SEARCH · Search NASA

Results for “pra”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS2013

Practical Application of PRA as an Integrated Design Tool for Space Systems

This paper presents the application of the first comprehensive Probabilistic Risk Assessment (PRA) during the design phase of a joint NASA/NOAA weather satellite program, Geostationary Operational Environmental Satellite Series R (GOES-R). GOES-R is the next generation weather satellite primarily to help understand the weather and help save human lives. PRA has been used at NASA for Human Space Flight for many years. PRA was initially adopted and implemented in the operational phase of manned space flight programs and more recently for the next generation human space systems. Since its first use at NASA, PRA has become recognized throughout the Agency as a method of assessing complex mission risks as part of an overall approach to assuring safety and mission success throughout project lifecycles. PRA is now included as a requirement during the design phase of both NASA next generation manned space vehicles as well as for high priority robotic missions. The influence of PRA on GOES-R design and operation concepts are discussed in detail. The GOES-R PRA is unique at NASA for its early implementation. It also represents a pioneering effort to integrate risks from both Spacecraft (SC) and Ground Segment (GS) to fully assess the probability of achieving mission objectives. PRA analysts were actively involved in system engineering and design engineering to ensure that a comprehensive set of technical risks were correctly identified and properly understood from a design and operations perspective. The analysis included an assessment of SC hardware and software, SC fault management system, GS hardware and software, common cause failures, human error, natural hazards, solar weather and infrastructure (such as network and telecommunications failures, fire). PRA findings directly resulted in design changes to reduce SC risk from micro-meteoroids. PRA results also led to design changes in several SC subsystems, e.g. propulsion, guidance, navigation and control (GNC), communications, mechanisms, and command and data handling (C&DH). The fault tree approach assisted in the development of the fault management system design. Human error analysis, which examined human response to failure, indicated areas where automation could reduce the overall probability of gaps in operation by half. In addition, the PRA brought to light many potential root causes of system disruptions, including earthquakes, inclement weather, solar storms, blackouts and other extreme conditions not considered in the typical reliability and availability analyses. Ultimately the PRA served to identify potential failures that, when mitigated, resulted in a more robust design, as well as to influence the program's concept of operations. The early and active integration of PRA with system and design engineering provided a well-managed approach for risk assessment that increased reliability and availability, optimized lifecyc1e costs, and unified the SC and GS developments.

Kalia, Prince↗

DOE OSTI2020

Dynamic PRA Methods to Evaluate the Impact on Accident Progression of Accident Tolerant Fuels

Accident tolerant fuels (ATFs) are new nuclear fuels developed in response to the accident at the Fukushima power station in March 2011. The goal of ATFs is to withstand accident scenarios through better performance compared to currently employed fuels (e.g., small-scale hydrogen generation). This paper targets a method for evaluating and comparing ATF performance from a probabilistic risk assessment (PRA) perspective by employing a newly developed combination of event trees and dynamic PRA methods. Compared to classical PRA methods based on event trees and fault trees, dynamic PRA can evaluate with higher resolution the safety impacts of physics dynamics and the timing/sequencing of events on the accident progression without the need to introduce overly conservative modeling assumptions and success criteria. In this paper, we analyze the impact on the accident progression of three different cladding configurations for two initiating events [a large break loss-of-coolant accident (LB-LOCA) and a station blackout (SBO)] by employing dynamic PRA methods. The goal is to compare the safety performance of ATFs (FeCrAl and Cr-coated cladding) and the currently employed Zr-based clad fuel. We employ two different strategies. The first focuses on the identification of success criteria discrepancies between the accident sequences generated by the classical PRA model and the set of simulation runs generated by dynamic PRA using ATF. The second one, on the other hand, directly uses dynamic PRA to evaluate the impact of timing of events (e.g., recovery actions) on accident progression. In conclusion, by applying these methods to the LB-LOCA and SBO initiating events, we show how dynamic PRA methods can provide analysts with detailed and quantitative information on the safety impact of ATFs.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Convolution Correction Factor Adjustments on Static PRA Models for Event Assessment

