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NASA NTRS2015

Deploying the NASA Meter Class Autonomous Telescope (MCAT) on Ascension Island

NASA has successfully constructed the 1.3m Meter Class Autonomous Telescope (MCAT) facility on Ascension Island in the South Atlantic Ocean. MCAT is an optical telescope designed specifically to collect ground-based data for the statistical characterization of orbital debris ranging from Low Earth Orbit (LEO) through Middle Earth Orbits (MEO) and beyond to Geo Transfer and Geosynchronous Orbits (GTO/GEO). The location of Ascension Island has two distinct advantages. First, the near-equatorial location fills a significant longitudinal gap in the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) network of telescopes, and second, it allows access to objects in Low Inclination Low-Earth Orbits (LILO). The MCAT facility will be controlled by a sophisticated software suite that operates the dome and telescope, assesses sky and weather conditions, conducts all necessary calibrations, defines an observing strategy (as dictated by weather, sky conditions and the observing plan for the night), and carries out the observations. It then reduces the collected data via four primary observing modes ranging from tracking previously cataloged objects to conducting general surveys for detecting uncorrelated debris. Nightly observing plans, as well as the resulting text file of reduced data, will be transferred to and from Ascension, respectively, via a satellite connection. Post-processing occurs at NASA Johnson Space Center. Construction began in September, 2014 with dome and telescope installation occurring in April through early June, 2015. First light was achieved in June, 2015. Acceptance testing, full commissioning, and calibration of this soon-to-be fully autonomous system commenced in summer 2015. The initial characterization of the system from these tests is presented herein.

Lederer, S. M.↗

NASA NTRS2021

Grappling Spacecraft

This article provides a survey overview of the techniques, mechanisms, algorithms, and test and validation strategies required for the design of roboticgrappling vehicles intended to approach and grapple free-flying client satellites. We concentrate on using a robotic arm to grapple a free-floating spacecraft, as distinct from spacecraft docking and berthing, where two spacecraft directly mate with each other. Robotic grappling of client spacecraft is a deceptively complex problem: It entails designing a robotic system that functions robustly in the visually stark, thermally extreme orbital environment,operating near massive and extremely expensive yet fragile client hardware,using relatively slow flight computers with limited and laggy communica-tions. Spaceflight robotic systems are challenging to test and validate priorto deployment and extremely expensive to launch, which significantly limitsopportunities to experiment with new techniques. These factors make thedesign and operation of orbital robotic systems significantly different fromthose of their terrestrial counterparts, and as a result, only a relative handfulof systems have been demonstrated on orbit. Nevertheless, there is increas-ing interest in on-orbit robotic servicing and assembly missions, and grap-pling is the core requirement for these systems. Although existing systemssuch as the Space Station Remote Manipulator System have demonstratedextremely reliable operation, upcoming missions will attempt to expand thetypes of spacecraft that can be safely and dependably grappled and berthed.

Carl Glen Henshaw↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRS1995

The role of small missions in planetary and lunar exploration

The Space Studies Board of the National Research Council charged its Committee on Planetary and Lunar Exploration (COMPLEX) to (1) examine the degree to which small missions, such as those fitting within the constraints of the Discovery program, can achieve priority objectives in the lunar and planetary sciences; (2) determine those characteristics, such as level of risk, flight rate, target mix, university involvement, technology development, management structure and procedures, and so on, that could allow a successful program; (3) assess issues, such as instrument selection, mission operations, data analysis, and data archiving, to ensure the greatest scientific return from a particular mission, given a rapid deployment schedule and a tightly constrained budget; and (4) review past programmatic attempts to establish small planetary science mission lines, including the Planetary Observers and Planetary Explorers, and consider the impact management practices have had on such programs. A series of small missions presents the planetary science community with the opportunity to expand the scope of its activities and to develop the potential and inventiveness of its members in ways not possible within the confines of large, traditional programs. COMPLEX also realized that a program of small planetary missions was, in and of itself, incapable of meeting all of the prime objectives contained in its report 'An Integrated Strategy for the Planetary Sciences: 1995-2010.' Recommendations are provided for the small planetary missions to fulfill their promise.

