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All sourcesNASA NTRSDOE OSTI
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Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines.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 NTRS2015

Dynamic Safety Cases for Through-Life Safety Assurance

We describe dynamic safety cases, a novel operationalization of the concept of through-life safety assurance, whose goal is to enable proactive safety management. Using an example from the aviation systems domain, we motivate our approach, its underlying principles, and a lifecycle. We then identify the key elements required to move towards a formalization of the associated framework.

Dynamic Safety Case↗
NASA NTRSDate not supplied

Creating Formal Characterizations ofRoutine Contingency Management inCommercial Aviation

Traditional approaches to safety management focus on collection of data describing unwanted states (i.e., accidents and incidents) and analysis of undesired behaviors (i.e., faults and errors) that precede those states. Thus, in the traditional view of safety, safety is both defined and measured by its absence, namely the lack of safety. In extremely high confidence systems like commercial air transport, however, opportunities to measure the absence of safety are relatively rare. Ironically, a critical barrier to measuring safety and the impact of mitigation strategies in commercial aviation is the lack of opportunities for measurement.

Intelligent Contingency Management,↗
DOE OSTI2023

A Comparison of Adverse Meteorology Inputs for Emergency Management and Safety Basis Dispersion Modeling Applications at SRS

The MELCOR Accident Consequence Calculation System (MACCS), used for safety basis accident analyses at SRS, implements adjustment factors to consider the increased vertical turbulence associated with local surface roughness and the increased lateral turbulence associated with time-based plume meander. The two models used for SRS emergency planning radiological consequence assessment, i.e., Hotspot and Puff/Plume, currently do not have an option to input or apply diffusion adjustments for meander or surface roughness. Adverse meteorology conditions at SRS have been determined as E stability class and 1.3 meters per second wind speed for ground level releases. This is the input used for emergency planning models. Contrastingly safety basis analyses use a different input, representing adverse meteorology values as F stability class and wind speeds of 1.3 meters per second (for a ground level release). This results in an apparent discrepancy between the two approaches. In this report we document the differing modeling assumptions, discuss the logic behind each modeling approach used to represent local roughness and plume meander conditions, and numerically compare dispersion coefficients and concentration estimates obtained from each set of model inputs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

ePORT, NASA's Computer Database Program for System Safety Risk Management Oversight (Electronic Project Online Risk Tool)

ePORT (electronic Project Online Risk Tool) provides a systematic approach to using an electronic database program to manage a program/project risk management processes. This presentation will briefly cover the standard risk management procedures, then thoroughly cover NASA's Risk Management tool called ePORT. This electronic Project Online Risk Tool (ePORT) is a web-based risk management program that provides a common framework to capture and manage risks, independent of a programs/projects size and budget. It is used to thoroughly cover the risk management paradigm providing standardized evaluation criterion for common management reporting, ePORT improves Product Line, Center and Corporate Management insight, simplifies program/project manager reporting, and maintains an archive of data for historical reference.

Johnson, Paul W.↗
NASA NTRSDate not supplied

Mars Sample Return: Risk Management & Sample Safety Assessment

Returning samples from Mars has long been a major planetary science objective due to the high scientific value and transformative potential of the resulting data. An exciting dimension of this objective is the potential for the detection of ancient microbiological life, and the possibility of improving our understanding of the evolution of habitable environments on Mars and the development of life on Earth. To ensure that returned samples meet stringent planetary protection requirements and do not expose Earth to potential biohazards, the joint NASA/ESA Sample Receiving Project (SRP) assembled the Sample Safety Assessment Protocol Tiger Team (SSAP-TT). Members were recruited with the specific goal of creating a multi-disciplinary and internationally distributed team of experts in their respective fields across the federal government, academia, and private industry. This team was chartered with reassessing previous sample safety assessment strategies, defining what constitutes a biological hazard, developing a protocol to test for potential biohazards, and establishing a statistical framework to determine if samples are “safe” for release. The team developed a three-step protocol, supported by a Bayesian statistical framework, to assess whether returned samples contain potential biohazards that could present a risk to Earth’s biosphere. Initial conclusions indicated that an effective and comprehensive safety assessment protocol is feasible using modern techniques and does not require an excessive amount of sample consumption or traditional microbiological detection methodology. Herein, we will present an overview of the MSR SRP, the proposed safety assessment protocol, and how aspects of this novel approach can be applied to biological assessment in healthcare product manufacturing practices.

