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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

National User Resource for Biological Accelerator Mass Spectrometry (Final Report)

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions. Over the next five years, our goals are to: 1. Improve the efficiency of operation for AMS measurements through installation of new interfaces to our AMS systems, technical modifications to improve gas accepting ion source efficiency and upgrading our data analysis software for improved ease of use and data reporting. 2. Increase the accessibility and visibility of ultra-sensitive 14C measurements for the biomedical research community by training of new investigators and expanding our national user base. 3. Provide high throughput, ultra-sensitive 14C analysis for the NIGMS and NIH user community.

47 OTHER INSTRUMENTATION↗
NASA NTRS2004

Software Assists in Responding to Anomalous Conditions

Fault Induced Document Retrieval Officer (FIDO) is a computer program that reduces the need for a large and costly team of engineers and/or technicians to monitor the state of a spacecraft and associated ground systems and respond to anomalies. FIDO includes artificial-intelligence components that imitate the reasoning of human experts with reference to a knowledge base of rules that represent failure modes and to a database of engineering documentation. These components act together to give an unskilled operator instantaneous expert assistance and access to information that can enable resolution of most anomalies, without the need for highly paid experts. FIDO provides a system state summary (a configurable engineering summary) and documentation for diagnosis of a potentially failing component that might have caused a given error message or anomaly. FIDO also enables high-level browsing of documentation by use of an interface indexed to the particular error message. The collection of available documents includes information on operations and associated procedures, engineering problem reports, documentation of components, and engineering drawings. FIDO also affords a capability for combining information on the state of ground systems with detailed, hierarchically-organized, hypertext- enabled documentation.

James, Mark↗
NASA NTRS2018

Electrodynamic Dust Shield (EDS) Preparation for MISSE-11 Launch

The dusty surfaces of the Moon, Mars, and various asteroids present a significant challenge to NASA’s manned and unmanned space exploration efforts. The fine, electrostatically charged dust is difficult to remove and has resulted in vision obscuration, false instrument readings, contamination, elevated temperatures, performance reduction, and equipment failure. To alleviate these problems, NASA KSC’s Electrostatics and Surface Physics Laboratory (ESPL) is developing active dust mitigation systems for solar system exploration and in situ resource utilization (ISRU). Part of KSC’s Swamp Works system, the lab emphasizes fast-paced, hands-on, cost-effective, and collaborative innovation. The Electrodynamic Dust Shield (EDS) is an ESPL technology being developed to prevent dust accumulation on space components. It consists of a dielectric substrate embedded with parallel electrodes which, when applied with high voltage out-of-phase waveforms, produce a traveling electric field wave that scatters dust particles from its surface via dielectrophoretic and electrostatic forces. The EDS is currently fully functional and has been tested for scalability and endurance in reduced gravity/pressure flight. Next, it will be part of the May 2019 Materials International Space Station Experiment-11 (MISSE-11) payload, which will test small shield samples for performance durability with exposure to the harsh space environment. The summer objective was to test EDS functionality before active and passive dust shield samples launch on MISSE-11, in order to screen for good flight/control samples as well as to compare pre-flight and post-flight clearing efficiencies. I imaged glass and Kapton shields of 2-phase and 3-phase electrode configurations with the Keyence VHX-5000 digital microscope to check for damage before and after all testing for a qualitative pre-flight baseline; helped with vacuum testing of high-voltage shield breakdown; assembled shields and wires using Electrobond 004 silver epoxy; and helped with vacuum and cryogenic dust clearance testing of assembled EDS systems. I learned CAD (computer-aided design) and 3D printing skills to create parts for a shaker table/motor assembly, which will allow for repeatable experiments by evening out EDS dust distribution in a consistent manner prior to dust scattering testing. I will also be improving the interfacing with Alpha Space’s prototype Data Communications Unit (DCU), a payload component to control power to the EDS and store data, by mapping the DCU structure upon power-up and writing shell scripts to improve various processes. Finally, I also CADed and 3D-printed plastic supports for three different geometries of the Electrostatic Precipitator (ESP), a project that aims to use high voltage electrodes to charge and filter out dust from the Martian atmosphere drawn for ISRU devices. This work on dust mitigation technologies will help to support future robotic and human space exploration missions, including NASA’s Journey to Mars; the EDS has especially significant applications to spacecraft solar panels, thermal radiators, viewports, and astronaut visors. This opportunity has also allowed me to become more familiar with various lab equipment and hardware testing, develop key modeling skills in SolidWorks, explore past and present components critical to America’s space program, work with inspiring peers and mentors, and ascertain my dream of working permanently for NASA.