Source record↗

NASA NTRS2013

Affordable Development and Qualification Strategy for Nuclear Thermal Propulsion

A number of recent assessments have confirmed the results of several earlier studies that Nuclear Thermal Propulsion (NTP) is a leading technology for human exploration of Mars. It is generally acknowledged that NTP provides the best prospects for the transportation of humans to Mars in the 2030's. Its high Isp coupled with the high thrusts achievable, allow reasonable trip times, thereby alleviating concerns about space radiation and "claustrophobia" effects. NASA has embarked on the latest phase of the development of NTP systems, and is adopting an affordable approach in response to the pressure of the times. The affordable strategy is built on maximizing the use of the large NTP technology base developed in the 1950's and 60's. The fact that the NTP engines were actually demonstrated to work as planned, is a great risk reduction feature in its development. The strategy utilizes non-nuclear testing to the fullest extent possible, and uses focused nuclear tests for the essential qualification and certification tests. The perceived cost risk of conducting the ground tests is being addressed by considering novel testing approaches. This includes the use of boreholes to contain radioactive effluents, and use of fuel with very high retention capability for fission products. The use of prototype flight tests is being considered as final steps in the development prior to undertaking human flight missions. In addition to the technical issues, plans are being prepared to address the institutional and political issues that need to be considered in this major venture. While the development and deployment of NTP system is not expected to be cheap, the value of the system will be very high, and amortized over the many missions that it enables and enhances, the imputed costs will be very reasonable. Using the approach outlined, NASA and its partners, currently the DOE, and subsequently industry, have a good chance of creating a sustained development program leading to human missions to Mars within the next few decades.

Gerrish, Harold P., Jr.↗

NASA NTRS2025

Distributed Sensing Node Configuration

A vision and radar sensor integration strategy for surveillance of Advance Air Mobility Mission concept of airspace is contained in the CAD drawing in this publication developed as part of NASA's Transformational Tools and Technology project. High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. Contained herein are drawings that are available in the stp and creo file formats of the distributed sensing nodes. Additional dovetail integration is included to enable rapid changing of embedded system. A day in the life node deployment is included in the 2023_last_flight_day.mp4. Please cite this publication if you use these drawings. This sensor configuration methodology is presented in detail in the publication reference below. Chester Dolph, Thomas Lombaerts, Corey A. Ippolito, Vahram Stepanyan, Evan Kawamura, Keerthana Kannan, George Szatkowski, Todd Ferante, Christopher Morris, Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano. "Distributed Sensor Fusion of Ground and Air Nodes using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds," AIAA 2024-1781. AIAA SCITECH 2024 Forum. January 2024.

Chester V Dolph↗

NASA NTRSDate not supplied

Cloud Climate Data Records from Geostationary Satellites to Support CERES: Challenges and Recent Progress

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record at all times of day during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. A brief overview of the current CERES/GEO cloud property data record constructed from three generations of GEOsat imagers for CERES Edition-4 is presented. In this version, different algorithms were applied to different satellites to take advantage of as much spectral information as possible for each satellite. Under this approach, the derived cloud properties were found to become accurate (and more similar to MODIS) for the GEOsat imagers with more spectral information. However, this approach also led to some discontinuities in cloud properties across platforms. Significant differences were also found between daytime and nighttime cloud properties. This paper will highlight the major issues in the Edition-4 record and discuss new strategies and early results from the next version of the SatCORPS GEOsat cloud property time series in development for CERES Edition-5 with the goal of improving day-night and cross-platform consistency during the CERES record.

clouds↗

NASA NTRS2022

Electrical Ground Support Equipment for the Sampling Caching System of the Mars 2020 Rover