Alvin L Smith↗
NASA NTRSDate not supplied

An Approach for Defining IASMS Services, Functions, and Capabilities

Assuring safety in the NAS with the inclusion of new entrants, such as Advanced Air Mobility (AAM), will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using autonomous vehicles. The focus of the In-time Aviation Safety Management System (IASMS) is to overcome AAM’s safety assurance challenges. The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs) designed to manage operational risks, identify unknown risks, and inform system designs. This paper describes an approach for defining SFCs based on technology trends in research, assessment of known and unknown risks in voluntary safety reports, and causal and contributing factors in aviation accidents and incidents. This approach would identify potential SFCs that further expand the Monitor, Assess, and Mitigate (M-A-M) functionality that represents the enabling framework of the IASMS. Safety implications that will result from integration of AAM in the transformation of the National Airspace System (NAS) were addressed in National Academies committees reports on AAM and IASMS. Development of a ConOps for IASMS was a top recommendation and can be represented as a reframing of safety assurance that builds on real-time alerting such as the Traffic Alert and Collision Avoidance System, and adds the more encompassing in-time temporal parameter in recognition of the different timelines for collecting and assessing safety data for risk mitigations. For example, mining for safety trends from data sources such as the Aviation Safety Information Analysis and Sharing system occurs over a longer time period. Research on AAM operations poses that SFCs can be designed to monitor the safety margin appropriate for AAM including with regards to the distance between current flight parameters and nominal ideal conditions. These in-time comparisons will become more complex as the density of operations increases at least in certain areas and can include planned and actual 4D trajectory, and in-time comparisons having implications on conflict modeling and prediction including expected and actual departure time, fix/waypoint crossing times, and arrival time. These comparisons would be integrated as part of SFCs that redefine and inform new safety margin. An increased safety margin improves management of operational risks while reducing the potential for anomalies. An increased safety margin also has implications for operator confidence in the certainty of its operations and trust in automation. Technology trends in research could be used to refine existing SFCs and define needs for additional SFCs that provide safety improvements to the design and operation of vehicles, airspace design, and operator performance requirements. NASA is developing innovative approaches to safeguard against major accidents and incidents that have occurred in the NAS and those anticipated with the inclusion of envisioned AAM operations. The innovations use operational performance data to monitor, detect, and predict flight variations exceeding safe nominal patterns, such as would be caused by navigational error, severe weather complications, or hijacking of UAS controls. These innovative approaches have high potential to prevent accidents and incidents in the new AAM era. It is anticipated that elements of the innovations will evolve into SFCs for the IASMS. Voluntary safety reports can be monitored to identify anomalies related to design or operational performance risks. Reports could be periodically monitored and assessed for specific topics. Reports might serve as weak signals or precursors indicative of emergent risk such as when combined with other safety information. The architecture could include SFCs that are based on voluntary safety reports recognizing the periodic temporal nature of data analysis. As previously mentioned, aviation accidents with their causal and contributing precursors can inform the need for SFCs in the IASMS. Accidents and incidents at San Francisco International Airport such as Asiana 214 and Air Canada 759 illustrate how combinations of different factors lead to increased risk. These types of precursors and different factors have implications on the types of SFCs that could be needed to monitor and manage different sources and types of design and operational risk. Continuing to assure the safety of AAM as designs and operations gain in complexity can be accompanied by defining SFCs that also increase in complexity. These SFCs can leverage information from findings and recommendations synthesized across on-going research, voluntary safety reports, and accident and incident reports. These SFCs can serve to refine accuracy of algorithms and resolve limitations with current practices. The IASMS architecture represents the framework for the SFCs and their critical role in safety assurance.

In-Time Aviation Safety Management System↗
NASA NTRS1971

System safety education focused on system management

System safety is defined and characteristics of the system are outlined. Some of the principle characteristics include role of humans in hazard analysis, clear language for input and output, system interdependence, self containment, and parallel analysis of elements.

Vernon L. Grose↗
NASA NTRS2024

Assessment of Some IASMS-relevant Data Sources for Aviation Safety

An In-time Aviation Safety Management System (IASMS) [1,2] is a set of services, functions, and capabilities (SFCs) necessary for monitoring known hazards and emergent risks, assessing safety data for anomalies, precursors, and trends, mitigating hazards that reach safety thresholds, and assuring efficacy of controls in mitigating hazards. An IASMS will continually monitor the NAS to collect data on the status of aircraft, air traffic management systems, weather, and airports. Within the NASA Aeronautics Research Mission Directorate (ARMD) System-Wide Safety (SWS) project’s technical challenge called In-time Aviation Safety Management Systems (IASMS) for Commercial Aviation Operations, which we often refer to as Technical Challenge 6 (TC-6), we have performed an assessment of several aviation data sources we have found that are relevant to assessing the safety of the National Airspace System (NAS) in the context of an IASMS. This assessment includes understanding the nature of the data themselves and using some data analytics tools on these data to show how they can be used to identify potential safety issues. We also describe how the data and analytics are part of a system that can allow for other data and analytics to be performed and for the results to be visualized for use by appropriate operators to identify potential safety issues and develop mitigations. This report is a step toward the ultimate goal of TC-6, which is to develop a prototype IASMS system that demonstrates the potential of an IASMS and inspire operators to build analogous systems to make the best possible use of the significant investments that they make in collecting, storing, and managingdata related to their operations.

