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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Reliability and Probabilistic Risk Assessment - How They Play Together

Since the Space Shuttle Challenger accident in 1986, NASA has extensively used probabilistic analysis methods to assess, understand, and communicate the risk of space launch vehicles. Probabilistic Risk Assessment (PRA), used in the nuclear industry, is one of the probabilistic analysis methods NASA utilizes to assess Loss of Mission (LOM) and Loss of Crew (LOC) risk for launch vehicles. PRA is a system scenario based risk assessment that uses a combination of fault trees, event trees, event sequence diagrams, and probability distributions to analyze the risk of a system, a process, or an activity. It is a process designed to answer three basic questions: 1) what can go wrong that would lead to loss or degraded performance (i.e., scenarios involving undesired consequences of interest), 2) how likely is it (probabilities), and 3) what is the severity of the degradation (consequences). Since the Challenger accident, PRA has been used in supporting decisions regarding safety upgrades for launch vehicles. Another area that was given a lot of emphasis at NASA after the Challenger accident is reliability engineering. Reliability engineering has been a critical design function at NASA since the early Apollo days. However, after the Challenger accident, quantitative reliability analysis and reliability predictions were given more scrutiny because of their importance in understanding failure mechanism and quantifying the probability of failure, which are key elements in resolving technical issues, performing design trades, and implementing design improvements. Although PRA and reliability are both probabilistic in nature and, in some cases, use the same tools, they are two different activities. Specifically, reliability engineering is a broad design discipline that deals with loss of function and helps understand failure mechanism and improve component and system design. PRA is a system scenario based risk assessment process intended to assess the risk scenarios that could lead to a major/top undesirable system event, and to identify those scenarios that are high-risk drivers. PRA output is critical to support risk informed decisions concerning system design. This paper describes the PRA process and the reliability engineering discipline in detail. It discusses their differences and similarities and how they work together as complementary analyses to support the design and risk assessment processes. Lessons learned, applications, and case studies in both areas are also discussed in the paper to demonstrate and explain these differences and similarities.

Safie, Fayssal↗
NASA NTRS2011

Use of Probabilistic Risk Assessment in Shuttle Decision Making Process

This slide presentation reviews the use of Probabilistic Risk Assessment (PRA) to assist in the decision making for the shuttle design and operation. Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and disciplined approach to identifying and analyzing risk in complex systems and/or processes that seeks answers to three basic questions: (i.e., what can go wrong? what is the likelihood of these occurring? and what are the consequences that could result if these occur?) The purpose of the Shuttle PRA (SPRA) is to provide a useful risk management tool for the Space Shuttle Program (SSP) to identify strengths and possible weaknesses in the Shuttle design and operation. SPRA was initially developed to support upgrade decisions, but has evolved into a tool that supports Flight Readiness Reviews (FRR) and near real-time flight decisions. Examples of the use of PRA for the shuttle are reviewed.

Boyer, Roger L.↗
DOE OSTI2024

Hazards and Probabilistic Risk Assessments of a Light-Water Reactor Coupled with Industrial Facilities

This report provides a roadmap and toolkit for site-specific risk assessments across a broad range of industrial customers co-located with nuclear power plants (NPPs). This report builds upon the body of work sponsored by the Department of Energy (DOE) Light-Water Reactor Sustainability (LWRS) Flexible Plant Operation and Generation Pathway that presented hazards assessment and generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors co-located with hydrogen production facilities. The report expands the hazards assessments to include other industrial facilities: an oil refinery, a methanol plant, a synthetic fuel (synfuel) plant, the production of synthetic gas (syngas) as part of the methanol and synfuel plants, and wood pulp and paper mills. All these facilities are specified through industrial process and requirements research performed by national laboratories, universities, and interaction with industry. Many of the processes used in this report are pre-conceptual designs to use for decarbonization of the current technology facilities. A process of failure modes and effects analysis (what can go wrong) and accidentology (what has historically gone wrong) was used to determine the hazards presented to the NPP by the addition of the HES and the industrial customer. Chemical properties of feedstocks and products are summarized as part of the hazards assessment. Example analysis procedures are provided for each of the hazard types identified. These deterministic analyses can be used to assess adherence to licensing criteria. They can also be used to meet other safety goals like protection of the public, workers, or industrial facility equipment. The probabilistic analysis consisted of three sizes of HESs modeled in a PRA to assess the impact on the initiating events (IE) and results of the PRA. The PRA results conclude that the resulting increases in IE frequencies are below the limits required for small changes to existing NPPs under 10 CFR 50.59.

