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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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NASA NTRS2018

Thermal Protection Systems Technology Transfer from Apollo and Space Shuttle to the Orion Program

This paper describes how the Orion program is utilizing the Thermal Protection System (TPS) experience from the Apollo and Space Shuttle programs to reduce program risk and improve affordability to meet NASA's future manned exploration missions. The Orion program successfully completed the Exploration Flight Test (EFT-1) mission in 2014 and is currently assembling, integrating, and testing the next spacecraft for the Exploration Mission (EM-1) to meet the flight test objectives of an unmanned orbital mission to the moon and return to earth in 2019. The Orion spacecraft production operations are located in the Neil Armstrong Operations and Checkout (O&C) facility at the Kennedy Space Center (KSC) providing an affordable and seamless delivery approach of vehicles directly to the launch site eliminating spacecraft transportation and additional checkout testing. Innovative vehicle design, manufacturing and test operations approaches are maturing and evolving with each Orion vehicle build to support the challenging NASA exploration mission requirements beyond Low Earth Orbit (LEO) while reducing program cost and schedule impacts. An example of Orion's evolution is the incorporation of an improved heat shield design, assembly and testing approach to meet the higher re-entry velocities for a lunar return for the EM-1 mission. The EFT- 1 heat shield was based on the Apollo heat shield manufacturing processes and was assembled at a supplier location and then transported to KSC for final integration. The EM-1 heat shield is now manufactured, assembled, tested, and installed into the spacecraft at the O&C facility reducing program cost and production schedules. The transition of the Space Shuttle TPS capabilities has enabled Orion to provide a human rated capsule design using proven materials and processes established over years of orbiter re-entry missions. The Orion Crew Module (CM) TPS configuration is derived from the Apollo CM approach utilizing improved materials and processes developed from the Space Shuttle program. The Orion EFT-1 heat shield utilized the Avcoat ablative material from Apollo which was injected into a honeycomb substrate and has been updated for EM-1 incorporating a block configuration bonded to a composite shell structure. This approach utilizes the proven Avcoat material for the heat shield ablator and is utilizing derived bonding and inspection methods and techniques from the Space Shuttle tile experience. The Orion back shell TPS configuration is based on Space Shuttle tile designs using proven tile materials and coatings. The Orion forward bay cover utilizes the high temperature tiles similar to the back shell tiles and low temperature blankets derived from the Space Shuttle program reducing weight impacts. Space Shuttle Multi-Layer Insulation (MLI) is installed in the Orion capsule to control the interior temperature environment providing a light weight design. These TPS design approaches have performed successfully on the Orion first flight test on EFT-1 and are incorporated in the configuration for the next flight test for EM-1. Completion of these two flight tests will certify the TPS for the Orion program for human rated exploration missions and has reduced the development cost to the Orion program. In addition to transitioning the Space Shuttle TPS design configurations to Orion, the supporting manufacturing infrastructure, manufacturing processes, and inspection methods are also incorporated into the Orion assembly operations at KSC. This has avoided significant startup schedule and costs impacts of new capabilities and development of support operations necessary to fabricate, install, inspect, and validate the TPS installations for the Orion spacecraft. The Thermal Protection System Facility (TPSF) which supported the Space Shuttle program provides the heat shield Avcoat blocks and the back shell AETB-8 tiles for Orion program. Extending the existing Space Shuttle TPS operations at KSC provides onsite support to the Orion spacecraft assembly operations. A significant benefit to the Orion program is the transition of the Space Shuttle technician work force at KSC. This highly skilled workforce was able to transition to the Orion program and immediately support the TPS installation operations. They brought with them the human rated manufacturing and assembly operations culture that was critical to the Space Shuttle success avoiding the retraining cost and schedule impacts of a new workforce to Orion. The technician skills certification program from the Space Shuttle was found to be compatible with the Lockheed Martin technician certification programs enabling a seamless training process utilizing process similarity avoiding the cost of retraining the work force to Orion standards. The existing workforce is certificated for multiple processes enabling reassignment of technicians to other tasks in the Orion AI&P operations reducing the overall touch labor manpower requirements. Additional benefits of using the Space Shuttle workforce is the incorporation of the lessons learned from Space Shuttle processes to improve the Orion TPS processes. This has resulted in a continuous evolution of TPS processes to improve the producibility and reduce the program cost for the TPS for the Orion program. Transitioning the Apollo and Space Shuttle TPS designs, processes, and technician workforce has been instrumental in enabling Orion to successfully meet the program challenges for NASA's exploration missions of the future.

