Results for “Risk algorithm” — Page 8

NASA NTRSDate not supplied

Data Driven UAM Flight Energy Consumption Prediction and Risk Assessment

With the current technological advancements revolutionizing the concept of Urban Air Mobility (UAM) and package delivery, there is also, a concurrent need to quantify the operational safety of these vehicles in terms of their associated risk. Conducting safe flight operations is critical for UAM vehicles which are electrically Vertical Takeoff and Landing (eVTOL) vehicles, to operate in current Air traffic control. In this paper, a data-driven method for UAM vehicle energy consumption prediction and risk quantification with conditional value-at-risk based on energy consumption distribution is presented. Significant factors affecting energy consumption, such as density altitude, aircraft design, airspeed, and collision avoidance algorithms, are considered in the data-driven based energy consumption prediction of different eVTOL

Data-driven↗

NASA NTRS2012

NASA's Robotic Lunar Lander Development Project

Since early 2005, NASA's Robotic Lunar Lander Development (RLLD) office at NASA MSFC, in partnership with the Applied Physics Laboratory (APL), has developed mission concepts and preformed risk-reduction activities to address planetary science and exploration objectives uniquely met with landed missions. The RLLD team developed several concepts for lunar human-exploration precursor missions to demonstrate precision landing and in-situ resource utilization, a multi-node lunar geophysical network mission, either as a stand-alone mission, or as part of the International Lunar Network (ILN), a Lunar Polar Volatiles Explorer and a Mercury lander mission for the Planetary Science decadal survey, and an asteroid rendezvous and landing mission for the Exploration Precursor Robotics Mission (xPRM) office. The RLLD team has conducted an extensive number of risk-reduction activities in areas common to all lander concepts, including thruster testing, propulsion thermal control demonstration, composite deck design and fabrication, and landing leg stability and vibration. In parallel, the team has developed two robotic lander testbeds providing closed-loop, autonomous hover and descent activities for integration and testing of flight-like components and algorithms. A compressed-air test article had its first flight in September 2009 and completed over 150 successful flights. This small test article (107 kg dry/146 kg wet) uses a central throttleable thruster to offset gravity, plus 3 descent thrusters (~37lbf ea) and 6 attitude-control thrusters (~12lbf ea) to emulate the flight system with pulsed operation over approximately 10s of flight time. The test article uses carbon composite honeycomb decks, custom avionics (COTS components assembled in-house), and custom flight and ground software. A larger (206 kg dry/322 kg wet), hydrogen peroxide-propelled vehicle began flight tests in spring 2011 and fly over 30 successful flights to a maximum altitude of 30m. The monoprop testbed also uses a central gravity-canceling thruster and 3 descent thrusters, but has 12 attitude-control thrusters and a maximum flight time of over a minute. The testbed uses aluminum ortho-grid decks, an LN200-1 IMU, Roke Manor Radar Altimeter, Illunis optical cameras, Novatel Pro-Pak GPS truth data system, Pressure transducers & thermocouples for housekeeping, "In-Control" ground system software, and the core Flight Executive (cFE) modular software environment. The peroxide lander testbed is able to accept other sensors and algorithms for testing, both from within NASA and from other customers. Through these activities, the RLLD team has significantly reduced technical risks for all small and medium class robotic landers for the Moon and other airless planetary bodies.

Cohen, Barbara A.↗

NASA NTRS2018

NASA SPLICE Project: Development and Testing of Precision Landing GN&C Technologies

NASA's technology advancement needs for entry, descent and landing call for high-precision, high-rate sensors that can improve navigation accuracy and vehicle control performance. Higher landing accuracy is required for any future human lander missions, and likely, for most robotic missions 1,2. Sensors and algorithms that significantly reduce navigation errors and can image the local terrain will enable landing at locations of high scientific interest that would otherwise pose significant risk to the vehicle. The Safe and Precise Landing-Integrated Capabilities Evolution project, or SPLICE, is developing precision landing and hazard avoidance (PL&HA) technologies for NASA and for potential commercial space flight missions. SPLICE technologies include sensors, algorithms, advanced space flight computing capabilities, and simulation tools used to integrate and study guidance, navigation, and control (GN&C) system performance. SPLICE efforts include hardware-in-the-loop (HWIL) simulation testing, ground testing, and flight testing, including reuse of hardware from the CoOperative Blending of Autonomous Landing Technologies (COBALT) suborbital flight-test payload3,4. Two of the precise navigation sensors that are being developed and matured within SPLICE are LiDARs. Since 2006, NASA Langley has been developing a Navigation Doppler LiDAR (NDL) for precise velocity measurements, and SPLICE is building an NDL engineering test unit (ETU) that will be brought up to TRL 6 following environmental and high-speed1,2 testing. NASA Goddard is developing a Hazard Detection LiDAR (HD LiDAR) engineering development unit (EDU) for SPLICE that has relevance to future human and robotic lander missions. The HD LiDAR will be flight test and matured to TRL 5.