space↗
NASA NTRS2002

An expert system for fault management assistance on a space sleep experiment

The expert system, Principal Investigator-in-a-box, or [PI], was designed to assist astronauts or other operators in performing experiments outside their expertise. Currently, the software helps astronauts calibrate instruments for a Sleep and Respiration Experiment without contact with the investigator on the ground. It flew on the Space Shuttle missions STS-90 and STS-95. [PI] displays electrophysiological signals in real time, alerts astronauts via the indicator lights when a poor signal quality is detected, and advises astronauts how to restore good signal quality. Thirty subjects received training on the sleep instrumentation and the [PI] interface. A beneficial effects of [PI] and training reduced troubleshooting time. [PI] benefited subjects on the most difficult scenarios, even though its lights were not 100% accurate. Further, questionnaires showed that most subjects preferred monitoring waveforms with [PI] assistance rather than monitoring waveforms alone. This study addresses problems of complex troubleshooting and the extended time between training and execution that is common to many human operator situations on earth such as in power plant operation, and marine exploration.

NASA Discipline Neuroscience↗
NASA NTRS1993

The rhesus measurement system: A new instrument for space research

The Rhesus Research Facility (RRF) is a research environment designed to study the effects of microgravity using rhesus primates as human surrogates. This experimental model allows investigators to study numerous aspects of microgravity exposure without compromising crew member activities. Currently, the RRF is slated for two missions to collect its data, the first mission is SLS-3, due to fly in late 1995. The RRF is a joint effort between the United States and France. The science and hardware portions of the project are being shared between the National Aeronautics and Space Administration (NASA) and France's Centre National D'Etudes Spatiales (CNES). The RRF is composed of many different subsystems in order to acquire data, provide life support, environmental enrichment, computer facilities and measurement capabilities for two rhesus primates aboard a nominal sixteen day mission. One of these subsystems is the Rhesus Measurement System (RMS). The RMS is designed to obtain in-flight physiological measurements from sensors interfaced with the subject. The RMS will acquire, preprocess, and transfer the physiologic data to the Flight Data System (FDS) for relay to the ground during flight. The measurements which will be taken by the RMS during the first flight will be respiration, measured at two different sites; electromyogram (EMG) at three different sites; electroencephalogram (EEG); electrocardiogram (ECG); and body temperature. These measurements taken by the RMS will assist the research team in meeting the science objectives of the RRF project.

Schonfeld, Julie E.↗
NASA NTRS1994

Application Reuse Library for Software, Requirements, and Guidelines

Better designs are needed for expert systems and other operations automation software, for more reliable, usable and effective human support. A prototype computer-aided Application Reuse Library shows feasibility of supporting concurrent development and improvement of advanced software by users, analysts, software developers, and human-computer interaction experts. Such a library expedites development of quality software, by providing working, documented examples, which support understanding, modification and reuse of requirements as well as code. It explicitly documents and implicitly embodies design guidelines, standards and conventions. The Application Reuse Library provides application modules with Demo-and-Tester elements. Developers and users can evaluate applicability of a library module and test modifications, by running it interactively. Sub-modules provide application code and displays and controls. The library supports software modification and reuse, by providing alternative versions of application and display functionality. Information about human support and display requirements is provided, so that modifications will conform to guidelines. The library supports entry of new application modules from developers throughout an organization. Example library modules include a timer, some buttons and special fonts, and a real-time data interface program. The library prototype is implemented in the object-oriented G2 environment for developing real-time expert systems.