Stewart, Michael↗
NASA NTRS1986

An application of adaptive learning to malfunction recovery

A self-organizing controller is developed for a simplified two-dimensional aircraft model. The Controller learns how to pilot the aircraft through a navigational mission without exceeding pre-established position and velocity limits. The controller pilots the aircraft by activating one of eight directional actuators at all times. By continually monitoring the aircraft's position and velocity with respect to the mission, the controller progressively modifies its decision rules to improve the aircraft's performance. When the controller has learned how to pilot the aircraft, two actuators fail permanently. Despite this malfunction, the controller regains proficiency at its original task. The experimental results reported show the controller's capabilities for self-organizing control, learning, and malfunction recovery.

Cruz, R. E.↗
NASA NTRS2003

Web-Based Instruction and Learning: Responding to K-14 Customer Needs

A follow-up working conference was held at Lewis Research Center (now Glenn Research Center) on September 23-25, 1997, to continue discussing issues related to the development of Web-based education materials for the K-14 community. The conference continued the collaboration among the NASA aerospace technology Centers (Ames, Dryden, Langley, and Lewis [now Glenn]), NASA Headquarters, the University of Idaho and the Pennsylvania State University. The conference consisted of presentations by the Aeronautics Cooperative Agreement teams, and working sessions that addressed issues related to the conference theme, responding to the K-14 customers needs. The group identified the most significant issues by consensus. The issues addressed were: classroom access, World Wide Web resources, teacher training, different teaching and learning styles, interactivity, and education standards. The working sessions produced observations and recommendations in each of these areas in order to work toward the goal of making NASA sponsored Web-based educational resources useful to teachers and students.

McCarthy, Marianne↗
NASA NTRS2006

Staffing the ISS Control Centers: Lessons Learned from Long-Duration Human Space Flight

The International Space Station (ISS) has been in operation with a permanent human presence in space for over five years, and plans for continued operations stretch ten years into the future. Ground control and support operations are, likewise, a 15-year enterprise. This long-term, 24-hour per day, 7 day per week support has presented numerous challenges in the areas of ground crew training, initial and continued certification, and console staffing. The Mission Control Center in Houston, Texas and the Payload Operations Center in Huntsville, Alabama have both tackled these challenges, with similar, yet distinct, approaches. This paper describes the evolution of the staffing and training policies of both control centers in a chronological progression. The relative merits and shortcomings of the various policies employed are discussed and a summary of "lessons learned" is presented. Finally, recommendations are made as best practices for future long-term space missions.

