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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

May, George A.↗
NASA NTRS2010

Altitude Testing of Large Liquid Propellant Engines

The National Aeronautics and Space Administration entered a new age on January 14, 2004 with President Bush s announcement of the creation the Vision for Space Exploration that will take mankind back to the Moon and on beyond to Mars. In January, 2006, after two years of hard, dedicated labor, engineers within NASA and its contractor workforce decided that the J2X rocket, based on the heritage of the Apollo J2 engine, would be the new engine for the NASA Constellation Ares upper stage vehicle. This engine and vehicle combination would provide assured access to the International Space Station to replace that role played by the Space Shuttle and additionally, would serve as the Earth Departure Stage, to push the Crew Excursion Vehicle out of Earth Orbit and head it on a path for rendezvous with the Moon. Test as you fly, fly as you test was chosen to be the guiding philosophy and a pre-requisite for the engine design, development, test and evaluation program. An exhaustive survey of national test facility assets proved the required capability to test the J2X engine at high altitude for long durations did not exist so therefore, a high altitude/near space environment testing capability would have to be developed. After several agency concepts the A3 High Altitude Testing Facility proposal was selected by the J2X engine program on March 2, 2007 and later confirmed by a broad panel of NASA senior leadership in May 2007. This facility is to be built at NASA s John C. Stennis Space Center located near Gulfport, Mississippi. 30 plus years of Space Shuttle Main Engine development and flight certification testing makes Stennis uniquely suited to support the Vision For Space Exploration Return to the Moon. Propellant handling infrastructure, engine assembly facilities, a trained and dedicated workforce and a broad and varied technical support base will all ensure that the A3 facility will be built on time to support the schedule needs of the J2X engine and the ultimate flight of the first Ares I vehicle. The A3 facility will be able to simulate pre-ignition altitude from sea-level to 100,000 feet and maintain it up to 650 seconds. Additionally the facility will be able to accommodate initial ignition, shutdown and then restart test profiles. A3 will produce up to 5000 lbm/sec of superheated steam utilizing a Chemical Steam generation system. Two separate inline steam ejectors will be used to produce a test cell vacuum to simulate the 100,000 ft required altitude. Operational capability will ensure that the facility can start up and shutdown without producing adverse pressure gradients across the J2X nozzle. The facility will have a modern thrust measurement system for accurate determination of engine performance. The latest advances in data acquisition and control will be incorporated to measure performance parameters during hotfire testing. Provisions are being made in the initial design of the new altitude facility to allow for testing of other, larger engines and potential upper stage launch vehicles that might require vacuum start testing of the engines. The new facility at Stennis Space Center will be complete and ready for hotfire operations in late 2010.

Maynard, Bryon T.↗
NASA NTRS2011

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

May, George A.↗
NASA NTRS2012

NASA's Space Launch System: A Flagship for Exploration Beyond Earth's Orbit

The National Aeronautics and Space Administration s (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is making progress toward delivering a new capability for exploration beyond Earth orbit in an austere economic climate. This fact drives the SLS team to find innovative solutions to the challenges of designing, developing, fielding, and operating the largest rocket in history. To arrive at the current SLS plan, government and industry experts carefully analyzed hundreds of architecture options and arrived at the one clear solution to stringent requirements for safety, affordability, and sustainability over the decades that the rocket will be in operation. This paper will explore ways to fit this major development within the funding guidelines by using existing engine assets and hardware now in testing to meet a first launch by 2017. It will explain the SLS Program s long-range plan to keep the budget within bounds, yet evolve the 70 metric ton (t) initial lift capability to 130-t lift capability after the first two flights. To achieve the evolved configuration, advanced technologies must offer appropriate return on investment to be selected through a competitive process. For context, the SLS will be larger than the Saturn V that took 12 men on 6 trips for a total of 11 days on the lunar surface over 4 decades ago. Astronauts train for long-duration voyages on the International Space Station, but have not had transportation to go beyond Earth orbit in modern times, until now. NASA is refining its mission manifest, guided by U.S. Space Policy and the Global Exploration Roadmap. Launching the Orion Multi-Purpose Cargo Vehicle s first autonomous certification flight in 2017, followed by a crewed flight in 2021, the SLS will offer a robust way to transport international crews and the air, water, food, and equipment they need for extended trips to asteroids, Lagrange Points, and Mars. In addition, the SLS will accommodate high-priority science experiments. SLS affordability initiatives include streamlining interfaces, applying risk-based insight into contracted work, centralizing systems engineering and integration, and nurturing a learning culture that continually benchmarks its performance against successful ventures. As this paper will explain, the SLS is making measurable progress toward becoming a global infrastructure asset for robotic and human scouts of all nations by harnessing business and technological innovations to deliver sustainable solutions for space exploration.

