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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
NASA NTRS1994

The evaluation of ASOS for the Kennedy Space Center's Shuttle Landing Facility

This report documents the Applied Meteorology Unit's (AMU) evaluation of the effectiveness and utility of the Automated Surface Observing System (ASOS) in terms of spaceflight operations and user requirements. In particular, the evaluation determines which of the Shuttle Landing Facility (SLF) observation requirements can be satisfied by ASOS. This report also includes a summary of ASOS' background, current configuration and specifications, system performance, and the possible concepts of operations for use of ASOS at the SLF. This evaluation stems from a desire by the Air Force to determine if ASOS units could be used to reduce the cost of SLF meteorological observations.

Yersavich, Ann↗
NASA NTRS2018

Fusion of Multiple Low-Resolution NASA Airborne Snow Observatory (ASO) Lidar Data for Forest Vegetation Structure Characterization

Airborne lidar provides timely updated maps for monitoring forest change at high resolution but it has been little used for that purpose due to the scarcity of long-term time-series over a common area. The NASA Jet Propulsion Laboratory Airborne Snow Observatory (ASO) is a landscape-level monitoring system that provides ongoing multi-year remote sensing measurements over mountainous ecosystems to primary quantify snow volume and dynamics. It collects low-resolution lidar data (~1.5 pt/m2) with a nominal weekly frequency up to 12 times a year with measurements that span 2013-2017 over 12 mountain watersheds across the western US that currently face ecological threads. In this work, we present a method to automatically register ASO weekly low-resolution lidar point clouds in order to calculate spatially consistent datasets (~12 pt/m2) adapted to fine scale forestry studies. We test the method using 12 lidar datasets acquired over the Tuolumne River Basin (Sierra Nevada, California) in the spring and summer of 2014. On average, the ASO lidar system provides accurate measurements in terms of geolocation (0.38m and 0.12m for the horizontal and vertical dimension, respectively) but some datasets are biased up to 1.38m and 0.53m, respectively. Our registration method successfully corrected for systematic bias improving the 3D geometry of forest point clouds.

Painter, Thomas H.↗
NASA NTRS1989

Coordination of Advanced Solar Observatory (ASO) Science Working Group (SWG) for the study of instrument accommodation and operational requirements on space station

The objectives are to coordinate the activities of the Science Working Group (SWG) of the Advanced Solar Observatory (ASO) for the study of instruments accommodation and operation requirements on board space station. In order to facilitate the progress of the objective, two conferences were organized, together with two small group discussions.

Wu, S. T.↗
NASA NTRS1989

Advanced Solar Observatory (ASO) accommodations requirements study

Results of an accommodations analysis for the Advanced Solar Observatory on Space Station Freedom are reported. Concepts for the High Resolution Telescope Cluster, Pinhole/Occulter Facility, and High Energy Cluster were developed which can be accommodated on Space Station Freedom. It is shown that workable accommodations concepts are possible. Areas of emphasis for the next stage of engineering development are identified.

Source record↗
NASA NTRS1990

The Advanced Solar Observatory

A conceptual plan for the development of a comprehensive long duration solar space observatory, The Advanced Solar Observatory (ASO) is described. The ASO is intended to provide solar astronomers with the observational power necessary to address fundamental problems relating to the solar convection zone and activity cycle; the thermal and nonthermal processes that control the transport of energy, mass, and magnetic flux in the solar atmosphere; the generation of the solar wind; and the dynamics of the inner heliosphere. The ASO concept encompasses three proposed Space Station-based instrument ensembles: (1) the High Resolution Telescope Cluster, which includes far ultraviolet, extreme ultraviolet, and X-ray telescopes; (2) the Pinhole/Occulter Facility, which includes Fourier transform and coded aperture hard X-ray and gamma ray telescopes and occulted ultraviolet and visible light coronagraphs; and (3) the High Energy Facility, which contains neutron, gamma ray, and low frequency radio spectrometers. Two other facilities, the Orbiting Solar Laboratory, and a package of Global Dynamics Instrumentation, will, with the Space Station ensembles, form a comprehensive capability for solar physics. The scientific program of the ASO, current instrument concepts for the Space Station based ASO instrument ensembles, and plans for their accommodation on the Space Station are described.