Olsen, Carrie D.↗
NASA NTRS2016

Human Flight to Lunar and Beyond - Re-Learning Operations Paradigms

For the first time since the Apollo era, NASA is planning on sending astronauts on flights beyond LEO. The Human Space Flight (HSF) program started with a successful initial flight in Earth orbit, in December 2014. The program will continue with two Exploration Missions (EM): EM-1 will be unmanned and EM-2, carrying astronauts, will follow. NASA established a multi-center team to address the communications, and related tacking/navigation needs. This paper will focus on the lessons learned by the team designing the architecture and operations for the missions. Many of these Beyond Earth Orbit lessons had to be re-learned, as the HSF program has operated for many years in Earth orbit. Unlike the Apollo missions that were largely tracked by a dedicated ground network, the HSF planned missions will be tracked (at distances beyond GEO) by the DSN, a network that mostly serves robotic missions. There have been surprising challenges to the DSN as unique modern human spaceflight needs stretch the experience base beyond that of tracking robotic missions in deep space. Close interaction between the DSN and the HSF community to understand the unique needs (e.g. 2-way voice) resulted in a Concept of Operations (ConOps) that leverages both the deep space robotic and the Human LEO experiences. Several examples will be used to highlight the unique challenges the team faced in establishing the communications and tracking capabilities for HSF missions beyond Earth Orbit, including: Navigation. At LEO, HSF missions can rely on GPS devices for orbit determination. For Lunar-and-beyond HSF missions, techniques such as precision 2-way and 3-way Doppler and ranging, Delta-Difference-of-range, and eventually possibly on-board navigation will be used. At the same time, HSF presents a challenge to navigators, beyond those presented by robotic missions - navigating a dynamic/"noisy" spacecraft. Impact of latency - the delay associated with Round-Trip-Light-Time (RTLT). Imagine trying to have a 2-way discussion (audio or video) with an astronaut, with a 2-3 sec or more delay inserted (for lunar distances) or 20 minutes delay (for Mars distances). Balanced communications link. For robotic missions, there has been a heavy emphasis on higher downlink data rates, e.g. bringing back science data. Higher uplink data rates were of secondary importance, as uplink was used only to send commands (and occasionally small files) to the spacecraft. The ratio of downlink-to-uplink data rates was often 10:1 or more. For HSF, a continuous forward link is established and rates for uplink and downlink are more similar.

Kenny, Edward (Ted)↗
NASA NTRSDate not supplied

Energy-Optimized Path Planning for Uas in Varying Winds Via Reinforcement Learning

In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.

trajectory planning↗
NASA NTRS1973

Design and performance of a large vocabulary discrete word recognition system. Volume 1: Technical report

The development, construction, and test of a 100-word vocabulary near real time word recognition system are reported. Included are reasonable replacement of any one or all 100 words in the vocabulary, rapid learning of a new speaker, storage and retrieval of training sets, verbal or manual single word deletion, continuous adaptation with verbal or manual error correction, on-line verification of vocabulary as spoken, system modes selectable via verification display keyboard, relationship of classified word to neighboring word, and a versatile input/output interface to accommodate a variety of applications.

Source record↗
NASA NTRS2020

Barefoot Rover: a Sensor-Infused Rover Wheel Demonstrating In-Situ Engineering and Science Extractions using Machine Learning

In this work, we demonstrate an instrumented wheel concept which utilizes a 2D pressure grid, an electrochemical impedance spectroscopy (EIS) sensor and machine learning (ML) to extract meaningful metrics from the interaction between the wheel and surface terrain. These include continuous slip/skid estimation, balance, and sharpness for engineering applications. Estimates of surface hydration, texture, terrain patterns, and regolith physical properties such as cohesion and angle of internal friction are additionally calculated for science applications. Traditional systems rely on post-processing of visual images and vehicle telemetry to estimate these metrics. Through in-situ sensing, these metrics can be calculated in near real time and made available to onboard science and engineering autonomy applications. This work aims to provide a deployable system for future planetary exploration missions to increase science and engineering capabilities through increased knowledge of the terrain.

Marchetti, Yuliya↗
NASA NTRS2011

Cable Tester Box

Cables are very important electrical devices that carry power and signals across multiple instruments. Any fault in a cable can easily result in a catastrophic outcome. Therefore, verifying that all cables are built to spec is a very important part of Electrical Integration Procedures. Currently, there are two methods used in lab for verifying cable connectivity. (1) Using a Break-Out Box and an ohmmeter this method is time-consuming but effective for custom cables and (2) Commercial Automated Cable Tester Boxes this method is fast, but to test custom cables often requires pre-programmed configuration files, and cables used on spacecraft are often uniquely designed for specific purposes. The idea is to develop a semi-automatic continuity tester that reduces human effort in cable testing, speeds up the electrical integration process, and ensures system safety. The JPL-Cable Tester Box is developed to check every single possible electrical connection in a cable in parallel. This system indicates connectivity through LED (light emitting diode) circuits. Users can choose to test any pin/shell (test node) with a single push of a button, and any other nodes that are shorted to the test node, even if they are in the same connector, will light up with the test node. The JPL-Cable Tester Boxes offers the following advantages: 1. Easy to use: The architecture is simple enough that it only takes 5 minutes for anyone to learn how operate the Cable Tester Box. No pre-programming and calibration are required, since this box only checks continuity. 2. Fast: The cable tester box checks all the possible electrical connections in parallel at a push of a button. If a cable normally takes half an hour to test, using the Cable Tester Box will improve the speed to as little as 60 seconds to complete. 3. Versatile: Multiple cable tester boxes can be used together. As long as all the boxes share the same electrical potential, any number of connectors can be tested together.