Restrepo, Carolina I.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗

NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗

NASA NTRS2008

Estimating the Need for Medical Intervention due to Sleep Disruption on the International Space Station

During ISS and shuttle missions, difficulties with sleep affect more than half of all US crews. Mitigation strategies to help astronauts cope with the challenges of disrupted sleep patterns can negatively impact both mission planning and vehicle design. The methods for addressing known detrimental impacts for some mission scenarios may have a substantial impact on vehicle specific consumable mass or volume or on the mission timeline. As part of the Integrated Medical Model (IMM) task, NASA Glenn Research Center is leading the development of a Monte Carlo based forecasting tool designed to determine the consumables required to address risks related to sleep disruption. The model currently focuses on the International Space Station and uses an algorithm that assembles representative mission schedules and feeds this into a well validated model that predicts relative levels of performance, and need for sleep (SAFTE Model, IBR Inc). Correlation of the resulting output to self-diagnosed needs for hypnotics, stimulants, and other pharmaceutical countermeasures, allows prediction of pharmaceutical use and the uncertainty of the specified prediction. This paper outlines a conceptual model for determining a rate of pharmaceutical utilization that can be used in the IMM model for comparison and optimization of mitigation methods with respect to all other significant medical needs and interventions.

Myers, Jerry G.↗

NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗

NASA NTRS2023

Human Systems Integration: Managing Risk in Anesthesia

The practice of anesthesia relies on clinicians’ ability to safely manage increasingly complex equipment. Devices such as ventilators, drug infusion pumps, and physiologic monitors use sophisticated algorithms to deliver care, but most clinicians are only trained to manage automated systems during normal operation. Few if any receive training on how to manage system failures. Although manufacturers are required to consult with human factors engineers as part of the equipment design process, most pieces of equipment are ultimately brought to market without extensive input from clinicians. Systems in the operating room can be as simple as an oxygen tank, or as complex as a multi-institutional healthcare organization. Humans are also a complex system, and play a critical role in the domains of operations, design, fabrication, maintenance, repair, and ultimately, dismantling and closeout. HSI seeks to provide a means for advocating the human side of the system. Human Systems Integration (HSI) is the cross disciplinary process used as part of the Systems Engineering process to reduce risk in systems. HSI professionals consider the human, hardware, and software elements of system design to optimize system performance and improve safety. HSI professionals work in domains of study that include training, management, human factors engineering, safety, and occupational health, among others. This article discusses the role of systems in the practice of anesthesia, and how consideration of the human during all phases of the system life cycle helps manage risks and promote a better patient outcome.

Human Factors↗

NASA NTRS2000

Perspectives on the Future of CFD

This viewgraph presentation gives an overview of the future of computational fluid dynamics (CFD), which in the past has pioneered the field of flow simulation. Over time CFD has progressed as computing power. Numerical methods have been advanced as CPU and memory capacity increases. Complex configurations are routinely computed now and direct numerical simulations (DNS) and large eddy simulations (LES) are used to study turbulence. As the computing resources changed to parallel and distributed platforms, computer science aspects such as scalability (algorithmic and implementation) and portability and transparent codings have advanced. Examples of potential future (or current) challenges include risk assessment, limitations of the heuristic model, and the development of CFD and information technology (IT) tools.

Kwak, Dochan↗

NASA NTRS2011

The Space Operations Simulation Center (SOSC) and Closed-loop Hardware Testing for Orion Rendezvous System Design

The Space Operations Simulation Center (SOSC) at the Lockheed Martin (LM) Waterton Campus in Littleton, Colorado is a dynamic test environment focused on Autonomous Rendezvous and Docking (AR&D) development testing and risk reduction activities. The SOSC supports multiple program pursuits and accommodates testing Guidance, Navigation, and Control (GN&C) algorithms for relative navigation, hardware testing and characterization, as well as software and test process development. The SOSC consists of a high bay (60 meters long by 15.2 meters wide by 15.2 meters tall) with dual six degree-of-freedom (6DOF) motion simulators and a single fixed base 6DOF robot. The large testing area (maximum sensor-to-target effective range of 60 meters) allows for large-scale, flight-like simulations of proximity maneuvers and docking events. The facility also has two apertures for access to external extended-range outdoor target test operations. In addition, the facility contains four Mission Operations Centers (MOCs) with connectivity to dual high bay control rooms and a data/video interface room. The high bay is rated at Class 300,000 (. 0.5 m maximum particles/m3) cleanliness and includes orbital lighting simulation capabilities.