May, Todd↗
NASA NTRS2012

NASA's Space Launch System: A Flagship for Exploration Beyond Earth's Orbit

The National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is making progress toward delivering a new capability for exploration beyond Earth orbit in an austere economic climate. This fact drives the SLS team to find innovative solutions to the challenges of designing, developing, fielding, and operating the largest rocket in history. To arrive at the current SLS plan, government and industry experts carefully analyzed hundreds of architecture options and arrived at the one clear solution to stringent requirements for safety, affordability, and sustainability over the decades that the rocket will be in operation. This paper will explore ways to fit this major development within the funding guidelines by using existing engine assets and hardware now in testing to meet a first launch by 2017. It will explain the SLS Program s long-range plan to keep the budget within bounds, yet evolve the 70 metric ton (t) initial lift capability to 130-t lift capability after the first two flights. To achieve the evolved configuration, advanced technologies must offer appropriate return on investment to be selected through a competitive process. For context, the SLS will be larger than the Saturn V that took 12 men on 6 trips for a total of 11 days on the lunar surface over 4 decades ago. Astronauts train for long-duration voyages on the International Space Station, but have not had transportation to go beyond Earth orbit in modern times, until now. NASA is refining its mission manifest, guided by U.S. Space Policy and the Global Exploration Roadmap. Launching the Orion Multi-Purpose Crew Vehicle s (MPCV s) first autonomous certification flight in 2017, followed by a crewed flight in 2021, the SLS will offer a robust way to transport international crews and the air, water, food, and equipment they need for extended trips to asteroids, Lagrange Points, and Mars. In addition, the SLS will accommodate high-priority science experiments. SLS affordability initiatives include streamlining interfaces, applying risk-based insight into contracted work, centralizing systems engineering and integration, and nurturing a learning culture that continually benchmarks its performance against successful ventures. As this paper will explain, the SLS is making measurable progress toward becoming a global infrastructure asset for robotic and human scouts of all nations by harnessing business and technological innovations to deliver sustainable solutions for space exploration.

May, Todd A.↗
NASA NTRS2014

Simscape Modeling Verification in the Simulink Development Environment

The purpose of the Simulation Product Group of the Control and Data Systems division of the NASA Engineering branch at Kennedy Space Center is to provide a realtime model and simulation of the Ground Subsystems participating in vehicle launching activities. The simulation software is part of the Spaceport Command and Control System (SCCS) and is designed to support integrated launch operation software verification, and console operator training. Using Mathworks Simulink tools, modeling engineers currently build models from the custom-built blocks to accurately represent ground hardware. This is time consuming and costly due to required rigorous testing and peer reviews to be conducted for each custom-built block. Using Mathworks Simscape tools, modeling time can be reduced since there would be no custom-code developed. After careful research, the group came to the conclusion it is feasible to use Simscape's blocks in MatLab's Simulink. My project this fall was to verify the accuracy of the Crew Access Arm model developed using Simscape tools running in the Simulink development environment.

SIMSCAPE↗
NASA NTRS2016

NASA Human System Risk Assessment Process

NASA utilizes an evidence based system to perform risk assessments for the human system for spaceflight missions. The center of this process is the multi-disciplinary Human System Risk Board (HSRB). The HSRB is chartered from the Chief Health and Medical Officer (OCHMO) at NASA Headquarters. The HSRB reviews all human system risks via an established comprehensive risk and configuration management plan based on a project management approach. The HSRB facilitates the integration of human research (terrestrial and spaceflight), medical operations, occupational surveillance, systems engineering and many other disciplines in a comprehensive review of human system risks. The HSRB considers all factors that influence human risk. These factors include pre-mission considerations such as screening criteria, training, age, sex, and physiological condition. In mission factors such as available countermeasures, mission duration and location and post mission factors such as time to return to baseline (reconditioning), post mission health screening, and available treatments. All of the factors influence the total risk assessment for each human risk. The HSRB performed a comprehensive review of all potential inflight medical conditions and events and over the course of several reviews consolidated the number of human system risks to 30, where the greatest emphasis is placed for investing program dollars for risk mitigation. The HSRB considers all available evidence from human research and, medical operations and occupational surveillance in assessing the risks for appropriate mitigation and future work. All applicable DRMs (low earth orbit for 6 and 12 months, deep space for 30 days and 1 year, a lunar mission for 1 year, and a planetary mission for 3 years) are considered as human system risks are modified by the hazards associated with space flight such as microgravity, exposure to radiation, distance from the earth, isolation and a closed environment. Each risk has a summary two-page assessment representing the state of knowledge/evidence of that risk, available risk mitigations, traceability to the Space Flight Human System Standards (SFHSS) and program requirements, and future work required. These data then can drive coordinated budgets across the Human Research Program, the International Space Station, Crew Health and Safety and Advanced Exploration System budgets to provide the most economical and timely mitigations. The risk assessments were completed for the 6 DRMs and serve as the baseline for which subsequent research and technology development and crew health care portfolios can be assessed. The HSRB reviews each risk at least annually or when new evidence/information is available that adds to the body of evidence. The current status of each risk can be reported to program management for operations, budget reviews and general oversight of the human system risk management program.

