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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Kestrel Results at Liftoff Conditions for a Space Launch System Configuration in Proximity to the Launch Tower

Aerodynamic data books for Space Launch System vehicles require databases for the integrated forces and moments and section loads during liftoff and transition to the ascent phase of flight. While the force and moment database can be generated from wind tunnel results, computational analyses are necessary to provide the extensive surface information required to generate proper lineloads. Of the two flight regimes, the liftoff problem is the more costly and complex situation to simulate, as it requires modeling of the vehicle in proximity to the launch tower. The effects of massive separation on the leeward pressure fields of both the tower and vehicle are not well captured with RANS methods, necessitating the use of more advanced methods, such as Delayed Detached Eddy Simulation, in conjunction with computational grids sufficiently refined to resolve the wakes. Details on the computational setup for employing the Kestrel flow solver to address the liftoff problem are presented. The methodology involves the use of independent unstructured near-body grids for the vehicle and the tower, overset by a solution adaptive Cartesian off-body grid. Results from the simulations are compared to experimental results from a test in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel.

Computational fluid dynamics↗
NASA NTRSDate not supplied

Pre-Launch Performance Trending of Joint Polar Satellite System (JPSS) Advanced Technology Microwave Sounder (ATMS)

The Advanced Technology Microwave Sounder (ATMS) microwave radiometer instrument is utilized on-board NOAA’s Joint Polar Satellite System (JPSS) fleet of spacecraft to perform temperature and water vapor soundings of Earth’s atmosphere. Consisting of 22 channels over a frequency range from 22 to 183 GHz, ATMS provides high-impact observations for numerical weather prediction (NWP) models. A general description of the ATMS instrument is discussed in [1]. There are five ATMS flight units in the polar-orbiting JPSS program; three are currently on-orbit and two are pending launch. The first ATMS was flown on the Suomi National Polar-orbiting Partnership (SNPP) satellite, launched in 2011. The second ATMS was launched on the NOAA-20 (previously JPSS-1) satellite in 2017. The third ATMS was launched on the NOAA-21 (previously JPSS-2) satellite in 2022. The SNPP and NOAA-20 ATMS units are operational. The NOAA-21 ATMS unit is completing on-orbit commissioning and checkout, having achieved provisional maturity status in December 2022 with validated maturity expected in May 2023 [2]. The fourth and fifth ATMS units are planned for the JPSS-3 (launch ~2028) and JPSS-4 (launch ~2032) satellites [3]. This paper will focus on trending performance characteristics of each JPSS ATMS build from the pre-launch activities. Pre-launch trending of some parameters have been previously published up to the JPSS-3 mission [4][5]. This paper differs from and expands the scope of the prior work as it will include all five of the JPSS ATMS builds. The entire suite of JPSS ATMS units have completed their pre-ship instrument-level I&T and verification activities. These activities include a radiometric performance characterization of each instrument. In addition to comparing the performance across builds, the performance will also be compared to requirements and specifications where applicable. The on-orbit performance of the launched units will be excluded from the scope of this paper in order to focus on evaluations that are common across all builds. The post-launch performance of SNPP ATMS is detailed in [1][6][7][8]. The post-launch performance of NOAA-20 ATMS is detailed in [6][8][9]. The pre-launch characterization of the ATMS occurs at both subassembly-level and instrument-level testing. The antenna subsystem is tested at Northrop Grumman’s Compact Antenna Test Range (CATR) in Azusa, CA [9]. This testing characterizes the antenna pattern and the pointing performance of the scan drive mechanism and antenna subsystem. Trended parameters from this evaluation will include beam pointing accuracy, beamwidth, and beam efficiency. These parameters are captured in each instrument’s Calibration Data Book [10]. Instrument-level radiometric performance evaluation is primarily done during thermal vacuum (TVAC) calibration testing at Northrop Grumman’s Azusa, CA facility [9]. A general description of the calibration activities is presented in [1][9]. The testing involves inferring a scene target brightness temperature (TB) and comparing it to the actual scene TB while the instrument is at flight-like temperature and pressure environmental conditions. Trended parameters from this activity will include Noise Equivalent Delta Temperature (NEDT), nonlinearity, radiometric accuracy, gain stability, striping, and inter-channel noise correlation. The trending evaluation will allow for a direct comparison of the ATMS performance across builds. The paper will highlight observed performance improvements.

Edward J Kim↗
NASA NTRS1981

LANDSAT-D data format control book. Volume 6, appendix G: GSFC HDT-AM inventory tape (GHIT-AM)

The data format specifications of the Goddard HDT inventory tapes (GHITS), which accompany shipments of archival digital multispectral scanner image data (HDT-AM tapes), are defined. The GHIT is a nine-track, 1600-BPI tape which conforms to the ANSI standard and serves as an inventory and description of the image data included in the shipment. The archival MSS tapes (HDT-AMs) contain radiometrically corrected but geometrically uncorrected image data plus certain ancillary data necessary to perform the geometric corrections.