Lee, Jason H.↗
NASA NTRS2019

Machine Learning-Based Atmospheric Phenomena Detection Platform

As the number of Earth pointing satellites has increased over the last several decades, the data volume retrieved from instruments onboard these satellites has also increased. It is expected that this trend will continue as more data intensive missions and small satellite constellations are launched. Currently, feature detection - namely atmospheric phenomena - in these datasets is performed manually and is thus not scalable with the growing data archives. Recent advancements in computational efficiency allow for the Earth science community to leverage machine learning to identify interesting atmospheric phenomena. Given the wide range of distinctive features in various atmospheric phenomena, a specialized machine learning model is required for accurate detection of these phenomena independently. The Phenomena Portal, developed at NASA IMPACT, is designed to provide visualization for the output from these machine learning models. In addition, detected events for each atmospheric phenomena are stored in a database that can be used to more easily use/subset larger spatiotemporal datasets. The user interface also incorporates additional features to enhance the user experience including spatiotemporal analysis, multiple base layer images, and a slider to filter events with lower probabilities of positive detection. Each detection supports user feedback on whether the detection is true or false that can then be stored and used to improve the machine learning model performance.

Gurung, Iksha↗
NASA NTRS2020

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates.As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Verification and Validation↗
NASA NTRS2012

Lessons Learned during Thermal Hardware Integration on the Global Precipitation Measurement Satellite

The Global Precipitation Measurement mission is a joint NASA/JAXA mission scheduled for launch in late 2013. The integration of thermal hardware onto the satellite began in the Fall of 2010 and will continue through the Summer of 2012. The thermal hardware on the mission included several constant conductance heat pipes, heaters, thermostats, thermocouples radiator coatings and blankets. During integration several problems arose and insights were gained that would help future satellite integrations. Also lessons learned from previous missions were implemented with varying degrees of success. These insights can be arranged into three categories. 1) the specification of flight hardware using analysis results and the available mechanical resources. 2) The integration of thermal flight hardware onto the spacecraft, 3) The preparation and implementation of testing the thermal flight via touch tests, resistance measurements and thermal vacuum testing.

Cottingham, Christine↗
NASA NTRS2010

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Das, Santanu↗
NASA NTRS1987

Controls-structures-electromagnetics interaction program

A technology development program is described involving Controls Structures Electromagnetics Interaction (CSEI) for large space structures. The CSEI program was developed as part of the continuing effort following the successful kinematic deployment and RF tests of the 15 meter Hoop/Column antenna. One lesson learned was the importance of making reflector surface adjustment after fabrication and deployment. Given are program objectives, ground based test configuration, Intelsat adaptive feed, reflector shape prediction model, control experiment concepts, master schedule, and Control Of Flexible Structures-II (COFS-II) baseline configuration.

Grantham, William L.↗
NASA NTRS1989

Unfurlable, continuous-surface reflector concept

Various concepts for large, deployable reflectors were developed and some have flown. In each case the surface material was either a continuous mesh of some sort or an assembly of rigid, continuous-surface facets or petals. Performance issues arise in each case. For mesh, reflectance diminishes with increasing frequency. For rigid sections, seams and relative positioning of the segments have to be dealt with. These two issues prompted the evolution of the concept of an unfurlable, continuous-surface reflector. The concept is described and what is learnt is presented, what is suspected will be learned, and also questions raised yet to be addressed.