aviation safety↗
DOE OSTI2020

Flammable Gas Generation and Control at the Idaho Completion Project Legacy Combustible Gas Generation - 20191

On April 11, 2018, four drums containing transuranic waste at the Idaho National Laboratory underwent over-pressurization, ejecting their lids and spreading radiological waste within a facility. An investigation has found that waste in the drums generated methane gas, which contributed to the event. Subsequent to the investigation, the potential for drums to have methane and other flammable analytes whose concentrations could approach or exceed the lower flammability limit (LFL) and the adequacy of the controls to prevent or mitigate a possible deflagration was evaluated. An extensive review of the historical records was performed to determine how many drums exceed the LFL. The historical record identified a small quantity of drums that exceeded the LFL for xylene, hydrogen, and methane. The primary codified applicable code or standard for handling drums is 29 CFR 1910.120 (j) 'Handling Drums and Containers.' It is used throughout the commercial and government sector. This code is integrated throughout the Fluor Idaho safety management programs and procedures to control the Standard Industrial Hazards (SIHs) associated with drum and container handling. Example requirements include: - Drums and containers are inspected, and their integrity assured prior to being moved. - Site operations are organized to minimize the amount of drum or container movement. - When there is a reasonable possibility of flammable atmospheres being present, material handling equipment and hand tools are of the type to prevent sources of ignition. Drums and containers under pressure, as evidenced by bulging or swelling, are not moved until the cause for excess pressure is determined and appropriate containment procedures have been implemented to protect employees from explosive relief of the drum. Drums which exceed the WIPP FGA limit have NCRs associated with them. Once a drum fails flammable gas sampling, an NCR is initiated. The NCR drives placement into NCR dense pack rows. A standing order drives placement of the FGA failures to a single planer segregation location. These drums are not allowed for further processing or shipment until the NCR has been cleared. Once in the single planer rows, follow-up FGA testing is performed by CCP. The NCR is dispositioned by follow-up sampling occurring after adequate time has been provided for the gas to diffuse through the filter assembly, or, in some cases, the drums are repackaged into additional drums to reduce the source term. To date, the ICP project has performed approximately 1.5 million drums moves without a deflagration event due to combustible gas generation. Flammable gas generation in a transuranic waste drum is not unique to the ICP but is common across the DOE complex. Based on the experience at Idaho, application of industry standards is sufficient to control the risk of drum deflagration due to drum movements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Implications of Safety and Operational Features of Small, Advanced Reactors for the Evaluation of Important Human Actions

The design and operational characteristics of non-light water reactors are likely to change the role of human actions in safety function management and the types of human actions that are deemed important. The objectives of this report are to: • Identify the implications of small, advanced reactor design characteristics on human performance and the changing role of human actions in the management of safety functions. • Identify the methods that may be used to identify important human actions. • Identify how HFE safety reviewers can help ensure that the methods adequately model human actions to identify those that are important to safety. We identified the implications of small, advanced reactor characteristics on the role of personnel in safety function management. Then we addressed how designers can identify which human actions are important to safety using both probabilistic risk assessment (PRA) and deterministic analyses. PRA identifies important human actions using risk-importance criteria. Deterministically identified important human actions include those identified by analyses of situations such as transients and accidents and defense in depth. In all cases, the acceptability of the analyses is dependent on the modeling, quantification, and criterion selection to determine which human actions are important. How well the designers address these processes determines the acceptability of their methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2005

Bridging the Divide between Safety and Risk Management for your Project or Program

This presentation will bridge the divide between these separate but overlapping disciplines and help explain how to use Risk Management as an effective management decision support tool that includes safety. Risk Management is an over arching communication tool used by management to prioritize and effectively mitigate potential problems before they concur. Risk Management encompasses every kind of potential problem that can occur on a program or project. Some of these are safety issues such as hazards that have a specific likelihood and consequence that need to be controlled and included to show an integrated picture of accepted) mitigated, and residual risk. Integrating safety and other assurance disciplines is paramount to accurately representing a program s or projects risk posture. Risk is made up of several components such as technical) cost, schedule, or supportability. Safety should also be a consideration for every risk. The safety component can also have an impact on the technical, cost, and schedule aspect of a given risk. The current formats used for communication of safety and risk issues are not consistent or integrated. The presentation will explore the history of these disciplines, current work to integrate them, and suggestions for integration for the future.

