, G. R.↗

NASA NTRS1996

Lightning Studies Using VHF Waveform Data

Several atmospheric electricity studies were begun utilizing VHF lightning data obtained with the lightning detection and ranging system (LDAR) at the Kennedy Space Center (KSC). The LDAR system uses differences in the time of arrival of electromagnetic noise generated by the lightning process to seven antennas to calculate very accurate three dimensional locations of lightning. New software was developed to obtain the source location of multiple, simultaneous, and spatially separate lightning signatures. Three studies utilizing these data were begun this summer: (1) VHF observations of simultaneous lightning, (2) ground based VHF observations of transionospheric pulse pairs (TIPPs), and (3) properties of intra-cloud recoil streamers. The principal result of each of these studies are: (1) lightning commonly occurs in well separated (2-50 km) regions simultaneously, (2) large amplitude pairs of VHF pulses are commonly observed on the ground but had not been previously identified due to the large number of signals usually observed in the VHF noise of close lightning, and (3) the VHF Q-noise and pulse signatures associated with K-changes within intra-cloud lightning propagate at velocities of more than 10(exp 8) m/s. The interim results of these three studies are reviewed in this brief report.

Moldwin, Mark↗

NASA NTRS1993

The EUVE satellite survey database

The EUVE survey database contains fundamental science data for 9000 potential source locations (pigeonholes) in the sky. The first release of the Bright Source List is now available to the public through an interface with the NASA Astrophysical Data System. We describe the database schema design and the EUVE source categorization algorithm that compares sources to the ROSAT Wide Field Camera source list.

Craig, N.↗

NASA NTRSDate not supplied

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗

NASA NTRS2024

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗

NASA NTRS2024

Thermal Inspection of Low Emissivity Surfaces Using a Pulsed Light Emitting Diodes (PLED) Heat Source

Thermal inspections of a structure typically utilize a flash or quartz lamp heat source located on the same side of an infrared camera. The heat source provides light energy for heating while the infrared camera measures the surface transient temperature response. The inspection can be difficult for low emissivity surfaces for several reasons. First, the high intensity light can reflect off the surface and cause “burn-in” to the camera’s detector. The “burn-in” can take time for the sensors to recover and potentially damage the detector. Secondly, the heat source after pulsing, has a transient cool down component. The cool down component can be reflected and therefore superimposed over the structure’s thermal response which can cause an error (false defect indications) in the inspection. Lastly, the heat source is spectrally broad and therefore while heating, infrared components of the heat source can produce non-uniformity in the measured temperature field. Typically for the inspection of reflective surfaces, paint or other emissivity enhancing coatings are applied before inspection. In this paper, a pulsed light emitting diodes (PLED) heat source is used. The PLED heat source is spectrally narrow, contained within the visible band, and therefore not detectable by the infrared camera. The PLED heat source is configured to reduce any transient cool down components that could produce false defect indications. The PLED thermal inspections are compared to commercially available flash thermography inspections on unpainted aluminum samples with simulated corrosion and additively manufactured Ti-6AL-4V metal specimens.

thermal nondestructive evaluation↗

NASA NTRS2024

Thermal Inspection of Low Emissivity Surfaces Using a Pulsed Light Emitting Diodes (PLED) Heat Source

Thermal inspections of a structure typically utilize a flash or quartz lamp heat source located on the same side of an infrared camera. The heat source provides light energy for heating while the infrared camera measures the surface transient temperature response. The inspection can be difficult for low emissivity surfaces for several reasons. First, the high intensity light can reflect off the surface and cause “burn-in” to the camera’s detector. The “burn-in” can take time for the sensors to recover and potentially damage the detector. Secondly, the heat source after pulsing, has a transient cool down component. The cool down component can be reflected and therefore superimposed over the structure’s thermal response which can cause an error (false defect indications) in the inspection. Lastly, the heat source is spectrally broad and therefore while heating, infrared components of the heat source can produce non-uniformity in the measured temperature field. Typically for the inspection of reflective surfaces, paint or other emissivity enhancing coatings are applied before inspection. In this paper, a pulsed light emitting diodes (PLED) heat source is used. The PLED heat source is spectrally narrow, contained within the visible band, and therefore not detectable by the infrared camera. The PLED heat source is configured to reduce any transient cool down components that could produce false defect indications. The PLED thermal inspections are compared to commercially available flash thermography inspections on unpainted aluminum samples with simulated corrosion and additively manufactured Ti-6AL-4V metal specimens.

thermal nondestructive evaluation↗

NASA NTRS1968

An investigation to locate the acoustic sources in a high speed jet exhaust stream

Experimental study to locate acoustic sources in Mach 2.5 jet exhaust stream

Potter, R. C.↗

DOE OSTI2023

3D Source Reconstruction Using Coded Aperture Gamma-Ray Imaging

Recent measurements with coded-aperture imagers demonstrate material mass determination in a holdup setting to an accuracy within a few percent. This capability is of particular interest to the Surplus Plutonium Disposition (SPD) project, which aims to dilute and dispose of surplus plutonium oxide. Gamma-ray imagers can be used to determine holdup without interrupting normal operations. In this work, we examine techniques for 3D source localization and mass determination using gamma-ray imagers. Coded-aperture imagers provide excellent source localization within the 2D image plane; however, multiple imagers operating in tandem are necessary to identify source location in 3D space. A Maximum Likelihood Expectation-Maximization (MLEM) method for fitting detector mappings is a powerful tool for accomplishing this task. MLEM allows 3D source localization to be simultaneously constrained using multiple gamma-ray imagers by constructing the basis for the MLEM fit using detector mappings from different detector locations stitched together. Each of these basis points represents a singular response from a source in 3D space and is generated using Monte Carlo simulations of sources placed individually at different locations throughout the imager’s field of view. Additionally, implementing knowledge of the physical equipment in the simulations of the glovebox used for the SPD project incorporates attenuation effects that are needed to calculate material holdup.