08 HYDROGEN↗
NASA NTRS2001

Overview of Probabilistic Risk Assessment at NASA: Past, Present and Future

PRA (Probabilistic Risk Assessment) adds the "probability" dimension to the traditional "deterministic" dimension of engineering and safety analyses. Although PRA, as a discipline, has been the evolutionary product of only the last couple of decades, much of the underlying methodology has been in use for a long time. In fact, the concept of probability for describing the likelihood that rare events happen is ancient and dates from Ancient Greece. Our vision is for NASA to achieve world-class state-of-the-art capability in performing and using PRA in technical and management decisions. Since NASA is a leader in many technical fields in which it is involved, we hope, before long, to see NASA become a leader in this field also.

Stamatelatos, Michael G.↗
NASA NTRS2018

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless if the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the NASA PRA and present a future dynamic option for software and PRA. Space Launch Vehicle Software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data, respectively. Software uncertainty will also be discussed. We determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Novack, Steven↗
NASA NTRS2018

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk Assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless of whether the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the National Aeronautics and Space Administration (NASA) PRA and present a future dynamic option for software and PRA. Space Launch Vehicle software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data. Software uncertainty will also be discussed. It is determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Steven D. Novack↗
NASA NTRSDate not supplied

The Future of Integrated Performance Modeling in the Crew Health and Performance – Probabilistic Risk Assessment Project

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was a significant step forward in efforts to robustly quantify the risk to crew health for exploration missions. However, there remains a significant gap in the ability to comprehensively assess and characterize risk across the disparate functionalities and capabilities which comprise the Crew Health and Performance (CHP) system. The Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project seeks to characterize CHP risks by expanding beyond the foundation established by its PRA predecessors like IMM and MEDPRAT, that simulate medical risk metrics like loss of crew life and evacuations. One of the new risk measures in the CHP-PRA system is embodied in our Performance Risk Model (PRisM). PRisM provides a novel way of assessing crew performance on mission tasks, using a generalized framework which relates back to NASA-STD-3001. This approach allows PRisM to capture and integrate data from a variety of different domains into a single, unified, reproducible representation of astronaut performance. In this presentation, we discuss the motivation for the CHP-PRA work and give a high level overview of the goals of the project, outline the forward work for PRisM, and discuss collaboration opportunities for the community who might explore if their domain knowledge and data could be represented, integrated, and quantified with these tools, whose outcomes are metrics useful for supporting operational mission planning and decision making.

Lauren McIntyre↗
NASA NTRS2011

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment.

Stamatelatos,Michael↗
DOE OSTI2025

Dynamic probabilistic risk assessment and game theory for cyber security risk analysis in nuclear power plants

Nuclear Power Plants and energy systems have become more prone to cyber-attacks with their digitalization and the increased use of smart equipment. Hence, it is important to quantify the risk associated with cyber-attacks in such systems. Dynamic Probabilistic Risk Assessment which involves studying the evolution of a system due to random events and operator and attacker actions during a cyber-attack by employing a physics-based model of the system is a suitable framework to quantify cybersecurity risk in nuclear power plants. In addition to the plant dynamics, it is also important to model the strategies of the attackers and plant operators for an effective cybersecurity risk assessment. Game theory provides a set of necessary tools to model such strategic interactions. In this research, a framework that integrates dynamic probabilistic risk assessment with game theory for cybersecurity risk analysis in nuclear power plants is presented. The mathematical formulation is derived based on the theory of continuous event trees. We propose a game theory based action model, that utilizes physics-based rewards to define the strategies of attackers and operators at every decision epoch. As a case study, the risk associated with cyber-attacks on the digital components in the secondary side of a pressurized water reactor is studied using a reduced order model. A set of attacker actions and a set of operator actions are defined for the system. The operator and attacker interactions were modelled using simultaneous game, their action policies were computed using the concept of mixed strategy Nash equilibrium and the evolution of the system was studied.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Integration of Evidence Base into a Probabilistic Risk Assessment

