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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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189 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Machine Learning Enabled Quantitative Risk Assessment of Aerial Wildfire Response

Aerial wildfire operations are high risk and account for a large number of firefighter deaths. Increasing intensity of wildfires is driving a surge in aerial operations, while simultaneously there is growing interest in improving system safety and performance. In this work, wildfire aviation mishaps documented using the SAFECOM system are analyzed using a previously developed framework for hazard extraction and analysis of trends (HEAT). Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard are calculated, including frequency, rate, and severity. We examine whether these metrics change over time, and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on rate of occurrence and average severity. Results identify jumper operations hazards as high-risk, in addition to bucket drop failures, cargo let down failures, and severe weather as medium risk.

machine learning↗
NASA NTRS2022

Supporting Hazard Analysis for Wildfire Response Using fmdtools and MIKA

The System Wide Safety (SWS) Safety Demonstrator (SD) Series drives development of an increasingly capable In-Time Aviation Safety Management System (IASMS) focusing on humanitarian applications, starting with wildfire response (SD-1). The goals of this report are to (1) provide an early hazard analysis and mitigation evaluation of wildfire response to support these efforts and (2) provide a demonstration of capabilities of the Fault Model Design Tools (fmdtools) and Manager for Intelligent Knowledge Access (MIKA) tools. fmdtools provides a modeling, simulation, and resiliency analysis framework in which a wildfire response model, the System Modeling and Analysis of Resiliency in Scalable Traffic Management for Emergency Response Operations (SMARt-STEReO), is built. MIKA is an intelligent knowledge manager with several capabilities, including assisting in hazard analysis by extracting and analyzing hazards from historical incident reports. The following topics are covered in the report: Understanding Wildfire Hazard Dynamics. We provide a description and simulated examples of how hazards occur in the SMARt-STEReO model of wildfire response and their effect on its outcome. This provides a common mental model and focuses the analysis presented in the remainder of the report. Wildfire Hazard Identification. MIKA identifies wildfire hazards from three relevant datasets: the ICS-209-PLUS, SAFECOM, and SAFENET. Hazards are manually organized into a taxonomy and MIKA analyzes each hazard’s effects, likelihood, severity, and risk. Evaluating Mitigation Strategies. The SMARt-STEReO wildfire response model built in fmdtools evaluates a subset of identified hazards. Specifically, we simulate the effect of communications faults and equipment faults on operator safety, the effect of changing winds and flammability, and a scenario with multiple ignition points and heavy smoke. Tool Limitations and Usage Considerations. We provide a discussion of appropriate tool use cases as well as limitations and considerations for usage. The tool findings are used to synthesize recommendations for wildfire response operations, which can be captured as part of an IASMS. Key recommendations are as follows: Hazards are identified from a broad spectrum of sources including aircraft subsystems, operational sources, and ground crew operations. Highest risk operational environment hazards identified are Evacuations. The highest risk manned aerial operations hazard categorized is Jumper Operations Mishap. Ground crew hazards that are highest risk are Burns, Cargo Operations Overhead, Dehydration, Entrapment, Falling Objects, Heart Attacks, Heat Exhaustion, Inadequate Training or Certification, Vehicle Breakdown, and Vehicle Collision. Modelled containment failures arise from a mismatch between the difficulty of the firefighting scenario and the capacity (e.g., speed, effectiveness, awareness) of the response. In firefighting scenarios where containment is possible (e.g., because the fire does not spread too quickly), these mismatches can occur because of a change in environmental conditions (e.g., wind, flammability, etc) or because of planning, equipment, or communications faults. Improvements to communications increase the capacity of the firefighting response by reducing the time needed to respond to the fire. While surveillance does not increase this capacity by itself, it increases operator safety by increasing state awareness, enabling firefighters to evade approaching fires. Increasing both has a synergistic effect. In general, these performance and resilience increases generalize over fault scenarios as well as unforeseen changes to circumstances (i.e., wind, aridity, etc.). However, these improvements need to be designed so as not to make the system prone to persistent large-scale communications outages, which can reduce performance.