D'Souza, Christopher↗

NASA NTRS2011

Accelerated Aging Experiments for Prognostics of Damage Growth in Composite Materials

Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.

composites↗

NASA NTRS2023

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s

Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade-study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

Observing System↗

NASA NTRS2013

Orion MPCV GN and C End-to-End Phasing Tests

End-to-end integration tests are critical risk reduction efforts for any complex vehicle. Phasing tests are an end-to-end integrated test that validates system directional phasing (polarity) from sensor measurement through software algorithms to end effector response. Phasing tests are typically performed on a fully integrated and assembled flight vehicle where sensors are stimulated by moving the vehicle and the effectors are observed for proper polarity. Orion Multi-Purpose Crew Vehicle (MPCV) Pad Abort 1 (PA-1) Phasing Test was conducted from inertial measurement to Launch Abort System (LAS). Orion Exploration Flight Test 1 (EFT-1) has two end-to-end phasing tests planned. The first test from inertial measurement to Crew Module (CM) reaction control system thrusters uses navigation and flight control system software algorithms to process commands. The second test from inertial measurement to CM S-Band Phased Array Antenna (PAA) uses navigation and communication system software algorithms to process commands. Future Orion flights include Ascent Abort Flight Test 2 (AA-2) and Exploration Mission 1 (EM-1). These flights will include additional or updated sensors, software algorithms and effectors. This paper will explore the implementation of end-to-end phasing tests on a flight vehicle which has many constraints, trade-offs and compromises. Orion PA-1 Phasing Test was conducted at White Sands Missile Range (WSMR) from March 4-6, 2010. This test decreased the risk of mission failure by demonstrating proper flight control system polarity. Demonstration was achieved by stimulating the primary navigation sensor, processing sensor data to commands and viewing propulsion response. PA-1 primary navigation sensor was a Space Integrated Inertial Navigation System (INS) and Global Positioning System (GPS) (SIGI) which has onboard processing, INS (3 accelerometers and 3 rate gyros) and no GPS receiver. SIGI data was processed by GN&C software into thrust magnitude and direction commands. The processing changes through three phases of powered flight: pitchover, downrange and reorientation. The primary inputs to GN&C are attitude position, attitude rates, angle of attack (AOA) and angle of sideslip (AOS). Pitch and yaw attitude and attitude rate responses were verified by using a flight spare SIGI mounted to a 2-axis rate table. AOA and AOS responses were verified by using a data recorded from SIGI movements on a robotic arm located at NASA Johnson Space Center. The data was consolidated and used in an open-loop data input to the SIGI. Propulsion was the Launch Abort System (LAS) Attitude Control Motor (ACM) which consisted of a solid motor with 8 nozzles. Each nozzle has active thrust control by varying throat area with a pintle. LAS ACM pintles are observable through optically transparent nozzle covers. SIGI movements on robot arm, SIGI rate table movements and LAS ACM pintle responses were video recorded as test artifacts for analysis and evaluation. The PA-1 Phasing Test design was determined based on test performance requirements, operational restrictions and EGSE capabilities. This development progressed during different stages. For convenience these development stages are initial, working group, tiger team, Engineering Review Team (ERT) and final.

Neumann, Brian C.↗

NASA NTRS2005

Tool for Ranking Research Options

Tool for Research Enhancement Decision Support (TREDS) is a computer program developed to assist managers in ranking options for research aboard the International Space Station (ISS). It could likely also be adapted to perform similar decision-support functions in industrial and academic settings. TREDS provides a ranking of the options, based on a quantifiable assessment of all the relevant programmatic decision factors of benefit, cost, and risk. The computation of the benefit for each option is based on a figure of merit (FOM) for ISS research capacity that incorporates both quantitative and qualitative inputs. Qualitative inputs are gathered and partly quantified by use of the time-tested analytical hierarchical process and used to set weighting factors in the FOM corresponding to priorities determined by the cognizant decision maker(s). Then by use of algorithms developed specifically for this application, TREDS adjusts the projected benefit for each option on the basis of levels of technical implementation, cost, and schedule risk. Based partly on Excel spreadsheets, TREDS provides screens for entering cost, benefit, and risk information. Drop-down boxes are provided for entry of qualitative information. TREDS produces graphical output in multiple formats that can be tailored by users.

Ortiz, James N.↗

NASA NTRS2017

Real-Time Prediction of Safety Margins in the National Airspace

Underlying all operations in the National Airspace System (NAS) is the concept of safety. Safety, as defined by acceptable levels of risk, is to be maintained at all times. The real-time safety monitoring (RTSM) framework is under development to provide an automated system to quantify safety in the NAS, estimate the current level of safety, and predict the future evolution of safety and the occurrence of events that pose an increased risk to flights so that these occurrences can be managed strategically rather than mitigated reactively. This paper presents the mathematical framework, the models, and the monitoring and prediction algorithms used to achieve this. RTSM computes safety as expressed through a set of safety margins based on user-defined safety metrics, thresolds, and events. Sources of uncertainty are modeled and propagated through the predictions in order to compute the probabilistic evolution of safety and the probability of events that introduce increased risk to operations. A prototype implementation is discussed and results demonstrating feasibility are presented. The results highlight the kinds of predictions that can be computed and the fidelity that is currently achieved.

national airspace system↗