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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 NTRS2014

A Comprehensive Analysis of the X-15 Flight 3-65 Accident

The November 15, 1967, loss of X-15 Flight 3-65-97 (hereafter referred to as Flight 3-65) was a unique incident in that it was the first and only aerospace flight accident involving loss of crew on a vehicle with an adaptive flight control system (AFCS). In addition, Flight 3-65 remains the only incidence of a single-pilot departure from controlled flight of a manned entry vehicle in a hypersonic flight regime. To mitigate risk to emerging aerospace systems, the NASA Engineering and Safety Center (NESC) proposed a comprehensive review of this accident. The goal of the assessment was to resolve lingering questions regarding the failure modes of the aircraft systems (including the AFCS) and thoroughly analyze the interactions among the human agents and autonomous systems that contributed to the loss of the pilot and aircraft. This document contains the outcome of the accident review.

Dennehy, Cornelius J.↗
NASA NTRS2004

Partially Premixed Flame (PPF) Research for Fire Safety

Incipient fires typically occur after the partial premixing of fuel and oxidizer. The mixing of product species into the fuel/oxidizer mixture influences flame stabilization and fire spread. Therefore, it is important to characterize the impact of different levels of fuel/oxidizer/product mixing on flame stabilization, liftoff and extinguishment under different gravity conditions. With regard to fire protection, the agent concentration required to achieve flame suppression is an important consideration. The initial stage of an unwanted fire in a microgravity environment will depend on the level of partial premixing and the local conditions such as air currents generated by the fire itself and any forced ventilation (that influence agent and product mixing into the fire). The motivation of our investigation is to characterize these impacts in a systematic and fundamental manner.

Puri, Ishwar K.↗
NASA NTRS1992

Development of microcomputer-based mental acuity tests

Recent disasters have focused attention on performance problems due to the use of alcohol and controlled substances in the workplace. Environmental stressors such as thermal extremes, mixed gases, noise, motion, and vibration also have adverse effects on human performance and operator efficiency. However, the lack of a standardized, sensitive, human performance assessment battery has probably delayed the systematic study of the deleterious effects of various toxic chemicals and drugs at home and in the workplace. The collective goal of the research reported here is the development of a menu of tests embedded in a coherent package of hardware and software that may be useful in repeated-measures studies of a broad range of agents that can degrade human performance. A menu of 40 tests from the Automated Performance Test System (APTS) is described, and the series of interlocking studies supporting its development is reviewed. The APTS tests, which run on several versions of laptop portables and desktop personal computers, have been shown to be stable, reliable, and factorially rich, and to have predictive validities with holistic measures of intelligence and simulator performances. In addition, sensitivity studies have been conducted in which performance changes due to stressors, agents, and treatments were demonstrated. We believe that tests like those described here have prospective use as an adjunct to urine testing for the screening for performance loss of individuals who are granted access to workplaces and stations that impact public safety.

Non-NASA Center↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗
NASA NTRS2004

Agent Architecture for Aviation Data Integration System

This paper describes the proposed agent-based architecture of the Aviation Data Integration System (ADIS). ADIS is a software system that provides integrated heterogeneous data to support aviation problem-solving activities. Examples of aviation problem-solving activities include engineering troubleshooting, incident and accident investigation, routine flight operations monitoring, safety assessment, maintenance procedure debugging, and training assessment. A wide variety of information is typically referenced when engaging in these activities. Some of this information includes flight recorder data, Automatic Terminal Information Service (ATIS) reports, Jeppesen charts, weather data, air traffic control information, safety reports, and runway visual range data. Such wide-ranging information cannot be found in any single unified information source. Therefore, this information must be actively collected, assembled, and presented in a manner that supports the users problem-solving activities. This information integration task is non-trivial and presents a variety of technical challenges. ADIS has been developed to do this task and it permits integration of weather, RVR, radar data, and Jeppesen charts with flight data. ADIS has been implemented and used by several airlines FOQA teams. The initial feedback from airlines is that such a system is very useful in FOQA analysis. Based on the feedback from the initial deployment, we are developing a new version of the system that would make further progress in achieving following goals of our project.

Kulkarni, Deepak↗
NASA NTRS2019

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nation’s air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two.