In this work we describe in detail the architecture, design, testing and operation of the Electrical Ground Support Equipment (EGSE) “Blue Box” used to test and validate the Sampling Caching System (SCS) of the Mars 2020 Perseverance rover. The Blue Box architecture is centered around COTS motor controllers and COTS input-output modules communicating over an EtherCAT bus. A custom, low-level safety subsystem ensures no harm can be done to the flight articles. The modular architecture of the EGSE reduces cost and complexity while expediting assembly time. The Blue Box drives the 19 actuators of the SCS which span the main robotic arm, the corer system, the internal sample handling arm, the sample tube sealing system and the gas dust removal tool; mimicking the Rover Motor Control Assembly (RMCA). Due to the limited availability of RMCA’s, the EGSE enabled and performed the bulk of testing activities for SCS. The majority of the SCS actuators are composed of a 3-phase DC brushless motors, hall sensors for commutation, dual resolvers for output angular measurement, brakes, heaters and platinum thermistors. Additionally, the EGSE read 12 strain gauges forming part of a force torque sensor, and switches used for external positioning references. Over the 3-year span of the V&V campaign for the SCS, over 32 EGSE systems were built, tested and deployed to test venues at JPL and externally. The EGSE tested several families of the SCS subsystem, ranging from engineering units, life test units and two flight units. Test venues that this EGSE supported included lab benches, ultra-clean cleanrooms, ATLO facilities, and thermal vacuum chambers. Together with the test software systems, SSDEV and SSDEV-ECAT, the Blue Box EGSE enabled the team to efficiently test flight hardware and flight software together. We go over the safety features and fault management techniques employed to protect flight hardware. The effects of the long, 50-feet, EGSE harnesses on motor performance, EMI, electrical noise, and motor control performance are explained. Mitigations to these unwanted effects, including shielding strategy and inductance compensation, are summarized. We go over an excerpt of notable anomalies that this EGSE suffered through its operation, along with investigations and resolutions. Lessons learned, areas of improvement as part of future work, and recommendations for future implementations for similar EGSE’s, are shared.

Levine, Dan↗

NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗

NASA NTRSDate not supplied

Analysis of the Value Added When Deploying a Model-Based Approach for the Validation and Verification of the Medical Database Software

The Medical Database (MD) is a virtual repository consisting of two software components: Medical Item Database (MedID) and the Evidence Library (EL). MedID consists of engineering data and associated information for specific medical resource items (e.g., pharmaceutical, medical devices, and supporting components), while the EL is a tool which provides all of the medical evidence necessary. The MD will 1) serve as the single “source of truth” for the Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool suite for both medical evidence and medical resource engineering data and 2) will be used in conjunction with the IMPACT tool suite to inform research prioritizations and perform systematic trade study evaluations to aid stakeholders in making informed decisions regarding simulated human spaceflight missions. The MD project used a Model-Based Systems Engineering (MBSE) approach to support all life cycles of the software development, while in parallel the human factors engineering team used modeling to support Human Centered Design (HCD) strategies in an effort to improve software usability. HCD is a frequently used approach in design frameworks that develops resolutions to complexities and challenges by involving the human perspective in all steps of the problem-solving process. By integrating the model-based approaches used for systems engineering and human factors activities, the project is able to leverage the model-based artifacts originally created for HCD activities for system level and human factors validation. In this presentation, our team highlights the value added when leveraging these model-based artifacts to support the on-going verification and validation activities.

C. Laing↗

NASA NTRS2009

Software Health Management: A Short Review of Challenges and Existing Techniques

Modern spacecraft (as well as most other complex mechanisms like aircraft, automobiles, and chemical plants) rely more and more on software, to a point where software failures have caused severe accidents and loss of missions. Software failures during a manned mission can cause loss of life, so there are severe requirements to make the software as safe and reliable as possible. Typically, verification and validation (V&V) has the task of making sure that all software errors are found before the software is deployed and that it always conforms to the requirements. Experience, however, shows that this gold standard of error-free software cannot be reached in practice. Even if the software alone is free of glitches, its interoperation with the hardware (e.g., with sensors or actuators) can cause problems. Unexpected operational conditions or changes in the environment may ultimately cause a software system to fail. Is there a way to surmount this problem? In most modern aircraft and many automobiles, hardware such as central electrical, mechanical, and hydraulic components are monitored by IVHM (Integrated Vehicle Health Management) systems. These systems can recognize, isolate, and identify faults and failures, both those that already occurred as well as imminent ones. With the help of diagnostics and prognostics, appropriate mitigation strategies can be selected (replacement or repair, switch to redundant systems, etc.). In this short paper, we discuss some challenges and promising techniques for software health management (SWHM). In particular, we identify unique challenges for preventing software failure in systems which involve both software and hardware components. We then present our classifications of techniques related to SWHM. These classifications are performed based on dimensions of interest to both developers and users of the techniques, and hopefully provide a map for dealing with software faults and failures.