Francisco, D.↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRS1962

SIMULATION EQUIPMENT USED IN THE TRAINING OF ASTRONAUTS AND FLIGHT CONTROL CREWS IN PROJECT MERCURY

At the beginning of the Mercury Program it was realized that the entire preparation of the man for piloting the spacecraft would have to be done by use of simulation techniques. It was also realized that no single all-inclusive flight simulator could provide the astronaut with practice in all phases of the mission in a sufficiently realistic environment. To determine the simulation needs, the Mercury-Atlas flight was broken down into areas of astronaut mission responsibility and into areas of physiological and psychological factors affecting the astronaut. A study was made of existing and proposed devices to determine requirements, suitability, feasibility, and availability of these devices. The simulators and devices finally selected are listed in Figure 1. Omitted from this list are several devices that are not actually simulators, such as mockups and visual devices} disorientation devices such as the one at Pensacola, and airplane 0 g familiarization flights. The listings in Figure 1 are not by order of importance or time availability but rather in groupings of fixed-based and dynamic or moving-based simulators.

Training equipment↗
NASA NTRS2006

Inferring Gear Damage from Oil-Debris and Vibration Data

A system for real-time detection of surface-fatigue-pitting damage to gears for use in a helicopter transmission is based on fuzzy-logic used to fuse data from sensors that measure oil-borne debris, referred to as "oil debris" in the article, and vibration signatures. A system to detect helicopter-transmission gear damage is beneficial because the power train of a helicopter is essential for propulsion, lift, and maneuvering, hence, the integrity of the transmission is critical to helicopter safety. To enable detection of an impending transmission failure, an ideal diagnostic system should provide real-time monitoring of the "health" of the transmission, be capable of a high level of reliable detection (with minimization of false alarms), and provide human users with clear information on the health of the system without making it necessary for them to interpret large amounts of sensor data.

Dempsey, Paula↗
NASA NTRS1993

Visual display aid for orbital maneuvering - Experimental evaluation

An interactive proximity operations planning system, which allows on-site planning of fuel-efficient, multiburn maneuvers in a potential multispacecraft environment, has been experimentally evaluated. An experiment has been carried out in which nonastronaut operators with brief initial training were required to plan a trajectory to retrieve an object accidentally separated from a dual-keel Space Station, for a variety of different orbital situations. The experiments have shown that these operators were able to plan workable trajectories, satisfying a number of operational constraints. Fuel use and planning time were strongly correlated, both with the angle at which the object was separated and with the existence of spatial constraints. Planning behavior was found to be strongly operator-dependent. This finding calls for the need for standardizing planning strategies through operator training or the use of semiautomated planning schemes.

Grunwald, Arthur J.↗
NASA NTRS2017

Coordinated Analysis 101: A Joint Training Session Sponsored by LPI and ARES/JSC

The Lunar and Planetary Institute (LPI) and the Astromaterials Research and Exploration Science (ARES) Division, part of the Exploration Integration and Science Directorate at NASA Johnson Space Center (JSC), co-sponsored a training session in November 2016 for four early-career scientists in the techniques of coordinated analysis. Coordinated analysis refers to the approach of systematically performing high-resolution and -precision analytical studies on astromaterials, particularly the very small particles typical of recent and near-future sample return missions such as Stardust, Hayabusa, Hayabusa2, and OSIRIS-REx. A series of successive analytical steps is chosen to be performed on the same particle, as opposed to separate subsections of a sample, in such a way that the initial steps do not compromise the results from later steps in the sequence. The data from the entire series can then be integrated for these individual specimens, revealing important in-sights obtainable no other way. ARES/JSC scientists have played a leading role in the development and application of this approach for many years. Because the coming years will bring new sample collections from these and other planned NASA and international exploration missions, it is timely to begin disseminating specialized techniques for the study of small and precious astromaterial samples. As part of the Cooperative Agreement between NASA and the LPI, this training workshop was intended as the first in a series of similar training exercises that the two organizations will jointly sponsor in the coming years. These workshops will span the range of analytical capabilities and sample types available at ARES/JSC in the Astromaterials Research and Astro-materials Acquisition and Curation Offices. Here we summarize the activities and participants in this initial training.