Houston, Vincent E.↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRS1997

A Cognitive System Model for Human/Automation Dynamics in Airspace Management

NASA has initiated a significant thrust of research and development focused on providing the flight crew and air traffic managers automation aids to increase capacity in en route and terminal area operations through the use of flexible, more fuel-efficient routing, while improving the level of safety in commercial carrier operations. In that system development, definition of cognitive requirements for integrated multi-operator dynamic aiding systems is fundamental. In order to support that cognitive function definition, we have extended the Man Machine Integrated Design and Analysis System (MIDAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems) operating aircraft, airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The MIDAS operator models have undergone significant development in order to understand the requirements for operator aiding and the impact of that aiding in the complex nondeterminate system of national airspace operations. The operator model's structure has been modified to include attention functions, action priority, and situation assessment. The cognitive function model has been expanded to include working memory operations including retrieval from long-term store, interference, visual-motor and verbal articulatory loop functions, and time-based losses. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. The model's internal representation has been be modified so that multiple, autonomous sets of equipment will function in a scenario as the single equipment sets do now. In order to support the analysis requirements with multiple items of equipment, it is necessary for equipment to access the state of other equipment objects at initialization time (a radar object may need to access the position and speed of aircraft in its area, for example), and as a function of perception and sensor system interaction. The model has been improved to include multiple world-states as a function of equipment am operator interaction. The model has been used -1o predict the impact of warning and alert zones in aircraft operation, and, more critic-ally, the interaction of flight-deck based warning mechanisms and air traffic controller action in response to ground-based conflict prediction and alerting systems. In this operation, two operating systems provide alerting to two autonomous, but linked sets of operators, whose view of the system and whose dynamics in response are radically different. System stability and operator action was predicted using the MIDAS model.

Corker, Kevin M.↗
NASA NTRS2002

Biosensors for Real-Time Monitoring of Radiation-Induced Biologic Effects in Space

This proposal seeks to develop cellular biosensors based on dendritic polymers. Nanoscale polymer structures less than 20 nm in diameter will be used as the basis of the sensor/actuators. The structures will be designed to target into specific cells of an astronaut and be able to monitor health issues such as the exposure to radiation or infectious agents. Multiple components can be assembled on the polymers including target directors, analytical devices (such as molecular probes), magnetic particles and metals, and imaging agents. The design and assembly of these devices has been pioneered at the Center for Biologic Nanotechnology in the University of Michigan. These molecules would also be able to administer therapeutics in response to the needs of the astronaut, and act as actuators to remotely manipulate an astronaut as necessary to ensure their safety. The reporting will be accomplished either through fluorescence signal monitoring, with the use of multispectral analysis for signal interpretation, or through functional MRI. These nanosensors coupled to NEMS devices could facilitate the success and increase the safety of extended space flight.

Baker, James R., Jr.↗
NASA NTRS2004

Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates whose consistency can be tracked across multiple time steps.

Narasimhan, Sriram↗
NASA NTRS2007

The Naturalistic Flight Deck System: An Integrated System Concept for Improved Single-Pilot Operations

This paper reviews current and emerging operational experiences, technologies, and human-machine interaction theories to develop an integrated flight system concept designed to increase the safety, reliability, and performance of single-pilot operations in an increasingly accommodating but stringent national airspace system. This concept, know as the Naturalistic Flight Deck (NFD), uses a form of human-centered automation known as complementary-automation (or complemation) to structure the relationship between the human operator and the aircraft as independent, collaborative agents having complimentary capabilities. The human provides commonsense knowledge, general intelligence, and creative thinking, while the machine contributes specialized intelligence and control, extreme vigilance, resistance to fatigue, and encyclopedic memory. To support the development of the NFD, an initial Concept of Operations has been created and selected normal and non-normal scenarios are presented in this document.

Schutte, Paul C.↗
NASA NTRS1975

Quarantine testing and biocharacterization of lunar materials

Quarantine testing was conducted to ensure the safety of all life on earth. The plants and animals which were exposed to lunar material were carefully observed for prolonged periods to determine if any mutation or changes in growing characteristics and behavior occurred. The quarantine testing was terminated after the Apollo 14 flight when it became apparent that previously returned lunar material contained no potentially harmful agents. Further biological experimentation with the lunar material was conducted to determine its chemical, physical, and nutritional qualities.

