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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 NTRS2006

Solar Sails

The Solar Sail Propulsion investment area has been one of the three highest priorities within the In-Space Propulsion Technology (ISPT) Project. In the fall of 2003, the NASA Headquarters' Science Mission Directorate provided funding and direction to mature the technology as far as possible through ground research and development from TRL 3 to 6 in three years. A group of experts from government, industry, and academia convened in Huntsville, Alabama to define technology gaps between what was needed for science missions to the inner solar system and the current state of the art in ultra1ightweight materials and gossamer structure design. This activity set the roadmap for development. The centerpiece of the development would be the ground demonstration of scalable solar sail systems including masts, sails, deployment mechanisms, and attitude control hardware and software. In addition, new materials would be subjected to anticipated space environments to quantify effects and assure mission life. Also, because solar sails are huge structures, and it is not feasible to validate the technology by ground test at full scale, a multi-discipline effort was established to develop highly reliable analytical models to serve as mission assurance evidence in future flight program decision-making. Two separate contractor teams were chosen to develop the SSP System Ground Demonstrator (SGD). After a three month conceptual mission/system design phase, the teams developed a ten meter diameter pathfinder set of hardware and subjected it to thermal vacuum tests to compare analytically predicted structural behavior with measured characteristics. This process developed manufacturing and handling techniques and refined the basic design. In 2005, both contractor teams delivered 20 meter, four quadrant sail systems to the largest thermal vacuum chamber in the world in Plum Brook, Ohio, and repeated the tests. Also demonstrated was the deployment and articulation of attitude control mechanisms. In conjunction with these tests, the stowed sails were subjected to launch vibration and ascent vent tests. Other investments studied radiation effects on the solar sail materials, investigated spacecraft charging issues, developed shape measuring techniques and instruments, produced advanced trajectory modeling capabilities, and identified and resolved gossamer structure dynamics issues. Technology validation flight and application to a He1iophysics science mission is on the horizon.

Young, Roy↗
NASA NTRS2002

Overview of the National Aeronautics and Space Administration's Nondestructive Evaluation (NDE) Program

NASA's Office of Safety and Mission Assurance sponsors an Agency-wide NDE Program that supports Aeronautics and Space Transportation Technology, Human Exploration and Development of Space, Earth Science, and Space Science Enterprises. For each of these Enterprises, safety is the number one priority. Development of the next generation aero-space launch and transportation vehicles, satellites, and deep space probes have highlighted the enabling role that NDE plays in these advanced technology systems. Specific areas of advanced component development, component integrity, and structural heath management are critically supported by NDE technologies. The simultaneous goals of assuring safety, maintaining overall operational efficiency, and developing and utilizing revolutionary technologies to expand human activity and space-based commerce in the frontiers of air and space places increasing demands on the Agencies NDE infrastructure and resources. In this presentation, an overview of NASA's NDE Program will be presented, that includes a background and status of current Enterprise NDE issues, and the NDE investment areas being developed to meet Enterprise safety and mission assurance needs through the year 2009 and beyond.

