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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Streamlining GNC Architecture Development and FSW Integration for the Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Engineering↗
NASA NTRSDate not supplied

Mars Ascent Vehicle GNC Targeting Routines with Considerations for Flight Software Development

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Jason Everett↗
NASA NTRSDate not supplied

FEA-Based Failure Analysis of Post-Buckled Composite Multi-Hat Stiffened Panel Under Static Compression

The aerospace industry uses composite multi-hat stiffened panels for skins because of their high strength-to-weight and stiffness-to-weight ratios. However, laminated composites with initial flaws are susceptible to delamination under buckling loads. The goal is to ensure damage initiation occurs past the buckled state, only stable damage progression at loads below the ultimate compressive load, and debonding failure past the ultimate compressive load. This approach reduces weight and addresses a potential design review for future aircraft repairs. The need exists to better understand damage initiation and growth mechanisms in composite structures while assisting in the design, analysis, and sustainment methods. Teflon inserts simulate damage defects during manufacturing, while barely visible impact helps capture human error during manufacturing. This work shows the interaction between the skin-stringer and ply-ply separations at the post-buckled state for both defect events using Abaqus Virtual Crack Closure Technique (VCCT). The initial configuration for the Teflon case was to leave the Teflon region unbonded, and the initial impact damage region approximation came from the experimental data and was considered the initial configuration. When compared against the experimental data produced through the NASA Advanced Composites Project (ACP), the present model captured damage transition from skin-hat to ply1-ply2. The model validations were within ~5% of the experimental data.

composite↗
NASA NTRSDate not supplied

Is "Sleepy Supervision" the New "Drowsy Driving"?

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

sleepiness↗
NASA NTRSDate not supplied

Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence

Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.

Arabidopsis↗
NASA NTRS2025

Limitations and Feasibility of Mini X-Ray Devices in Space Environments

LIMITATIONS AND FEASIBILITY OF MINI X-RAY DEVICES IN SPACE ENVIRONMENTS As space exploration advances toward long-duration missions, reliable medical diagnostic tools become increasingly critical. The miniature x-ray (XR) technology demonstrations by the Exploration Medical Capability (ExMC) and the Exploration Medical Integrated Product Team (XMIPT) aim to assess the feasibility and utility of miniature XR devices in spaceflight. This abstract explores the limitations of current miniature XR systems, the challenges of training crew members, the potential role of clinical decision support systems (CDSS), and the feasibility of ground-based image interpretation. We also propose the integration of miniature XR into other ExMC efforts aimed at identifying the capabilities and resources needed for future exploration class missions. One of the primary challenges with miniature XR devices is the ability to achieve specific anatomical views, particularly in the confined and weightless conditions of a spacecraft. Operators may struggle to acquire diagnostic-quality images when space is limited for proper patient positioning and the volume of the imaging device. Since space radiation and detector limitations may further impact image quality, the flexibility of the operating procedures of these devices will be critical for their success in space applications. CHALLENGES IN TRAINING CREW TO OPERATE IMAGING DEVICES Training in the skills necessary to acquire diagnostic-quality scans may be a barrier for non-clinician crewmembers. The curriculum developed for crew medical officers (CMOs) will require simplification and adaptation to fit into the highly truncated pre-flight training period. Therefore, hands-on familiarization and simulation, both pre-flight and just-in-time training during missions, will be crucial to ensuring the crew can operate the devices in real-life situations. The ability to adjust acquisition parameters must be simplified or made automatic through exam selections on equipment user interfaces, and subject and operator positioning should be assisted with laser guidance and pictorial guides. POTENTIAL FOR CDSS OR ARTIFICIAL INTELLIGENCE (AI)-ASSISTED CDSS CDSS and AI-assisted CDSS offer significant promise in assisting crew members with limited medical training. These systems could provide real-time feedback on image quality and interpretation, helping to mitigate the risks of human error during space missions. Integrating procedural guidance tools, such as virtual and augmented reality, will support crewmembers in accurately positioning patients and obtaining high-quality images. However, the success of such systems will depend on the development of robust training datasets, integration with spaceflight-rated hardware, and the medical decision-making capabilities of operators. FEASIBILITY OF GROUND INTERPRETATION AND DATA TRANSMISSION Reliance on ground-based interpretation may prove difficult for acute care during exploration class-missions due to delays in transmission with increasing distance from Earth or complete communication blackout periods. In such instances where immediate interpretation for clinical intervention is required, crew must be able to interpret the images independently or utilize AI-based assistance to do so. File sizes for XR exams can also be large if numerous images are acquired and bandwidth constraints may limit data transmissions for both radiography and ultrasound exams. FUTURE WORK AND INTEGRATION INTO THE EVIDENCE LIBRARY Future work proposes integrating miniature XR devices into NASA’s Evidence Library to address medical conditions identified as significant contributors to crew morbidity and mortality. The possibility of combining miniature XR with other imaging modalities, such as ultrasound devices, is also under investigation. In conclusion, while miniature XR technology holds potential for extraterrestrial medical systems, there are significant challenges to overcome. Training, integration of AI tools, dedicated exam protocols for microgravity, and improved data transmission systems will be key to realizing the full benefits of miniature XR technology in space.