Gerald R. Taylor↗
NASA NTRS2003

NASA Tech Briefs, August 2003

Topics covered include: Stable, Thermally Conductive Fillers for Bolted Joints; Connecting to Thermocouples with Fewer Lead Wires; Zipper Connectors for Flexible Electronic Circuits; Safety Interlock for Angularly Misdirected Power Tool; Modular, Parallel Pulse-Shaping Filter Architectures; High-Fidelity Piezoelectric Audio Device; Photovoltaic Power Station with Ultracapacitors for Storage; Time Analyzer for Time Synchronization and Monitor of the Deep Space Network; Program for Computing Albedo; Integrated Software for Analyzing Designs of Launch Vehicles; Abstract-Reasoning Software for Coordinating Multiple Agents; Software Searches for Better Spacecraft-Navigation Models; Software for Partly Automated Recognition of Targets; Antistatic Polycarbonate/Copper Oxide Composite; Better VPS Fabrication of Crucibles and Furnace Cartridges; Burn-Resistant, Strong Metal-Matrix Composites; Self-Deployable Spring-Strip Booms; Explosion Welding for Hermetic Containerization; Improved Process for Fabricating Carbon Nanotube Probes; Automated Serial Sectioning for 3D Reconstruction; and Parallel Subconvolution Filtering Architectures.

Source record↗
NASA NTRS2014

UAS Conflict-Avoidance Using Multiagent RL with Abstract Strategy Type Communication

The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.

Unmanned Autonomous Systems↗
NASA NTRS2014

Announced Strategy Types in Multiagent RL for Conflict-Avoidance in the National Airspace

The use of unmanned aerial systems (UAS) in the national airspace is of growing interest to the research community. Safety and scalability of control algorithms are key to the successful integration of autonomous system into a human-populated airspace. In order to ensure safety while still maintaining efficient paths of travel, these algorithms must also accommodate heterogeneity of path strategies of its neighbors. We show that, using multiagent RL, we can improve the speed with which conflicts are resolved in cases with up to 80 aircraft within a section of the airspace. In addition, we show that the introduction of abstract agent strategy types to partition the state space is helpful in resolving conflicts, particularly in high congestion.

National Airspace↗
NASA NTRS2016

NASA and ESA Collaboration on Alternative to Nitric Acid Passivation: Parameter Optimization of Citric Acid Passivation for Stainless Steel Alloys

National Aeronautics and Space Administration (NASA) Headquarters chartered the Technology Evaluation for Environmental Risk Mitigation Principal Center (TEERM) to coordinate agency activities affecting pollution prevention issues identified during system and component acquisition and sustainment processes. The primary objectives of NASA TEERM are to: Reduce or eliminate the use of hazardous materials or hazardous processes at manufacturing, remanufacturing, and sustainment locations. Avoid duplication of effort in actions required to reduce or eliminate hazardous materials through joint center cooperation and technology sharing. Corrosion is an extensive problem that affects the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The damaging effects of corrosion result in steep costs, asset downtime affecting mission readiness, and safety risks to personnel. Consequently, it is vital to reduce corrosion costs and risks in a sustainable manner. NASA and ESA have numerous structures and equipment that are fabricated from stainless steel. The standard practice for protection of stainless steel is a process called passivation. Passivation is defined by The American Heritage Dictionary of the English Language as to treat or coat (a metal) in order to reduce the chemical reactivity of its surface. Passivation works by forming a shielding outer (metal oxide) layer that reduces the impact of destructive environmental factors such as air or water. Consequently, this process necessitates a final product that is very clean and free of iron and other contaminants. Typical passivation procedures call for the use of nitric acid; however, there are a number of environmental, worker safety, and operational issues associated with its use. Citric acid is an alternative to nitric acid for the passivation of stainless steels. Citric acid offers a variety of benefits including increased safety for personnel, reduced environmental impact, and reduced operational cost. The primary objective of this effort is to qualify citric acid as an environmentally-preferable alternative to nitric acid for the passivation of stainless steel alloys. While citric acid use has become more prominent in industry, there is little evidence that citric acid is a technically sound passivation agent, especially for the unique and critical applications encountered by NASA and ESA.

citric acid passivation↗
NASA NTRS2005

Systemic Analysis Approaches for Air Transportation

Air transportation system designers have had only limited success using traditional operations research and parametric modeling approaches in their analyses of innovations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be used with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed. However, air transportation has proven itself an extensive, complex system whose behavior is difficult to describe, no less predict. There is a wide range of system analysis techniques available, but some are more appropriate for certain applications than others. Specifically in the area of complex system analysis, the literature suggests that both agent-based models and network analysis techniques may be useful. This paper discusses the theoretical basis for each approach in these applications, and explores their historic and potential further use for air transportation analysis.

Conway, Sheila↗