Generazio, Edward R.↗
NASA NTRS1999

NASA Lewis Launch Collision Probability Model Developed and Analyzed

There are nearly 10,000 tracked objects orbiting the earth. These objects encompass manned objects, active and decommissioned satellites, spent rocket bodies, and debris. They range from a few centimeters across to the size of the MIR space station. Anytime a new satellite is launched, the launch vehicle with its payload attached passes through an area of space in which these objects orbit. Although the population density of these objects is low, there always is a small but finite probability of collision between the launch vehicle and one or more of these space objects. Even though the probability of collision is very low, for some payloads even this small risk is unacceptable. To mitigate the small risk of collision associated with launching at an arbitrary time within the daily launch window, NASA performs a prelaunch mission assurance Collision Avoidance Analysis (or COLA). For the COLA of the Cassini spacecraft, the NASA Lewis Research Center conducted an in-house development and analysis of a model for launch collision probability. The model allows a minimum clearance criteria to be used with the COLA analysis to ensure an acceptably low probability of collision. If, for any given liftoff time, the nominal launch vehicle trajectory would pass a space object with less than the minimum required clearance, launch would not be attempted at that time. The model assumes that the nominal positions of the orbiting objects and of the launch vehicle can be predicted as a function of time, and therefore, that any tracked object that comes within close proximity of the launch vehicle can be identified. For any such pair, these nominal positions can be used to calculate a nominal miss distance. The actual miss distances may differ substantially from the nominal miss distance, due, in part, to the statistical uncertainty of the knowledge of the objects positions. The model further assumes that these position uncertainties can be described with position covariance matrices. With these, and some additional simplifying assumptions, a closed-form solution for the probability of collision is obtained. This solution provides clear insights into how each of the independent parameters affects the probability of collision. It shows that for a given maximum probability of collision and prior knowledge of the objects position uncertainties and sizes, only knowledge of the nominal closest approach distance is required to make the launch/no launch decision. The model was completed and used for the mission assurance COLA analysis for the Cassini spacecraft, which was launched on a Titan IV/Centaur rocket on October 15, 1997. Although the model was specifically developed for the Cassini mission, it is clearly applicable for other launches as well. The effect of COLA closures on the launch window is shown. The bar represents the entire 140-min launch window on October 15, 1997; the blackened areas represent the loss of launch opportunities due to the potential for a collision with an orbiting object.

Bollenbacher, Gary↗
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 NTRS2005

Taking the Risk Out of Risk Assessment

The ability to understand risks and have the right strategies in place when risky events occur is essential in the workplace. More and more organizations are being confronted with concerns over how to measure their risks or what kind of risks they can take when certain events transpire that could have a negative impact. NASA is one organization that faces these challenges on a daily basis, as effective risk management is critical to the success of its missions especially the Space Shuttle missions. On July 29, 1996, former NASA Administrator Daniel Goldin charged NASA s Office of Safety and Mission Assurance with developing a probabilistic risk assessment (PRA) tool to support decisions on the funding of Space Shuttle upgrades. When issuing the directive, Goldin said, "Since I came to NASA [in 1992], we've spent billions of dollars on Shuttle upgrades without knowing how much they improve safety. I want a tool to help base upgrade decisions on risk." Work on the PRA tool began immediately. The resulting prototype, the Quantitative Risk Assessment System (QRAS) Version 1.0, was jointly developed by NASA s Marshall Space Flight Center, its Office of Safety and Mission Assurance, and researchers at the University of Maryland. QRAS software automatically expands the reliability logic models of systems to evaluate the probability of highly detrimental outcomes occurring in complex systems that are subject to potential accident scenarios. Even in its earliest forms, QRAS was used to begin PRA modeling of the Space Shuttle. In parallel, the development of QRAS continued, with the goal of making it a world-class tool, one that was especially suited to NASA s unique needs. From the beginning, an important conceptual goal in the development of QRAS was for it to help bridge the gap between the professional risk analyst and the design engineer. In the past, only the professional risk analyst could perform, modify, use, and perhaps even adequately understand PRA. NASA wanted to change this by developing a PRA tool that would be friendlier, more understandable, and more useful to the broader engineering community. This concept ultimately led to the look, feel, and functionality that QRAS has today.

Source record↗
NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs 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 Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on 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 either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗
NASA NTRS2010

Techniques for Assuring NASA Mission Success Using Redundancy and Multi-Functionality Designs

Topics include NASA centers around the country; 2009 highlights of significant successes in space transportation, exploration, and science; significant accomplishments; places to explore include Lagrange points, near-Earth objects, Mars and the Moon, and International Space Station research; Marshall's missions include propulsion and transportation systems, life support systems, and earth and space science spacecraft, systems, and operations; project lifecycle management model; motivation of avionics fault-tolerance, redundancy needs and concerns, redundancy versus reliability; parallel-series configurations; effect of adding redundancy on mission success; example of rules-based approach where reliability and safety interaction impacts design; impact of common cause failure; approach ot bottom-up reliability analysis; three factors that lead to redundant system failure; Apollo 13 multi-functional reliability and example; and mitigating the risk of single string spacecraft architecture;.