Walker, Arthur B. C., Jr.↗
NASA NTRS1988

Automation of surface observations program

At present, surface weather observing methods are still largely manual and labor intensive. Through the nationwide implementation of Automated Surface Observing Systems (ASOS), this situation can be improved. Two ASOS capability levels are planned. The first is a basic-level system which will automatically observe the weather parameters essential for aviation operations and will operate either with or without supplemental contributions by an observer. The second is a more fully automated, stand-alone system which will observe and report the full range of weather parameters and will operate primarily in the unattended mode. Approximately 250 systems are planned by the end of the decade. When deployed, these systems will generate the standard hourly and special long-line transmitted weather observations, as well as provide continuous weather information direct to airport users. Specific ASOS configurations will vary depending upon whether the operation is unattended, minimally attended, or fully attended. The major functions of ASOS are data collection, data processing, product distribution, and system control. The program phases of development, demonstration, production system acquisition, and operational implementation are described.

Short, Steve E.↗
NASA NTRS1974

The ASSESS program documenting and evaluating low cost techniques for conducting research in the flight environment

The Airborne Science Office (ASO) of the Ames Research Center has for 10 years operated an airborne scientific research program in infrared astronomy and other disciplines. The Lear Jet, CV-990, and C-141 flying laboratories are flown by ASO crews, while the major responsibility for defining, developing, and operating the experimental equipment is placed on individual researchers, who have included scientists from many countries. The ASSESS (Airborne Science/Shuttle Experiment Systems Simulation) program consists of two phases: Phase A documents the present management and operational practices of the ASO, and Phase B consists of airborne research missions constrained (for example, by crew confinement) to simulate certain aspects of experimental operations on Shuttle/Spacelab missions. Various parallels between the Airborne Science Program and Spacelab are pointed out and their applications to Spacelab planning are discussed.

Weaver, L. B.↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
NASA NTRS1984

Future scientific missions for STS

The present investigation is concerned with a number of astrophysics missions which are currently being studied by NASA. The missions will be initially flown on the Space Transportation System (STS) and subsequently could be conducted in connection with the utilization of a Space Station on Space Platform. One of the missions involves the use of a large antenna system designed to significantly extend the aperture synthesis technique of earth-based very long baseline interferometry technique (orbiting VLBI). It has been recommended that a space VLBI antenna be launched in low earth orbit during this decade. Another mission considered is related to the Advanced Solar Observatory (ASO). ASO is a long duration space observatory. It will consist of four major instrument groupings including a High Resolution Solar Telescope Cluster (HRSTC), a Pinhole Occulter Facility, a Solar High Energy Facility, and a Solar Low Frequency Radio Facility. Attention is also given to a new program entitled Capabilities for Opportunity Payload Experiments (COPE).

De Sanctis, C. E.↗
NASA NTRS1985

Anticipated scientific return of the Advanced Solar Observatory

The scientific importance of the Advanced Solar Observatory (ASO) is discussed with emphasis on its soft X-ray, XUV, and EUV facilities. The principal achievement expected from the ASO's SXR/XUV and EUV telescope is a greatly improved resolution of the magnetic structure and activity in the transition region and corona. Observations from these facilities, combined with complementary observations of the photosphere and chromosphere from Solar Optical Telescope and of the higher corona from the Pinhole/Occulter Facility, are expectecd to yield significant advances in all major areas of solar physics concerning the causes and effects of solar magnetic fields.