Hazard analysis↗
NASA NTRS2022

Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports

Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.

Machine learning↗
NASA NTRSDate not supplied

ISS EVA 23 Lessons Learned

Roughly 44 minutes into EVA 23, ESA Astronaut Luca Parmitano reported water inside his helmet on the back of his head. The EVA ground team, Luca and his spacewalking partner Chris Cassidy were unable to identify the water’s source. As they continued to work, the amount of water in Luca’s helmet increased and eventually migrated from the back of his head onto his face. EVA 23 was terminated early and the crew safely returned to the Space Station. What started as a normal EVA day on ISS became one of the most serious mishaps in the history of Spacewalking.

Chris Hansen↗
NASA NTRSDate not supplied

Identifying Outside Influences as Latent Factors to Risk in Human Performance

During the Acquisition Life Cycle for a program, there are several opportunities for the system in design to be adjusted in accordance with its changing landscape as it is being shaped by evolving policy and organizational culture. Human Systems Integration is integral to identifying these opportunities of change as there are a set number of activities that may account for altered operational states, human performance deviations, and overall component engagement if HSI is enacted early enough in the life cycle. Some changes that occur in the operational environment may not be accounted for since the organizational culture and practices are not currently a part of the HSI focus. Likewise, policy changes themselves from a top-to-bottom analysis may not have the appearance of effecting human performance until having gone through a trail-and-error period. Workarounds to adjust for unforeseen policy affects become the system's solution that usually includes changes in the training and education of the operators, maintainers, and support personnel. A system full of workarounds and off-normal practices, that cause operators to disregard the purpose of the design, coupled with the false notion that these activities are proven for successful system operation, is the very definition of “an accident waiting to happen”. Unforeseen changes in policies and practices that cause new and unusual activities to successfully and keep the system running, should be considered latent factors that may cause a potential mishap, and not part of the resilience that humans provide to the successful operation of the system. This presentation will explore how latent factors may find their way into system operations and how they can be identified and addressed.

Human Systems Integration↗
NASA NTRS2018

Risk Tolerance and Safety Culture: Minimizing the Risk of Catastrophe by Bringing the Lessons of Space Home

The Johnson Space Center has pursued integrating safety culture and risk management practices and techniques throughout a large organization with an extraordinarily diverse mission. Past mission and institutional failures have been used to inform NASA's safety culture and a implement a uniform risk management environment. Basics from the loss prevention industry have influenced NASA's approach to sustaining its unique, yet aging, infrastructure. Lessons learned at NASA can augment and enhance occupational safety, health and risk management capabilities within hazardous industries. By focusing on risk informed decision-making, building a pervasive safety culture, and accommodating human factors, NASA's safety capabilities have been specifically developed to minimize the risk of loss of life and loss of mission.

Mishap↗
NASA NTRS2023

Commercial Low-Earth Orbit Destination (CLD) Concept of Operations

This concept of operations describes: - High-level Government goals, objectives, and needs for the CLD program, vehicle(s), and missions, - Strategies, policies, and processes for developing, certifying, maintaining, using, and retiring CLDs in the context of Government use, and - Government and CLD partner roles, responsibilities, and interactions during various phases of Government CLD operations. The intent of this document is to describe the overall high-level concept of how the end-to-end CLD service will be used to convey the nature and scope of services the Government intends to procure from the CLD partner. It will provide insight into NASA’s concepts for CLD operations rather than to provide mature, explicit, or complete requirements. It addresses how the concept of operations will be used to meet NASA expectations from an operational perspective to facilitate an understanding of the end-to-end CLD service needs, goals, and objectives (NGOs). Formal CLD requirements and standards are detailed in the NASA Commercial LEO Development Program’s (CLDP) various requirements, interface, process, and standards documents, as well as the contractual Statements of Work (SOW) and Data Requirement Deliverables (DRD). As such, the Concept of Operations can use terms such as “should” or “may” or “expects to” since it is not a contractual requirements document. It exists solely to convey the initial nature and type of services the Government intends to procure and is not, per se, a requirements document.

Operations↗