A M Nelson↗
NASA NTRS2024

Applying Generative-AI to NASA Documentation and Processes

This research and development project leverages generative-AI to assist in the generation of software process documentation based on NASA standards. By utilizing fine-tuned AI models, the proposed system will analyze NASA's software guidelines, helping to translate them into well-structured, compliant process documents. This assistance can reduce the manual effort required to produce such documentation, enhance consistency, and assure alignment with NASA's stringent software development and operational requirements. In addition to assisting in the generation of software process documentation, the project explores how generative-AI can help create audit checklists as well as assess the compliance of NASA provider documentation against applicable NASA standards. This approach would support the compliance auditing process, providing real-time insights and assessments. The intended result will be a streamlined process, potentially including a Python-based tool and database, that improves audit efficiency, reduces human error, lowers manpower costs and required manhours, and assures continuous compliance with NASA and industry evolving standards for safety-critical software development. Future task might be to investigate the software industry approach and standards for potential collaboration.

NASA Standards↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

A Python Library for Radiance Matrix-based Simulation Control and EnergyPlus Integration

Radiance matrix-based methods enable efficient parametric simulations, allowing users to vary sky conditions, fenestration systems, and other model parameters at a minimal cost to computation. However, the steep learning curve and complex workflow hinder the widespread adoption of matrix-based methods. The frads Python library with a series of command-line tools was developed to automate the entire matrix-based simulation process, lowering entry barriers and reducing human error. Co-simulation between EnergyPlus and Radiance was also enabled using the Python library from EnergyPlus. Key Innovations • Command-line based automation of Radiance matrix-based simulation methods • Python library facilitates broader adoption of Radiance matrix-based simulation methods • Radiance EnergyPlus run-time integration enabling the modeling of advanced control systems Practical Implications The frads library, with associated command-line tools, provides practitioners with the capability to easily adopt and use Radiance matrix-based simulation methods for various daylighting, solar control, and energy-related evaluations. Frads' current form is designed for 1) users familiar with a command-line interface and 2) software developers to integrate the matrix-based methods into existing software packages.

Wang, Taoning↗
DOE OSTI2022

Energy Management Information Systems Cybersecurity Best Practices

Energy management information systems (EMIS) are a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance. Critical systems are often integrated with or operate on the same networks as EMIS scope systems, necessitating stable, continuous, and secure communication. When connecting EMIS to building automation and utility control systems, there are also many physical assets that could cause harm to the building and its occupants if a malicious act or human error were introduced. It is imperative to ensure all EMIS scope systems are connected securely to the EMIS and do not open vulnerable pathways to other facility networks and operations. The Federal Energy Management Program (FEMP) promotes best practices for impactful utilization of EMIS at federal facilities. This best practice document is part of a series of fact sheets created to help accelerate the market adoption and use of EMIS in the federal sector. It provides an overview of required EMIS cybersecurity standards for compliance and authority to operate along with additional recommendations.

cybersecurity↗
DOE OSTI2022

Energy Management Information Systems Cybersecurity Best Practices

Energy management information systems (EMIS) are a broad and rapidly evolving family of tools that monitor, analyze, and control building energy use and system performance. Critical systems are often integrated with or operate on the same networks as EMIS scope systems, necessitating stable, continuous, and secure communication. When connecting EMIS to building automation and utility control systems, there are also many physical assets that could cause harm to the building and its occupants if a malicious act or human error were introduced. It is imperative to ensure all EMIS scope systems are connected securely to the EMIS and do not open vulnerable pathways to other facility networks and operations. The Federal Energy Management Program (FEMP) promotes best practices for impactful utilization of EMIS at federal facilities. This best practice document is part of a series of fact sheets created to help accelerate the market adoption and use of EMIS in the federal sector. It provides an overview of required EMIS cybersecurity standards for compliance and authority to operate along with additional recommendations.