Stumm, J. E.↗
NASA NTRS2001

Synthesis of Two-Photon Materials and Two-Photon Liquid Crystals

The duration of the grant was interrupted by two major accidents that the PI met with-- an auto accident in Pasadena, CA during her second summer at JPL which took almost eight months for recovery and a second accident during Fall 2000 that left her in crutches for the entire semester. Further, the time released agreed by the University was not given in a timely fashion. The candidate has been given post-grant expire time off. In spite of all these problems, the PI synthesized a number of new two-photon materials and studied the structure-activity correlation to arrive at the best-optimized structure. The PI's design proved to be one of the best in the sense that these materials has a hitherto unreported two-photon absorption cross section. Many materials based on PI's design was later made by the NASA colleague. This is Phase 1. Phase II of this grant is to orate liquid crystalline nature into this potentially useful materials and is currently in progress. Recent observations of nano- and pico-second response time of homeotropically aligned liquid crystals suggest their inherent potentials to act as laser hardening materials, i.e., as protective devices against short laser pulses. The objective of the current project is to exploit this potential by the synthesis of liquid crystals with high optical nonlinearity and optimizing their performance. The PI is trying structural variations to bring in liquid crystalline nature without losing the high two-photon cross section. Both Phase I and Phase II led to many invited presentations and publications in reputed journals like 'Science' and 'Molecular Crystals'. The list of presentations and reprints are enclosed. Another important and satisfying outcome of this grant is the opportunity that this grant offered to the budding undergraduate scientists to get involved in a visible research of international importance. All the students had a chance to learn a lot during research, had the opportunity to present their work at the National level conferences. They continue to retain their interest in their research and went on to accomplish further laurels.

Subramaniam, Girija↗
NASA NTRS2002

NASA Materials Related Lessons Learned

Lessons Learned have been the basis for our accomplishments throughout the ages. They have been passed down from father to son, mother to daughter, teacher to pupil, and older to younger worker. Lessons Learned have also been the basis for the nation's accomplishments for more than 200 years. Both government and industry have long recognized the need to systematically document and utilize the knowledge gained from past experiences in order to avoid the repetition of failures and mishaps. Through the knowledge captured and recorded in Lessons Learned from more than 80 years of flight in the Earth's atmosphere, NASA's materials researchers are constantly working to develop stronger, lighter, and more durable materials that can withstand the challenges of space. The Agency's talented materials engineers and scientists continue to build on that rich tradition by using the knowledge and wisdom gained from past experiences to create futurist materials and technologies that will be used in the next generation of advanced spacecraft and satellites that may one day enable mankind to land men on another planet or explore our nearest star. These same materials may also have application here on Earth to make commercial aircraft more economical to build and fly. With the explosion in technical accomplishments over the last decade, the ability to capture knowledge and have the capability to rapidly communicate this knowledge at lightning speed throughout an organization like NASA has become critical. Use of Lessons Learned is a principal component of an organizational culture committed to continuous improvement.

Garcia, Danny↗
NASA NTRS2003

NASA Materials Related Lessons Learned

Lessons Learned have been the basis for our accomplishments throughout the ages. They have been passed down from father to son, mother to daughter, teacher to pupil, and older to younger worker. Lessons Learned have also been the basis for the nation s accomplishments for more than 200 years. Both government and industry have long recognized the need to systematically document and utilize the knowledge gained from past experiences in order to avoid the repetition of failures and mishaps. Through the knowledge captured and recorded in Lessons Learned from more than 80 years of flight in the Earth s atmosphere, NASA s materials researchers are constantly working to develop stronger, lighter, and more durable materials that can withstand the challenges of space. The Agency s talented materials engineers and scientists continue to build on that rich tradition by using the knowledge and wisdom gained from past experiences to create futuristic materials and technologies that will be used in the next generation of advanced spacecraft and satellites that may one day enable mankind to land men on another planet or explore our nearest star. These same materials may also have application here on Earth to make commercial aircraft more economical to build and fly. With the explosion in technical accomplishments over the last decade, the ability to capture knowledge and have the capability to rapidly communicate this knowledge at lightning speed throughout an organization like NASA has become critical. Use of Lessons Learned is a principal component of an organizational culture committed to continuous improvement.

Garcia, Danny↗