INTRODUCTION: A probabilistic decision support model such as the Integrated Medical Model (IMM) utilizes an immense amount of input data that necessitates a systematic, integrated approach for data collection, and management. As a result of this approach, IMM is able to forecasts medical events, resource utilization and crew health during space flight. METHODS: Inflight data is the most desirable input for the Integrated Medical Model. Non-attributable inflight data is collected from the Lifetime Surveillance for Astronaut Health study as well as the engineers, flight surgeons, and astronauts themselves. When inflight data is unavailable cohort studies, other models and Bayesian analyses are used, in addition to subject matters experts input on occasion. To determine the quality of evidence of a medical condition, the data source is categorized and assigned a level of evidence from 1-5; the highest level is one. The collected data reside and are managed in a relational SQL database with a web-based interface for data entry and review. The database is also capable of interfacing with outside applications which expands capabilities within the database itself. Via the public interface, customers can access a formatted Clinical Findings Form (CLiFF) that outlines the model input and evidence base for each medical condition. Changes to the database are tracked using a documented Configuration Management process. DISSCUSSION: This strategic approach provides a comprehensive data management plan for IMM. The IMM Database s structure and architecture has proven to support additional usages. As seen by the resources utilization across medical conditions analysis. In addition, the IMM Database s web-based interface provides a user-friendly format for customers to browse and download the clinical information for medical conditions. It is this type of functionality that will provide Exploratory Medicine Capabilities the evidence base for their medical condition list. CONCLUSION: The IMM Database in junction with the IMM is helping NASA aerospace program improve the health care and reduce risk for the astronauts crew. Both the database and model will continue to expand to meet customer needs through its multi-disciplinary evidence based approach to managing data. Future expansion could serve as a platform for a Space Medicine Wiki of medical conditions.

Saile, Lyn↗
NASA NTRS2006

Exploration Health Risks: Probabilistic Risk Assessment

Maintenance of human health on long-duration exploration missions is a primary challenge to mission designers. Indeed, human health risks are currently the largest risk contributors to the risks of evacuation or loss of the crew on long-duration International Space Station missions. We describe a quantitative assessment of the relative probabilities of occurrence of the individual risks to human safety and efficiency during space flight to augment qualitative assessments used in this field to date. Quantitative probabilistic risk assessments will allow program managers to focus resources on those human health risks most likely to occur with undesirable consequences. Truly quantitative assessments are common, even expected, in the engineering and actuarial spheres, but that capability is just emerging in some arenas of life sciences research, such as identifying and minimize the hazards to astronauts during future space exploration missions. Our expectation is that these results can be used to inform NASA mission design trade studies in the near future with the objective of preventing the higher among the human health risks. We identify and discuss statistical techniques to provide this risk quantification based on relevant sets of astronaut biomedical data from short and long duration space flights as well as relevant analog populations. We outline critical assumptions made in the calculations and discuss the rationale for these. Our efforts to date have focussed on quantifying the probabilities of medical risks that are qualitatively perceived as relatively high risks of radiation sickness, cardiac dysrhythmias, medically significant renal stone formation due to increased calcium mobilization, decompression sickness as a result of EVA (extravehicular activity), and bone fracture due to loss of bone mineral density. We present these quantitative probabilities in order-of-magnitude comparison format so that relative risk can be gauged. We address the effects of conservative and nonconservative assumptions on the probability results. We discuss the methods necessary to assess mission risks once exploration mission scenarios are characterized. Preliminary efforts have produced results that are commensurate with earlier qualitative estimates of risk probabilities in this and other operational contexts, indicating that our approach may be usefully applied in support of the development of human health and performance standards for long-duration space exploration missions. This approach will also enable mission-specific probabilistic risk assessments for space exploration missions.

Rhatigan, Jennifer↗
NASA NTRS2011

Applications of the International Space Station Probabilistic Risk Assessment Model

Recently the International Space Station (ISS) has incorporated more Probabilistic Risk Assessments (PRAs) in the decision making process for significant issues. Future PRAs will have major impact to ISS and future spacecraft development and operations. These PRAs will have their foundation in the current complete ISS PRA model and the current PRA trade studies that are being analyzed as requested by ISS Program stakeholders. ISS PRAs have recently helped in the decision making process for determining reliability requirements for future NASA spacecraft and commercial spacecraft, making crew rescue decisions, as well as making operational requirements for ISS orbital orientation, planning Extravehicular activities (EVAs) and robotic operations. This paper will describe some applications of the ISS PRA model and how they impacted the final decision. This paper will discuss future analysis topics such as life extension, requirements of new commercial vehicles visiting ISS.