Shivers, Herb↗
NASA NTRSDate not supplied

SMD Class D standard MAR

Overview presentation for a streamlined Class D mission assurance requirements document developed by an Agency-wide team for NASA Science Mission Directorate

SMA↗
NASA NTRS2018

NC Space Grant Report

During the summer of 2018 I supported the Safety & Mission Assurance Directorate (SMA) and Operations Support Division (QA-20) at Stennis Space Center. The mission of the SMA team is to prove safety, risk, reliability, independent assessments, configuration management and quality assurance guidance, and services for all NASA Stennis Space Center (SSC) programs, facilities, and supporting infrastructure. The office actively participates and contributes to the Agency-level Safety & Mission Assurance (S&MA) effort. Over the course of the Summer I participated in three projects. Two of them were focused around Fault Tree Analysis (FTA) and the third focused on relief valves for their E-1 engine test stand.

Torres, David↗
NASA NTRS2016

The NASA Electronic Parts and Packaging (NEPP) Program - Presentation to Korean Aerospace Research Institute

This presentation will provide basic information about NASA's Electronic Parts and Packaging Program (NEPP), for sharing with representatives of the South Korean Aerospace Research Institute (KARI) as part of a larger presentation by Headquarters Office of Safety and Mission Assurance. The NEPP information includes mission and goals, history of the program, basic focus areas, strategies, deliverables and some examples of current tasks.

Technology Challenges↗
NASA NTRS2011

GSFC Supplier Surveillance

Topics covered include: Develop Program/Project Quality Assurance Surveillance Plans The work activities performed by the developer and/or his suppliers are subject to evaluation and audit by government-designated representatives. CSO supports project by selecting on-site supplier representative s by one of several methods: (1) a Defense Contract Management Agency (DCMA) person via a Letter Of Delegation (LOD), (2) an independent assurance contractor (IAC) via a contract Audits, Assessments, and Assurance (A3) Contract Code 300 Mission Assurance Support Contract (MASC)

Kelly, Michael P.↗
NASA NTRS1998

Mars Pathfinder

The Mars Pathfinder Mission Assurance program was the first.

concurrent engineering early↗
NASA NTRS1996

Defect Detection and Prevention

As NASA continues to impement its Faster, Better, Cheaper philosophy, new approaches for implementing Mission Assurance activities are being developed.

faster↗
NASA NTRSDate not supplied

What You Need to Know about Planetary Protection Categorization for Your Mission

Planetary protection mission categorization helps to define the risk posture for requirements definition and implementation practices on space flight projects with planetary protection considerations. NASA has recently updated it’s planetary protection policy (NPR 8715.24), and with the update comes a major change in how the planetary protection categorization process is performed for NASA missions. Previously, projects would request the NASA Planetary Protection Officer (PPO) provide a categorization letter documenting the planetary protection category assigned to the proposed mission. Under the new policy the project manager (PM) proposes the planetary protection category for their mission. Depending on the level of the category, the PPO either concurs or recommends concurrence to the Chief of Safety and Mission Assurance (SMA) before forwarding to the Mission Directorate Associate Administrator (MDAA) for final approval prior to the system requirements review (SRR). But what topics must be considered for categorization? This presentation will provide key considerations for planetary protection mission categorization including the target solar system body to be explored, the trajectory to get to the target body, and the operations to be performed at the target body. Additional unique scenarios such as secondary and auxiliary payloads, encountering other solar system bodies on the way to the target body, and sample return missions will also be discussed to share lessons learned from missions that have been through the new categorization process.

Elaine Seasly↗
NASA NTRS2018

Computer Programming Resources 2018

The document is a compilation of Computer Programming resources. It is geared toward front-end and back-end web development. It specializes on an ongoing project to create a website for the Safety and Mission Assurance Directorate. The Quality Assurance System (QAS) is intended to improve the overall quality and efficiency of communication among NASA Quality Assurance Engineers and Contractors. The use of QAS software will be limited to NASA Kennedy Space Center and Contractors. It is accessible through the KSC network.

Confluence↗