Cybersecurity↗
DOE OSTI2021

BioSecure Digital Twin: Manufacturing Innovation and Cybersecurity Resilience

U.S. national security, prosperity, economy, and well-being require secure, flexible, and resilient Biopharmaceutical Manufacturing. The COVID-19 pandemic reaffirmed that the biomedical production value-chain is vulnerable to disruption and has been under attack from sophisticated nation-state adversaries. Current cyber defenses are inadequate, and the integrity of critical production systems and processes are inherently vulnerable to cyber-attacks, human error, and supply chain disruptions. The following chapter explores how a BioSecure Digital Twin will improve U.S. manufacturing resilience and preparedness to respond to these hazards by significantly improving monitoring, integrity, security, and agility of our manufacturing infrastructure and systems. The BioSecure Digital Twin combines a scalable manufacturing framework with a robust platform for monitoring and control to increase U.S. biopharma manufacturing resilience. Then, the chapter discusses some of the inherent vulnerabilities and challenges at the nexus of health and advanced manufacturing. Next, the chapter highlights that as the Pandemic evolves, we need agility and resilience to overcome significant obstacles. This section highlights an innovative application of Cyber Informed Engineering to developing and deploying a BioSecure Digital Twin to improve the resilience and security of the biopharma industrial supply chain and production processes. Finally, the chapter concludes with a process framework to complement the Digital Twin platform, called the Biopharma (Observe, Orient, Decide, Act) OODA Loop Framework (BOLF), a four-step approach to decision-making outputs from the Digital Twin. The BOLF will help end users leverage twin technology by distilling the available information, focusing the data on context, and rapidly making the best decision while remaining cognizant of changes that can be made as more data becomes available.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

NUTRON NESHAPs Dashboard

My project aims to enhance the Nuclear Material Tracking Application (NUTRON) software by integrating a tool for the National Emission Standards for Hazardous Air Pollutants (NESHAP) emissions calculations and presenting this data on a dashboard. Inefficiencies in the current NESHAP calculation process were addressed, which will result in more timely regulatory compliance efforts. Key improvements include revising the transfer request system, incorporating effective dose calculations, and developing a data visualization dashboard into NUTRON. The project involved creating a wireframe, preparing an Engineering Calculations and Analysis Report (ECAR), stakeholder meetings, and providing supplemental documentation. Key findings indicate that the proposed modifications will streamline the NESHAP calculation process, reduce human error, and provide REC personnel with accurate and timely data for material use determinations and dose estimations. Future work focuses on completing the NUTRON modifications and fully integrating the new features, ensuring a more efficient and reliable system for tracking and reporting nuclear material transfers.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

From Obsolete to Optimal (ATR Demineralized Water System)

My project was migrating a Human-Machine Interface (HMI) and Programmable Logic Controller (PLC) program from legacy software to a modern platform, enhancing operational efficiency and eliminating unsupported, obsolete equipment. Initially, I used a migration tool to transfer the programs to the new software, then manually updated variables to align with the new PLC. I optimized the PLC code by leveraging new scaling features in the I/O cards, removing over 45 obsolete rungs, and enhancing clarity with functional tag aliases. Next, I modernized the HMI's visuals with an updated color scheme and 3D buttons. I developed multiple interface versions iteratively refining them based on operator feedback. I also designed and created a dedicated "Rounds" screen incorporating insights from a previous intern’s project to streamline daily rounds. To improve clarity, consistency, and efficiency I redesigned the entire interface. Adding visual indicators to enhance operational awareness: red is used for values outside normal specifications, green for normal operating modes, yellow for manual mode, and grey for offline units. Additionally, I added a dynamic visual representation of tank levels, creating PLC logic to show the level in inches as well as inches and feet. I then incorporated a navigation sidebar to facilitate efficient screen transitions. Risk of human error was mitigated by defining narrow input ranges for tank level shutoff and password protecting setpoint changes. I also fixed issues with datatypes that caused inaccuracies in the original code. Overall, this comprehensive upgrade significantly enhanced the HMI's functionality, user experience, and operational reliability.

47 - OTHER INSTRUMENTATION↗
NASA NTRS1998

A Conceptual Framework for Predicting Error in Complex Human-Machine Environments

We present a Goals, Operators, Methods, and Selection Rules-Model Human Processor (GOMS-MHP) style model-based approach to the problem of predicting human habit capture errors. Habit captures occur when the model fails to allocate limited cognitive resources to retrieve task-relevant information from memory. Lacking the unretrieved information, decision mechanisms act in accordance with implicit default assumptions, resulting in error when relied upon assumptions prove incorrect. The model helps interface designers identify situations in which such failures are especially likely.