Current probabilistic risk assessment (PRA) models will contain cut sets that have time-constrained basic events such as switched components, recovery of failed components, and failures in time. For these cut sets that have multiple time-constrained events that interact (e.g., offsite power recover at the same time as multiple diesel generators fail to run), there is the possibility that the minimal cut set quantified result is too large. Since current PRA models typically assume that the accident scenario starts at “time zero,” the fails-to-run basic events use this starting time as the basis for their respective reliability models. Over time, the standard practice was to recognize this issue and accept it as part of the conservatism built into the event tree/fault tree PRA models. However, now that these same PRA models are being used in a regulatory forum, this conservatism needs to be addressed. Further, this issue exists not only for the baseline PRA results, but for deficiencies that have been observed or those that have the potential of occurrence, whether the deficiency is complete failure or just a degraded condition. We have found that this conservatism becomes very important when evaluating scenarios such as station blackout sequences and other loss of offsite power sequences. This paper will address the theory and implemented process used by the Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) PRA software. Starting with SAPHIRE version 8, the process that was developed is to calculate a “convolution correction factor” based on three distinct calculations. The first calculation is the cut set based frequency calculated via the standard event tree/fault tree PRA model (assuming all run failures start at time equal to zero). The second calculation is to perform the exact time-constrained calculation by integrating the component failure models over the time period of interest (e.g., time to recover, a mission time). For example, this exact calculation for the case of the diesel generators is to convolve their failure models with the offsite power recovery model to obtain the exact frequency. The third calculation is to produce the convolution correction factor by dividing the exact frequency calculation by the standard cut set frequency. This convolution correction factor is then applied to the PRA cut sets in order to remove the time-constrained conservatism. The process used to obtain the convolution correction factor for the baseline PRA is important; however, in this paper we also describe what happens when there is degradation of a component or a potential deficiency. These issues become very important in order to ensure that the convolution correction factor is re-calculated correctly. If the correction factor does not get re-calculated and the same baseline convolution correction factor is used, the final analysis result could be over conservative or under conservative. This paper will discuss how these issues are addressed and provide examples on how SAPHIRE automatically makes the necessary adjustments. MathCad verification results can be computed using the provided equations and input data.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2016

Towards a Methodology and Tooling for Model-Based Probabilistic Risk Assessment (PRA)

A Probabilistic Risk Assessment (PRA) aims to identify and assess potential risks to system technical performance requirements for the purpose of furnishing risk insights into project decisions. PRAs have traditionally been conducted manually using software with an isolated data model. As system complexity rises it becomes difficult to ensure consistency between a PRA, the evolving system design, and other engineering analyses; the techniques for conducting PRAs must evolve to meet this challenge. This work presents progress towards a methodology and tooling for conducting a PRA by leveraging data in the system model, embedded for other purposes and analyses, to conduct a PRA. An approach for identifying the appropriate probabilistic equation for each risk scenario from a standard library is presented, which is a significant step towards the quantification of the likelihood of a risk scenario occurrence. The final calculation of the likelihood of occurrence is left as an item of future work. We also present the development of preliminary tooling to carry out the methodology on a well-formed system model. The information needed to conduct the PRA is embedded in a consistent manner in a system model, so the model-based PRA can be regularly executed as the system model changes. The ability to modify the PRA in concert with lifecycle evolution affords a project the opportunity to track the extent to which system modification impacts compliance with requirements. These aspects of this model-based PRA methodology make it capable of managing risk in increasingly complex technical systems.

Schreiner, Samuel S.↗

DOE OSTI2017

ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY AVERAGE COMPONENT PERFORMANCE DATA

In the nuclear industry, risk monitors are intended to provide a point-in-time estimate of the system risk given the current plant configuration. Current risk monitors are limited in that they do not properly take into account the deteriorating states of plant equipment, which are unit-specific. Current approaches to computing risk monitors use probabilistic risk assessment (PRA) techniques, but the assessment is typically a snapshot in time. Living PRA models attempt to address limitations of traditional PRA models in a limited sense by including temporary changes in plant and system configurations. However, information on plant component health are not considered. This often leaves risk monitors using living PRA models incapable of conducting evaluations with dynamic degradation scenarios evolving over time. There is a need to develop enabling approaches to solidify risk monitors to provide time and condition-dependent risk by integrating traditional PRA models with condition monitoring and prognostic techniques. This paper presents estimation of system risk evolution over time by integrating plant risk monitoring data with dynamic PRA methods incorporating aging and degradation. Several online, non-destructive approaches have been developed for diagnosing plant component conditions in nuclear industry, i.e., condition indication index, using vibration analysis, current signatures, and operational history [1]. In this work the component performance measures at U.S. commercial nuclear power plants (NPP) [2] are incorporated within the various dynamic PRA methodologies [3] to provide better estimates of probability of failures. Aging and degradation is modeled within the Level-1 PRA framework and is applied to several failure modes of pumps and can be extended to a range of components, viz. valves, generators, batteries, and pipes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Assessment of dynamic PRA techniques with industry average component performance data - 208