Malin, Jane T.↗
NASA NTRS1995

Helicopter Human Factors

Even under optimal conditions, helicopter flight is a most demanding form of human-machine interaction, imposing continuous manual, visual, communications, and mental demands on pilots. It is made even more challenging by small margins for error created by the close proximity of terrain in NOE flight and missions flown at night and in low visibility. Although technology advances have satisfied some current and proposed requirements, hardware solutions alone are not sufficient to ensure acceptable system performance and pilot workload. However, human factors data needed to improve the design and use of helicopters lag behind advances in sensor, display, and control technology. Thus, it is difficult for designers to consider human capabilities and limitations when making design decisions. This results in costly accidents, design mistakes, unrealistic mission requirements, excessive training costs, and challenge human adaptability. NASA, in collaboration with DOD, industry, and academia, has initiated a program of research to develop scientific data bases and design principles to improve the pilot/vehicle interface, optimize training time and cost, and maintain pilot workload and system performance at an acceptable level. Work performed at Ames, and by other research laboratories, will be reviewed to summarize the most critical helicopter human factors problems and the results of research that has been performed to: (1) Quantify/model pilots use of visual cues for vehicle control; (2) Improve pilots' performance with helmet displays of thermal imagery and night vision goggles for situation awareness and vehicle control; (3) Model the processes by which pilots encode maps and compare them to the visual scene to develop perceptually and cognitively compatible electronic map formats; (4) Evaluate the use of spatially localized auditory displays for geographical orientation, target localization, radio frequency separation; (5) Develop and flight test control/display concepts; (6) Quantify, model, predict, and improve pilots, workload-management strategies; and (7) Design computer-game trainers to reduce training time and cost.

Hart, Sandra G.↗
NASA NTRS2015

Automatic Speech Acquisition and Recognition for Spacesuit Audio Systems

NASA has a widely recognized but unmet need for novel human-machine interface technologies that can facilitate communication during astronaut extravehicular activities (EVAs), when loud noises and strong reverberations inside spacesuits make communication challenging. WeVoice, Inc., has developed a multichannel signal-processing method for speech acquisition in noisy and reverberant environments that enables automatic speech recognition (ASR) technology inside spacesuits. The technology reduces noise by exploiting differences between the statistical nature of signals (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, ASR accuracy can be improved to the level at which crewmembers will find the speech interface useful. System components and features include beam forming/multichannel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, and ASR decoding. Arithmetic complexity models were developed and will help designers of real-time ASR systems select proper tasks when confronted with constraints in computational resources. In Phase I of the project, WeVoice validated the technology. The company further refined the technology in Phase II and developed a prototype for testing and use by suited astronauts.

Ye, Sherry↗
NASA NTRS2003

Discrete Abstractions of Hybrid Systems: Verification of Safety and Application to User-Interface Design

Human interaction with a complex control system involves the user, the automation’s discrete mode logic, and the underlying continuous dynamics of the physical system. The user-interface of such systems always displays a reduced set of information about the entire system. Designing interfaces such that all the pertinent information is available and assuring that this information is correct is important for any user-interface, but especially so for safety-critical systems such as automotive systems and autopilots. Here we describe a methodology for the analysis of hybrid control systems that incorporate user interaction, with the goal of assuring that the information provided to the user is correct. That is, the user-interface must contain all information necessary to safely complete a desired procedure or task. We begin with a hybrid system model which incorporates discrete mode logic as well as nonlinear continuous dynamics. Using a hybrid computational tool for reachability, we find the largest region of the state-space in which we can guarantee the state of the system can always remain – this is the safe region of operation. By implementing a controller for safety which arises from this computation, we mathematically guarantee that this safe region is invariant, meaning that the system will always remain within the safe region if the determined controller is used on the boundary of the safe region. Verification within a hybrid framework allows us to account for the continuous dynamics underlying the discrete representations displayed to the user. Using the computed invariant regions as discrete states, we can abstract a discrete event system from this hybrid system with safety restrictions. This abstraction can be used to determine what information must be provided on the display. Furthermore, in cases in which an interface already exists, the abstraction provides the necessary input into existing interface verification methods. We provide two examples: a car traveling through a yellow light at an intersection and an aircraft autopilot in an automatic landing/go-around maneuver. The examples demonstrate the applicability of this methodology to hybrid systems that have operational constraints we can pose in terms of safety. This methodology differs from existing work in hybrid system verification in that we directly account for the user’s interactions with the system.