Freed, Michael↗
DOE OSTI2023

A formally certified end-to-end implementation of Shor’s factorization algorithm

Quantum computing technology may soon deliver revolutionary improvements in algorithmic performance, but it is useful only if computed answers are correct. While hardware-level decoherence errors have garnered significant attention, a less recognized obstacle to correctness is that of human programming errors—“bugs.” Techniques familiar to most programmers from the classical domain for avoiding, discovering, and diagnosing bugs do not easily transfer, at scale, to the quantum domain because of its unique characteristics. To address this problem, we have been working to adapt formal methods to quantum programming. With such methods, a programmer writes a mathematical specification alongside the program and semiautomatically proves the program correct with respect to it. The proof’s validity is automatically confirmed—certified—by a “proof assistant.” Formal methods have successfully yielded high-assurance classical software artifacts, and the underlying technology has produced certified proofs of major mathematical theorems. As a demonstration of the feasibility of applying formal methods to quantum programming, we present a formally certified end-to-end implementation of Shor’s prime factorization algorithm, developed as part of a framework for applying the certified approach to general applications. By leveraging our framework, one can significantly reduce the effects of human errors and obtain a high-assurance implementation of large-scale quantum applications in a principled way.

Science & Technology - Other Topics↗
NASA NTRSDate not supplied

Biocybernetic Closed-Loop System for Mitigating Hazardous States of Awareness

The past century of passenger flight has seen continuous improvement in aviation safety by the aerospace industry. However, while commercial aviation accident rates have continued to decline, human error-related incident and accident rates remain remarkably constant across all types of aviation (Shappell, et al., 2007). Unfortunately, this level of human error is unacceptable when considering projections for increased traffic volume (FAA, 2009), and is likely to yield more incidents and accidents unless a more complete understanding of operator error is achieved and remediations are implemented. One area of interest highlighted by researchers is Hazardous States of Awareness (HSAs) that can result from deficiencies in the design and inappropriate use of human-machine interfaces. Identifying and mitigating HSAs is critical for reducing operator errors. One promising approach uses psychophysiological measures which enable automated systems to adapt to the operator?s state and modify modes of operation to support optimal human performance (Scerbo, 2007). This paper will survey previous research and describe future directions for the application of psychophysiological measures of operators derived from cortical and autonomic assessment to perform real-time adaptive modulation of human-automation task mode mixes. The authors will present a summary of previous work done at NASA LaRC and Old Dominion University using a Psychophysiologically Adaptive System (PAS) in which the level of automation of the NASA Multi-Attribute Task Battery was modulated based on Engagement Indices derived from the users? electroencephalogram (Pope, Bogart, & Bartolome, 1995; for review see, Scerbo, Freeman, & Mikulka, 2003). Future theoretical and methodological directions for this type of closed-loop research will be discussed. Specifically, the capacity for this type of PAS to maintain effective operator state and to enable validation of candidate physiological indices will be described. Consideration will also be given to critical system characteristics (e.g., engagement indices, methods for invoking changes among system states, individual differences among users, etc.) that have been or still need to be studied. The potential of the PAS approach for interactive system design and prototyping will also be described. Examples of adaptive automation flight deck concepts in recent experiments will be highlighted and discussed.

Chad L Stephens↗
NASA NTRS1992

Effect of contrast on human speed perception

This study is part of an ongoing collaborative research effort between the Life Science and Human Factors Divisions at NASA ARC to measure the accuracy of human motion perception in order to predict potential errors in human perception/performance and to facilitate the design of display systems that minimize the effects of such deficits. The study describes how contrast manipulations can produce significant errors in human speed perception. Specifically, when two simultaneously presented parallel gratings are moving at the same speed within stationary windows, the lower-contrast grating appears to move more slowly. This contrast-induced misperception of relative speed is evident across a wide range of contrasts (2.5-50 percent) and does not appear to saturate (e.g., a 50 percent contrast grating appears slower than a 70 percent contrast grating moving at the same speed). The misperception is large: a 70 percent contrast grating must, on average, be slowed by 35 percent to match a 10 percent contrast grating moving at 2 deg/sec (N = 6). Furthermore, it is largely independent of the absolute contrast level and is a quasilinear function of log contrast ratio. A preliminary parametric study shows that, although spatial frequency has little effect, the relative orientation of the two gratings is important. Finally, the effect depends on the temporal presentation of the stimuli: the effects of contrast on perceived speed appears lessened when the stimuli to be matched are presented sequentially. These data constrain both physiological models of visual cortex and models of human performance. We conclude that viewing conditions that effect contrast, such as fog, may cause significant errors in speed judgments.

Stone, Leland S.↗