Laminack, Alex↗

NASA NTRS1977

HEAO-A nominal scanning observation schedule

The HEAO-A observatory, scheduled for launch in late June 1977, will spend most of its orbital lifetime in a scanning mode, spining from 0.03 to 0.1 rpm about an axis aligned with the sun. The dates of availability in the scan band are given for a list of 248 X-ray sources. Celestial maps of source locations and scan planes, and examples of the nighttime elevation of available sources are presented. This document is intended to aid ground-based observers in planning coordinated observations with HEAO-A.

Fishman, G. J.↗

NASA NTRS1992

Source polarization effects in an optical fiber fluorosensor

The exact field solution of a step-index profile fiber was used to determine the injection efficiency of a thin-film distribution of polarized sources located in the cladding of an optical fiber. Previous results for random source orientation were confirmed. The behavior of the power efficiency, P(eff), of a polarized distribution of sources was found to be similar to the behavior of a fiber with sources with random orientation. However, for sources polarized in either the x or y direction, P(eff) was found to be more efficient.

Egalon, Claudio O.↗

DOE OSTI2023

Moment tensor reconstruction

A seismic monitoring system includes a plurality of seismic monitors and a processing device operatively coupled to the plurality of seismic monitors. The processing device receives recordings of waveforms of motion detected at the plurality of seismic detectors in a geographic area. The processing device applies the respective recordings to corresponding positions of the seismic detectors in a three-dimensional geological model that describes its elastic attributes and tests a plurality of moment tensors at a plurality of locations. Based on the testing, the processing device determines a globally convergent source location and moment tensor in the three-dimensional model based on the testing.

Petrov, Petr↗

DOE OSTI2026

Radiation asymmetry during shattered pellet and massive gas injection in DIII-D

Infrared thermography of the first wall in DIII-D is used to show the toroidal peaking of plasma radiation during mitigated disruptions with shattered pellet injection (SPI) and with massive gas injection (MGI). During MGI, the radiation peak location is shown to be due to the n = 1 magnetohydrodynamic (MHD) activity and continuously variable in toroidal phase based on externally applied error fields, consistent with previous experimental results. Furthermore, the measured toroidal peaking factor (TPF) is in agreement with the previous estimates based on radiometry, with a total TPF of 1.07 ± 0.05 when integrated over the entire duration of the disruption, and a value of 1.3 ± 0.1 during the thermal quench (TQ). For SPI, the location of the radiation peak is found to be determined predominantly by the particle source location, and thus can be varied on DIII-D by injecting from either of two toroidally separated injectors. 3D non-linear MHD simulations with the NIMROD code support this picture. The localized SPI particle source leads to higher peaking than for MGI, with a total TPF of 1.3 ± 0.1 over the full disruption and a TQ TPF of 1.9 +0.5/−0.3, a value also consistent with the NIMROD simulations. This TQ value is close to or potentially exceeding the allowable limit for the previously planned Be first wall in ITER, pointing to the importance of radiation asymmetries depending on the choice of first wall material.

Physics - Plasma physics↗

NASA NTRS1998

Gamma Ray Bursts-Afterglows and Counterparts

Several breakthrough discoveries were made last year of x-ray, optical and radio afterglows and counterparts to gamma-ray bursts, and a redshift has been associated with at least one of these. These discoveries were made possible by the fast, accurate gamma-ray burst locations of the BeppoSAX satellite. It is now generally believed that the burst sources are at cosmological distances and that they represent the most powerful explosions in the Universe. These observations also open new possibilities for the study of early star formation, the physics of extreme conditions and perhaps even cosmology. This session will concentrate on recent x-ray, optical and radio afterglow observations of gamma-ray bursts, associated redshift measurements, and counterpart observations. Several review and theory talks will also be presented, along with a summary of the astrophysical implications of the observations. There will be additional poster contributions on observations of gamma-ray burst source locations at wavelengths other than gamma rays. Posters are also solicited that describe new observational capabilities for rapid follow-up observations of gamma-ray bursts.

Fishman, Gerald J↗

NASA NTRS1991

A search for embedded young stellar objects in and near the IC 1396 complex

The IRAS data base is used to locate young stellar object candidates in and near the IC 1396 complex located in the Cepheus OB2 association. Co-added survey data are used to identify all sources with a flux density Snu(100) greater than 10 Jy and with Snu(100) greater than Snu(60). The 15 sources located at the positions of globules and dark clouds are further analyzed using the inscan slices to assess the source profiles.

Schwartz, Richard D.↗