Source record↗
NASA NTRS1981

LANDSAT-D data format control book. Volume 6, appendix K: Unprocessed multispectral scanner high density tape (HDT-RM/HDT-GM)

Unprocessed MSS data which is recorded on HDT-RM (a 28 track, high density tape) and on HDT-GM (a 14 track, nonbias recorded, high density tape) are inputs for the LANDSAT 4 data management system. All MSS data initially recorded on HDT-GM are copied to HDT-RM prior to processing. This specification establishes the requirements for the format of the LANDSAT D HDT-RM/HDT-GM.

Andersen, K. E.↗
NASA NTRS1982

LANDSAT-D data format control book. Volume 6, appendix A: Partially processed thematic mapper High Density Tape (HDT-AT)

One of the outputs of the data management system being developed to provide a variety of standard image products from the thematic mapper and the multispectral band scanners on LANDSAT 4, is the partially processed TM data (radiometric corrections applied and geometric correction matrices for two projections appended) which is recorded on a 28-track high density tape. Specifications are presented for the format of the recorded data as well as for the time code and the major and minor frames of the tape. Major frame types, formats, and field definitions are included.

Jai, A.↗
NASA NTRS1982

LANDSAT-D data format control book. Volume 2: Telemetry

The formats used for the transmission of LANDSAT-D and LANDSAT-D Prime spacecraft telemetry data through either the TDRS/GSTDN via the NASCOM Network to the CSF are described as well as the telemetry flow from the command and data handling subsystem, a telemetry list and telemetry matrix assignment for the mission and engineering formats. The on-board computer (OBC) controlled format and the dwell format are also discussed. The OBCs contribution to telemetry, and the format of the reports, are covered. The high rate data channel includes the payload correction data format, the narrowband tape recorder and the OBC dump formats.

Talipsky, R.↗
NASA NTRS1981

LANDSAT-D data format control book. Volume 6: (Products)

Four basic product types are generated from the raw thematic mapper (TM) and multispectral scanner (MSS) payload data by the NASA GSFC LANDSAT 4 data management system: (1) unprocessed data (raw sensor data); (2) partially processed data, which consists of radiometrically corrected sensor data with geometric correction information appended; (3) fully processed data, which consists of radiometrically and geometrically corrected sensor data; and (4) inventory data which consists of summary information about product types 2 and 3. High density digital recorder formatting and the radiometric correction process are described. Geometric correction information is included.

Kabat, F.↗
NASA NTRS1981

LANDSAT-D data format control book. Volume 5: (Payload)

The LANDSAT-D flight segment payload is the thematic mapper and the multispectral scanner. Narrative and visual descriptions of the LANDSAT-D payload data handling hardware and data flow paths from the sensing instruments through to the GSFC LANDSAT-D data management system are provided. Key subsystems are examined.

Andrew, H.↗
NASA NTRS1977

Space station systems analysis study. Part 2, volume 3: Appendixes, Book 2: Supporting data (7 through 18)

Topics discussed include: (1) design considerations for a MARS sample return laboratory module for space station investigations; (2) crew productivity as a function of work shift arrangement; (3) preliminary analysis of the local logistics problem on the space construction base; (4) mission hardware construction operational flows and timelines; (5) orbit transfer vehicle concept definition; (6) summary of results and findings of space processing working review; (7) crew and habitability subsystem (option L); (8) habitability subsystem considerations for shuttle tended option L; (9) orbiter utilization in manned sortie missions; (10) considerations in definition of space construction base standard module configuration (option L); (11) guidance, control, and navigation subsystems; and (12) system and design tradeoffs.

Source record↗
NASA NTRS2008

Famine Early Warning Systems and Remote Sensing Data

This book describes the interdisciplinary work of USAID's Famine Early Warning System Network (FEWS NET) and its influence on how food security crises are identified, documented and the kind of responses that result. The book describes FEWS NET's systems and methods for using satellite remote sensing to identify and describe how biophysical hazards impact the lives and livelihoods of the population where they occur. It presents several illustrative case studies that will demonstrate the integration of both physical and social science disciplines in its work. FEWS NET s operational needs have driven science in biophysical remote sensing applications through its collaboration with the US Geological Survey, the National Aeronautics and Space Administration, National Oceanographic and Atmospheric Administration, and US Department of Agriculture, as well as methodologies in the social science domain through its support of the US Agency for International Development, UNWorld Food Program and numerous international non-governmental organizations such as Save the Children, Oxfam and others. Because FEWS NET is an organization that must provide a global picture of food insecurity to decision makers, the information it relies on are by necessity observable and able to be documented. Thus many aspects of traditional livelihood analysis, for example, cannot be used by FEWS NET as they rely upon relationships, and ways of expressing power and knowledge at the local scale that cannot be easily scaled up to express variations in access to food at a community level. The book focuses on the ways that remote sensing information is transformed into an understanding of the actions that must be taken in order to ensure that lives and livelihoods are protected, including describing the remote sensing observations and models needed to identify hazards and the information gathering requirements and analytical frameworks needed to understand their impact. Its focus is primarily analysis conducted in Africa, but also touches upon FEWS NET s work in Central America, Haiti and Afghanistan. As an organization that seeks to integrate social and physical science methodologies and strategies into its work on a daily basis, it is a fascinating and rich example of interdisciplinary knowledge generation and innovation.

Brown, Molly E.↗