Meeko Oishi↗
NASA NTRSDate not supplied

RadLab: Graphical and Programming Interfaces for Interrogation of Space Telemetry Data

Sensors on multiple spacecraft in and beyond low Earth orbit continuously monitor and collect space radiation data and transmit it back to Earth. These data are of vast importance to space biology research, as ionizing radiation affects living organisms—astronauts and non-human experiment subjects alike—placing them at higher risk of carcinogenesis, degenerative diseases, and radiation sickness. Therefore, knowledge of the biological effects of space radiation is essential for planning future crewed missions beyond low Earth orbit. The RadLab project, initiated by GeneLab and ALSDA (the Open Science Data Repository; OSDR) and sponsored by the NASA Human Research Program, is a new effort aimed at connecting dosimetry data from radiation detectors located on the International Space Station (ISS), as well as other spacecraft. To date, access to these data has been fragmented across space agencies and databases; to address this issue, we have developed an application programming interface (API) and an associated graphical user interface (GUI) designed to provide a single point of access to the data. As of now, OSDR has focused on the detectors located on the ISS, with the long-term goal to establish a self-sustained portal receiving continuous updates through APIs connecting to multiple radiation databases of varying scope, as well as individual investigator contributions. The RadLab API implements a request syntax enabling users to query data by craft, sensor type, timespan, etc, allowing for arbitrary combinations of original source data, thus providing programmatic access for use in computational pipelines, while the GUI facilitates data visualization and exploration, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

radiation↗
DOE OSTI2024

ENDFtk: A robust tool for reading and writing ENDF-formatted nuclear data

ENDFtk is a recently developed C++ and Python interface to interact with ENDF-6 formatted nuclear data files. It provides a robust and complete interface, allowing the reading and writing of all formats currently part of the ENDF-6 formats manual, as well as some non-ENDF formats used by the NJOY processing code. It provides an interface that mimics the names in the ENDF-6 formats manual as well as an equivalent interface using human-readable attribute names. It is robust and powerful enogh for nuclear data experts to develop complex applications, while also simple enough to be used non-experts to retrieve and manipulate evaluated nuclear data. ENDFtk offers the ability to easily interrogate and manipulate data either in large-scale code projects or in simple Python scripts. Here, in this paper, a brief overview of the interface is given, as well as more substantial examples demonstrating plotting simple data, interacting with more complex data, and writing new data to files. ENDFtk is open source and available for download via GitHub (https://github.com/njoy/ENDFtk).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

An integrated software suite for surface-based analyses of cerebral cortex

The authors describe and illustrate an integrated trio of software programs for carrying out surface-based analyses of cerebral cortex. The first component of this trio, SureFit (Surface Reconstruction by Filtering and Intensity Transformations), is used primarily for cortical segmentation, volume visualization, surface generation, and the mapping of functional neuroimaging data onto surfaces. The second component, Caret (Computerized Anatomical Reconstruction and Editing Tool Kit), provides a wide range of surface visualization and analysis options as well as capabilities for surface flattening, surface-based deformation, and other surface manipulations. The third component, SuMS (Surface Management System), is a database and associated user interface for surface-related data. It provides for efficient insertion, searching, and extraction of surface and volume data from the database.

NASA Discipline Neuroscience↗
DOE OSTI2025

A change language for ontologies and knowledge graphs

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of “apply patch” and “diff” commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders—e.g. ontology edits can be specified by commands like “add synonym ‘arm’ to ‘forelimb’” or “move ‘Parkinson disease’ under ‘neurodegenerative disease’.” We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1990

Fault diagnosis

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Abbott, Kathy↗
NASA NTRS2005

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

Otterstatter, Matthew R.↗
DOE OSTI2024

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin’s Regulation on Actin Filaments

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experimental work suggests that cofilin also forms functionally distinct oligomers through thiol post-translational modification (PTM) that encourages actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. Here, we also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol post-translational modification cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

Adaptive Distributed Environment for Procedure Training (ADEPT)

ADEPT (Adaptive Distributed Environment for Procedure Training) is designed to provide more effective, flexible, and portable training for NASA systems controllers. When creating a training scenario, an exercise author can specify a representative rationale structure using the graphical user interface, annotating the results with instructional texts where needed. The author's structure may distinguish between essential and optional parts of the rationale, and may also include "red herrings" - hypotheses that are essential to consider, until evidence and reasoning allow them to be ruled out. The system is built from pre-existing components, including Stottler Henke's SimVentive instructional simulation authoring tool and runtime. To that, a capability was added to author and exploit explicit control decision rationale representations. ADEPT uses SimVentive's Scalable Vector Graphics (SVG)- based interactive graphic display capability as the basis of the tool for quickly noting aspects of decision rationale in graph form. The ADEPT prototype is built in Java, and will run on any computer using Windows, MacOS, or Linux. No special peripheral equipment is required. The software enables a style of student/ tutor interaction focused on the reasoning behind systems control behavior that better mimics proven Socratic human tutoring behaviors for highly cognitive skills. It supports fast, easy, and convenient authoring of such tutoring behaviors, allowing specification of detailed scenario-specific, but content-sensitive, high-quality tutor hints and feedback. The system places relatively light data-entry demands on the student to enable its rationale-centered discussions, and provides a support mechanism for fostering coherence in the student/ tutor dialog by including focusing, sequencing, and utterance tuning mechanisms intended to better fit tutor hints and feedback into the ongoing context.