Pipatsrisawat, Knot↗

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

Medical Operations↗

NASA NTRS2001

Low power, lightweight vapor sensing using arrays of conducting polymer composite chemically-sensitive resistors

Arrays of broadly responsive vapor detectors can be used to detect, identify, and quantify vapors and vapor mixtures. One implementation of this strategy involves the use of arrays of chemically-sensitive resistors made from conducting polymer composites. Sorption of an analyte into the polymer composite detector leads to swelling of the film material. The swelling is in turn transduced into a change in electrical resistance because the detector films consist of polymers filled with conducting particles such as carbon black. The differential sorption, and thus differential swelling, of an analyte into each polymer composite in the array produces a unique pattern for each different analyte of interest, Pattern recognition algorithms are then used to analyze the multivariate data arising from the responses of such a detector array. Chiral detector films can provide differential detection of the presence of certain chiral organic vapor analytes. Aspects of the spaceflight qualification and deployment of such a detector array, along with its performance for certain analytes of interest in manned life support applications, are reviewed and summarized in this article.

NASA Discipline Life Sciences Technologies↗

NASA NTRS2005

Space Qualification of Laser Diode Arrays

Laser instruments have great potential in enabling a new generation of remote-sensing scientific instruments. NASA s desire to employ laser instruments aboard satellites, imposes stringent reliability requirements under severe conditions. As a result of these requirements, NASA has a research program to understand, quantify and reduce the risk of failure to these instruments when deployed on satellites. Most of NASA s proposed laser missions have base-lined diode-pumped Nd:YAG lasers that generally use quasi-constant wave (QCW), 808 nm Laser Diode Arrays (LDAs). Our group has an on-going test program to measure the performance of these LDAs when operated in conditions replicating launch and orbit. In this paper, we report on the results of tests designed to measure the effect of vibration loads simulating launch into space and the radiation environment encountered on orbit. Our primary objective is to quantify the performance of the LDAs in conditions replicating those of a satellite instrument, determine their limitations and strengths which will enable better and more robust designs. To this end we have developed a systematic testing strategy to quantify the effect of environmental stresses on the optical and electrical properties of the LDA.

Troupaki, Elisavet↗

NASA NTRS2012

Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System

As communication and networking technologies advance, networks will become highly complex and heterogeneous, interconnecting different network domains. There is a need to provide user authentication and data protection in order to further facilitate critical mission operations, especially in the tactical and mission-critical net-centric networking environment. The Autonomous Information Unit (AIU) technology was designed to provide the fine-grain data access and user control in a net-centric system-testing environment to meet these objectives. The AIU is a fundamental capability designed to enable fine-grain data access and user control in the cross-domain networking environments, where an AIU is composed of the mission data, metadata, and policy. An AIU provides a mechanism to establish trust among deployed AIUs based on recombining shared secrets, authentication and verify users with a username, X.509 certificate, enclave information, and classification level. AIU achieves data protection through (1) splitting data into multiple information pieces using the Shamir's secret sharing algorithm, (2) encrypting each individual information piece using military-grade AES-256 encryption, and (3) randomizing the position of the encrypted data based on the unbiased and memory efficient in-place Fisher-Yates shuffle method. Therefore, it becomes virtually impossible for attackers to compromise data since attackers need to obtain all distributed information as well as the encryption key and the random seeds to properly arrange the data. In addition, since policy can be associated with data in the AIU, different user access and data control strategies can be included. The AIU technology can greatly enhance information assurance and security management in the bandwidth-limited and ad hoc net-centric environments. In addition, AIU technology can be applicable to general complex network domains and applications where distributed user authentication and data protection are necessary. AIU achieves fine-grain data access and user control, reducing the security risk significantly, simplifying the complexity of various security operations, and providing the high information assurance across different network domains.

Chow, Edward T.↗

NASA NTRS2019

Risk Analysis Associated with Loss of Toxic Gases During Orion Landing and Recovery Operations

Mission, landing and recovery operations for the Orion crew module involve reentry into the Earth's atmosphere and the deployment of three Nomex parachutes to slow the descent before landing along the west coast of the United States. Orion may have residual fuel (hydrazine, N2H4) or coolant (ammonia, NH3) on board which are both highly toxic to crew in the event of exposure. These risks were evaluated using a first principles analysis approach through fluid dynamics modeling. Plume calculations were first performed with the ANSYS Fluent computational fluid dynamics code. Data were then extracted at locations relevant to crew safety such as the snorkel fan inlet and the egress hatch. Mixing calculations were performed to quantify exposure concentrations within the crew bay before and during egress and departure. Finally, results included herein were used to inform the Orion post-landing Concept of Operations (ConOps) so that strategies could be formulated to maintain crew safety in the event of the loss of fuel or coolant.

Orion↗