Grant, Warren↗
NASA NTRSDate not supplied

Investigating Risks Due to Artemis EVA Tempo Via Probabilistic Risk Assessment

Spaceflight operations pose unique challenges to crew health, safety, and resource management. As space agencies and private companies continue to push the boundaries of human exploration, it is essential to understand the risks associated with Extravehicular Activities (EVAs) and develop strategies to mitigate them. The tempo at which EVAs are conducted – the total number and frequency of these activities – can have a profound impact on medical risks, resource consumption, and overall mission success. Probabilistic risk assessment (PRA) provides a powerful framework for evaluating complex systems and identifying potential hazards. Our work employs the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) [1] to simulate mission events, occurrence and treatment of medical conditions, and track the utilization of resources. Coupled with the Evidence Library [2], a medical evidence base for exploration-class missions developed by the Exploration Medical Capability within NASA’s Human Research Program, we can estimate these risks with increased fidelity and optimize medical kit contents to meet specific mission requirements. This presentation provides a detailed examination of how EVA tempo influences medical risk estimates for a lunar surface design reference mission. A comprehensive analysis is conducted to assess the additional mass and volume burden imposed on medical kits required to maintain adequate levels of risk mitigation. Furthermore, we estimate the distribution of the number of successful EVAs completed based on the level of task impairment imposed by medical events and flight rules related to specific medical events, such as decompression sickness.

Modeling↗
DOE OSTI2021

Methods for the Enhancement of Security Probabilistic Risk Assessments

The US Department of Energy is exploring the methods associated with probabilistic risk assessment (PRA) and how they could be used to enhance physical security evaluations for nuclear facilities. A brief review of both international and US Nuclear Regulatory Commission physical security assessment methods is presented. From this review, and with an understanding of the technical elements associated with a traditional nuclear safety PRA, several observations are made pertaining to where physical security analysis methods could be enhanced. These observations principally support and enhance the answers within a physical security context to the risk triplet: (1) What can go wrong? (2) What is the likelihood? and (3) What are the consequences? Finally, benefits and technical challenges toward the development of a full security PRA are presented.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Probabilistic Risk Assessment of a Light Water Reactor Coupled with a High Temperature Electrolysis Hydrogen Production Plant

Two generic probabilistic risk assessments (PRA) for the addition of a heat extraction system (HES) addition to a light water reactor (LWR) are performed, one for a pressurized water reactor (PWR) and one for a boiling water reactor (BWR). The results investigate the applicability of the potential licensing approaches which do not require a full U.S. Nuclear Regulatory Commission (NRC) licensing review. The PRAs are generic, and some assumptions are made. Many conservative assumptions from a the preliminary PWR PRA report were eliminated using design data for both the HES and the high temperature electrolysis facility (HTEF). The results of the PRA indicate that the 10 CFR 50.59 licensing approach is justified due to the minimal increase in initiating event frequencies for all DBAs, none exceeding 5.6%. The PRA results for CDF and LERF support the use of RG 1.174 as further risk information that supports a change without a full LAR. Further insights provided through hazard analysis and sensitivity studies confirm with high confidence that the safety case for licensing an HES addition and a HTEF sited at 1.0 km from the NPP is strong and that the placement of a HTEF at 0.5 km is a viable case. Site specific information can alter these conclusions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A knowledge-informed large language model framework for U.S. nuclear power plant shutdown initiating event classification for probabilistic risk assessment

Identifying and classifying shutdown initiating events (SDIEs) is critical for developing shutdown probabilistic risk assessment for nuclear power plants. Existing computational approaches cannot achieve satisfactory performance due to the challenges of unavailable large, labeled datasets, imbalanced event types, and label noise. To address these challenges, we propose a hybrid pipeline that integrates a knowledge-informed machine learning model to prescreen non-SDIEs and a large language model (LLM) to classify SDIEs into four types. In the prescreening stage, we proposed a set of 44 SDIE text patterns that consist of the most salient keywords and phrases from six SDIE types. Text vectorization based on the SDIE patterns generates feature vectors that are highly separable by using a simple binary classifier. The second stage builds Bidirectional Encoder Representations from Transformers (BERT)-based LLM, which learns generic English language representations from self-supervised pretraining on a large dataset and adapts to SDIE classification by fine-tuning it on an SDIE dataset. The proposed approaches are evaluated on a dataset with 10,928 events using precision, recall ratio, F 1 score, and average accuracy. In conclusion, the results demonstrate that the prescreening stage can exclude more than 97% non-SDIEs, and the LLM achieves an average accuracy of 95.1% for SDIE classification.

99 - GENERAL AND MISCELLANEOUS↗