Draper, D. S.↗
NASA NTRSDate not supplied

Astronauts' Performance: The Retention and Transfer of Training

The space environment imposes on the astronaut crew significant physiological, psycho-social, and cognitive loads that can not be replicated on the ground. These loads likely impact crew performance. To date, no systematic data collection has taken place to understand the effects of such loads on crew members’ ability to retain trained knowledge and skills, and to transfer such knowledge and skills to novel situations. The research described here was originally designed to be the first such study to systematically collect data on the effects of long duration space flight on training retention and transfer. Because current theories of retention and transfer are based on results obtained in university laboratories using undergraduate students as research participants, and because crew time in space is very expensive, this study was designed to compare the performance of 4 groups of subjects: crew members in space, crew members on the ground, crew-like subjects, and university undergraduate students. Results from the ground-phase of the study reported here demonstrate that crew members’ performance under cognitive load can not be predicted from the performance of university undergraduate students. It is still an open question the extent to which crew members’ cognitive performance in space can be predicted from the performance of crew members on the ground.

training↗
NASA NTRS2014

NASA 2014 The Hyperspectral Infrared Imager (HyspIRI) - Science Impact of Deploying Instruments on Separate Platforms

The Hyperspectral Infrared Imager (HyspIRI) mission was recommended for implementation by the 2007 report from the U.S. National Research Council Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, also known as the Earth Science Decadal Survey. The HyspIRI mission is science driven and will address a set of science questions identified by the Decadal Survey and broader science community. The mission includes a visible shortwave infrared (VSWIR) imaging spectrometer, a multispectral thermal infrared (TIR) imager and an intelligent payload module (IPM). The IPM enables on-board processing and direct broadcast for those applications with short latency requirements. The science questions are organized as VSWIR-only, TIR-only and Combined science questions, the latter requiring data from both instruments. In order to prepare for the mission NASA is undertaking pre-phase A studies to determine the optimum mission implementation, in particular, cost and risk reduction activities. Each year the HyspIRI project is provided with feedback from NASA Headquarters on the pre-phase A activities in the form of a guidance letter which outlines the work that should be undertaken the subsequent year. The 2013 guidance letter included a recommendation to undertake a study to determine the science impact of deploying the instruments from separate spacecraft in sun synchronous orbits with various time separations and deploying both instruments on the International Space Station (ISS). This report summarizes the results from that study. The approach taken was to evaluate the impact on the combined science questions of time separations between the VSWIR and TIR data of <3 minutes, <1 week and a few months as well as deploying both instruments on the ISS. Note the impact was only evaluated for the combined science questions which require data from both instruments (VSWIR and TIR). The study concluded the impact of a separation of <3 minutes was minimal, e.g. if the instruments were on separate platforms that followed each other in a train. The impact of a separation of <1 week was strongly dependent on the question that was being addressed with no impact for some questions and a severe impact for others. The impact of a time separation of several months was severe and in many cases it was no longer possible to answer the sub-question. The impact of deploying the instruments on the ISS which is in a precessive (non-sun synchronous) orbit was also very question dependent, in some cases it was possible to go beyond the original question, e.g. to examine the impact of the diurnal cycle, whereas in other cases the question could not be addressed for example if the question required observations from the polar regions. As part of the study, the participants were asked to estimate, as a percentage, how completely a given sub-question could be answered with 100% indicating the question could be completely answered. These estimations should be treated with caution but nonetheless can be useful in assessing the impact. Averaging the estimates for each of the combined questions the results indicate that 97% of the questions could be answered with a separation of < 3 minutes. With a separation of < 1 week, 67% of the questions could be answered and with a separation of several months only 21% of the questions could be answered.

Turpie, Kevin↗
NASA NTRS1995

Descent advisor preliminary field test

A field test of the Descent Advisor (DA) automation tool was conducted at the Denver Air Route Traffic Control Center in September 1994. DA is being developed to assist Center controllers in the efficient management and control of arrival traffic. DA generates advisories, based on trajectory predictions, to achieve accurate meter-fix arrival times in a fuel efficient manner while assisting the controller with the prediction and resolution of potential conflicts. The test objectives were to evaluate the accuracy of DA trajectory predictions for conventional- and flight-management-system-equipped jet transports, to identify significant sources of trajectory prediction error, and to investigate procedural and training issues (both air and ground) associated with DA operations. Various commercial aircraft (97 flights total) and a Boeing 737-100 research aircraft participated in the test. Preliminary results from the primary test set of 24 commercial flights indicate a mean DA arrival time prediction error of 2.4 sec late with a standard deviation of 13.1 sec. This paper describes the field test and presents preliminary results for the commercial flights.

Green, Steven M.↗