Lutomski, Mike↗
NASA NTRSDate not supplied

Flight Testing of In-Time Safety Assurance Technologies for UAS Operations

Ongoing research at NASA is driven by a strategic plan defined by the Aeronautics Research Mission Directorate and a vision for future In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. In both visions, system safety awareness and provision are expanded through increased access to relevant data; integrated analysis and predictive capabilities; improved real-time detection and alerting of domain-specific hazards; decision support, and in some cases, automated risk mitigation strategies. One primary research focus is to develop means by which more timely (i.e., “in-time”) actions may be taken to mitigate precursors, anomalies, or trends that are observed during operations. In this paper, we describe such means as a collection of Services, Functions, and Capabilities (SFCs) that are supported by an underlying information system. For example, an integrated risk assessment capability is envisioned that continuously monitors safety-related metrics and margins and recommends timely operational changes. Assessment functions and/or services can be based on data analytics and predictive models derived from heterogeneous data sets that span relevant indicator metrics and their time histories. Likewise, on-board functions can identify and reduce susceptibility to precursor conditions that have led (and can lead) to aircraft loss-of-control or out-of-control accidents. This paper summarizes development and testing of such an information system tailored to hazards anticipated for future highly autonomous flight missions near and over densely populated areas. Testing is accomplished via simulation and by using small, unmanned aircraft operating over a test range at NASA’s Langley Research Center. Flight plans and test scenarios are defined to emulate several use-cases, including package delivery; reconnaissance; fire management; and urban air taxi vertiport operations. Two test phases are summarized with Phase 1 occurring in (2019-2020) and Phase 2 ongoing (2021-present). Results focus on SFC performance, technology readiness level assessment, and requirements discovery/validation. Companion papers are cited throughout for additional details on the recent testing.

safety management↗
NASA NTRS2022

SWS TechTalk Systems Modelling for SWS Safety Demonstrator 1 (SD-1): Wildfire Response Operations

Build out a System Safety Model for System-Wide Safety’s first Technical Challenge 5 (TC5) Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System in emerging wildfire response operations. Supporting the Systems Engineer and using Model-Based Systems Engineering (MBSE) practices, languages, and tools. Summer 2022 SD1 milestones: TC5-03-01 Initial Stakeholder Engagement Concept of Operations Development milestone and the upcoming TC5-03-02 Identification of key gaps and prioritization of NASA, SWS milestone.

model-based systems engineering↗
NASA NTRS1993

Medical surveillance and biological monitoring of lead exposed employees

Employee health protection is an employer responsibility. The multi-faceted aspects of employee protection from the potentially harmful effects of inorganic lead sometimes stress the relationships of several employer units. These include supervision and management, safety, operations and maintenance, engineering, environmental health, environmental management, and occupational medicine. The administrative aspects of program development are discussed. The purpose is to emphasize the opportunity for cooperation by all of the employee health components in developing an optimum surveillance and protection program.

Ferguson, E. B.↗
DOE OSTI2025

Synopsis of NREL's Automated Mobility District (AMD) Research Program and Associated Publications

An automated mobility district (AMD) envisions a system of integrated mobility options that serves major activity centers such as campuses, central business districts, and large medical facilities. The National Renewable Energy Laboratory (NREL) has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings. This document provides a synopsis of findings revealed over the last three phases of work, which have yielded insights into the creation and management of AMDs anticipated to use automated vehicle (AV) technology over the next decade. Phase I and Phase II tracked the deployment and lessons learned from 10 early-stage demonstrations of automated shuttle deployments, and their associated insights into the challenges for automated driving systems to achieve safe operations within district-scale deployments. Phase III began in-depth investigations of critical subsystem components, as automation, electrification, and on-demand service continue to converge within initial AMD operations. These directed studies focus on elements of electrification, curbfront/station management, the role of infrastructure sensing, and overall integration of AMD safety management in central, simultaneous coordination of multiple AMD fleets. Future research in AMDs includes systems engineering methodology (more frequently referred to as "digital twins") for planning, design, testing, and ongoing operation of AMDs; location (or co-location) of management functions; and human supervision and passenger communications for safety and security in unattended vehicles. The synopsis references the foundational research products (papers and presentations) that have been published through conference proceedings, journal articles, and NREL reports.

33 ADVANCED PROPULSION SYSTEMS↗