Domeshek, Eric↗
NASA NTRS2016

Modeling in the State Flow Environment to Support Launch Vehicle Verification Testing for Mission and Fault Management Algorithms in the NASA Space Launch System

Analysis methods and testing processes are essential activities in the engineering development and verification of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS). Central to mission success is reliable verification of the Mission and Fault Management (M&FM) algorithms for the SLS launch vehicle (LV) flight software. This is particularly difficult because M&FM algorithms integrate and operate LV subsystems, which consist of diverse forms of hardware and software themselves, with equally diverse integration from the engineering disciplines of LV subsystems. M&FM operation of SLS requires a changing mix of LV automation. During pre-launch the LV is primarily operated by the Kennedy Space Center (KSC) Ground Systems Development and Operations (GSDO) organization with some LV automation of time-critical functions, and much more autonomous LV operations during ascent that have crucial interactions with the Orion crew capsule, its astronauts, and with mission controllers at the Johnson Space Center. M&FM algorithms must perform all nominal mission commanding via the flight computer to control LV states from pre-launch through disposal and also address failure conditions by initiating autonomous or commanded aborts (crew capsule escape from the failing LV), redundancy management of failing subsystems and components, and safing actions to reduce or prevent threats to ground systems and crew. To address the criticality of the verification testing of these algorithms, the NASA M&FM team has utilized the State Flow environment6 (SFE) with its existing Vehicle Management End-to-End Testbed (VMET) platform which also hosts vendor-supplied physics-based LV subsystem models. The human-derived M&FM algorithms are designed and vetted in Integrated Development Teams composed of design and development disciplines such as Systems Engineering, Flight Software (FSW), Safety and Mission Assurance (S&MA) and major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GN&C), Thrust Vector Control (TVC), liquid engines, and the astronaut crew office. Since the algorithms are realized using model-based engineering (MBE) methods from a hybrid of the Unified Modeling Language (UML) and Systems Modeling Language (SysML), SFE methods are a natural fit to provide an in depth analysis of the interactive behavior of these algorithms with the SLS LV subsystem models. For this, the M&FM algorithms and the SLS LV subsystem models are modeled using constructs provided by Matlab which also enables modeling of the accompanying interfaces providing greater flexibility for integrated testing and analysis, which helps forecast expected behavior in forward VMET integrated testing activities. In VMET, the M&FM algorithms are prototyped and implemented using the same C++ programming language and similar state machine architectural concepts used by the FSW group. Due to the interactive complexity of the algorithms, VMET testing thus far has verified all the individual M&FM subsystem algorithms with select subsystem vendor models but is steadily progressing to assessing the interactive behavior of these algorithms with LV subsystems, as represented by subsystem models. The novel SFE applications has proven to be useful for quick look analysis into early integrated system behavior and assessment of the M&FM algorithms with the modeled LV subsystems. This early MBE analysis generates vital insight into the integrated system behaviors, algorithm sensitivities, design issues, and has aided in the debugging of the M&FM algorithms well before full testing can begin in more expensive, higher fidelity but more arduous environments such as VMET, FSW testing, and the Systems Integration Lab7 (SIL). SFE has exhibited both expected and unexpected behaviors in nominal and off nominal test cases prior to full VMET testing. In many findings, these behavioral characteristics were used to correct the M&FM algorithms, enable better test coverage, and develop more effective test cases for each of the LV subsystems. This has improved the fidelity of testing and planning for the next generation of M&FM algorithms as the SLS program evolves from non-crewed to crewed flight, impacting subsystem configurations and the M&FM algorithms that control them. SFE analysis has improved robustness and reliability of the M&FM algorithms by revealing implementation errors and documentation inconsistencies. It is also improving planning efficiency for future VMET testing of the M&FM algorithms hosted in the LV flight computers, further reducing risk for the SLS launch infrastructure, the SLS LV, and most importantly the crew.

Trevino, Luis↗