Moore, Ron↗
NASA NTRS1990

Science and application payloads in the 90's

An overview is conducted of the payloads under development for NASA to support space science and the Mission to Planet Earth. The science payloads reviewed include the Controls, Astrophysics, and Structures Experiment in Space (CASES), the Advanced Solar Observatory (ASO), the Laser Atmospheric Wind Sounder (LAWS), and the Lightning Imaging Sensor. The payloads are supported by the Extended Duration Orbiter, the Space Station Freedom, and an infrastructure that includes platforms in polar and geosynchronous orbits. The LAWS payload can provide data for global wind-profile measurements, and the ASO can coordinate solar-physics observations and study solar dynamic processes. The CASES payload is designed to investigate critical control technology for stabilizing and pointing large flexible structures in space for astrophysics missions.

De Sanctis, Carmine E.↗
NASA NTRS2005

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

MCKissick, Burnell T.↗
NASA NTRSDate not supplied

Designing Autonomy into Interfaces for Long-Duration Missions

As NASA develops technologies for long-duration crewed missions, we must understand how communication between ground control teams and astronauts differs from the current dynamic to adapt new concepts for long-duration mission operations. Today, ground control teams support astronauts with immediate availability to answer questions, resolve issues, and manage activities. In the near future, however, extended communication delays during long-duration missions will require astronauts to become more autonomous. As many of the responsibilities shift from the ground control teams to the astronauts on-board, the concept of operations must also change from how it functions today. With increased astronaut autonomy, software tools must be developed that enable efficient completion of mission tasks without increased mental workload. Designing software tools to facilitate crew autonomy requires development teams to know which data will enhance quick decision making while providing necessary context for situational awareness of systems being managed on-board. NASA's Autonomous Systems and Operations (ASO) team is presently developing a software interface tool, EXPRESS (EXpedite the PRocessing of Experiments for Space Station) 2.5 to enable a long-duration crew to schedule activities for and operate autonomous systems. This paper describes details of the integrated human factors approach that drove the design of the elements of the software tool, including self-managed scheduling, constraint-driven planning, autonomous system fault recovery, and recommended troubleshooting actions. Additionally, this paper will chronicle ASO modifications of the user interface after the team's first flight demonstration, how it was based on lessons learned during software development, as well as from crew feedback in order to develop the current version which will be demonstrated on ISS in 2021. In the upcoming ISS demonstration, the astronaut crew will be given scenarios for scheduling and operating autonomous system activities, including off-nominal scenarios and autonomous system recoveries. The demonstration of the EXPRESS 2.5 tool is a step towards improved levels of autonomy as our new journeys take us farther into space.

autonomy↗
NASA NTRSDate not supplied

AMO-EXPRESS-2.5: Crew Autonomy Onboard the International Space Station

NASA is committed to landing American astronauts, including the first woman and the next man, on the Moon by 2024. Currently, the crew cannot take on all functions performed by the ground today, so the future crews will need more automation to reduce the crew workload for future missions. Of significant importance for these missions is the balance between crew autonomy and vehicle automation. The Advanced Exploration Systems (AES) Autonomous Systems and Operations (ASO) Project has been investigating the ability to evaluate crew self-scheduling and activity monitoring for future space missions. The ASO project designed the Autonomous Mission and Operations- EXpedite the PRocessing of Experiments to Space Station Rack-2.5 (AMO-EXPRESS-2.5) payload to evaluate crew self-scheduling and activity monitoring. The AMO-EXPRESS-2.5 builds on the previous AMO-EXPRESS and AMO-EXPRESS-2.0 demonstrations on ISS. The AMO-EXPRESS-2.5 demonstration goals are to prove crew self-scheduling by planning through diagnosing systems expertise, failure detection, procedure recommendation and situational awareness. This paper will describe the development, test and execution results of the AMO-EXPRESS-2.5 demonstration, and will also outline the future planned development and operational efforts to enable autonomy for future deep space manned missions.

Brooke C. Allen↗
NASA NTRS2022

Assimilation of NASA’s Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

he NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Hydrologic modeling↗