In the nuclear industry, risk monitors are intended to provide a point-in-time estimate of the system risk given the current plant configuration. Current risk monitors are limited in that they do not properly take into account the deteriorating states of plant equipment, which are unit-specific. Current approaches to computing risk monitors use probabilistic risk assessment (PRA) techniques, but the assessment is typically a snapshot in time. Living PRA models attempt to address limitations of traditional PRA models in a limited sense by including temporary changes in plant and system configurations. However, information on plant component condition is not considered. This often leaves risk monitors using living PRA models incapable of conducting evaluations with dynamic degradation scenarios evolving over time. There is a need to develop enabling approaches to solidify risk monitors to provide time- and condition-dependent risk by integrating traditional PRA models with condition monitoring and prognostic techniques. This paper presents an estimation of system risk evolution over time by integrating plant risk monitoring data with dynamic PRA methods that incorporate aging and degradation. Several online, nondestructive approaches have been developed for diagnosing plant component conditions in the nuclear industry, i.e., using a condition indication index, vibration analysis, current signatures, and operational history . In this work, the component performance measures at U.S. commercial nuclear power plants are incorporated within the dynamic PRA methodologies to provide better estimates of the probability of failures. Aging and degradation are modeled within the Level 1 PRA framework, are applied to several failure modes of pumps, and can be extended to a range of components, namely, valves, generators, batteries, and pipes. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools and data for modeling this risk is highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data’s high uncertainty. This report outlines initial work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant models. This research paves the way for the development of methods to reduce fire data uncertainty used in modeling. The Nuclear Regulatory Commission has mandated that nuclear power plants perform fire risk modeling. However, there are several issues with the current risk modeling implementation that affect the results. Approved modeling methods can be overly conservative and often do not match plant experience. Also, the data used in the modeling can have high uncertainties and is influenced by expert judgement. To evaluate input data uncertainty, researchers performed an initial review of several fire experiments done at Sandia National Laboratories. Uncertainties for fire data can come from many sources, such as experiment design constraints, environmental conditions, or other plant-specific aspects. There are many different significant and insignificant parameters driving the uncertainty. Additionally, the uncertainty of the different input data used in the fire modeling could have a significant or insignificant effect on the entire plant risk. A four-step methodology was developed to perform Integrated Probabilistic Risk Assessment Importance Ranking. A demonstration case using these steps was set up and three of the four steps were completed in fiscal year (FY) 2019 and the fourth step done FY 2020. These steps are: 1. The qualitative analysis of potential sources was conducted with the following items identified for the demonstration. • Maximum heat release rate • Time to maximum heat release rate • Duration of max heat release rate • Time to decay • Thermal conductivity of concrete • Specific heat of concrete • Density of concrete • Cable jacket thickness 2. A quantitative characterization of dominant sources of uncertainty was performed. A list of distributions and determined values of the dominant sources is shown in Appendix A. 3. A quantitative screening of the potential sources of uncertainty using Morris Elementary Effects Analysis was completed. An experimental model using the physics-based fire modeling tool Fire Dynamics Simulator was developed and coupled with the Risk Analysis Virtual Environment. The Morris analysis identified at least two parameters that can be eliminated as significant contributors (specific heat of concrete and cable jacket thickness). 4. Global importance measure (Global IM) analysis to generate a comprehensive ranking based on their influence on the plant risk. In this research, a moment-independent Global IM is used since it can address (a) uncertainty in the input parameters of the fire model, (b) uncertainty in the risk outputs, and (c) non-linearity and interactions among input parameters in the fire model, more accurately than the correlation-based and variance-based global methods. The observations from the research showed that, depending on the initial and boundary conditions of the fire scenarios, fire-induced damage could have a very small probability and could be dominated by the tail of the uncertainty distribution; hence, the accuracy of the correlation-based and variance-based methods is questionable. The moment-independent Global IM analysis in this research provides a better understanding of how experimental uncertainty data affects industry’s plant models and where improvements in that data will have the largest benefit for improving fire modeling accuracy in causing core damage. Among the five unscreened parameters obtained from the Morris EE analysis, the Global IM analysis results for the case study indicated that max heat release rate and fire location are the most important parameters. The report also outlines benefits of using a unified computational platform that integrates the underlying simulations (e.g., a fire progression model), quantitative screening (using the Morris EE method), and the Global IM analysis. A unified platform can (i) facilitate the ranking of input parameters considering multiple key fire scenarios simultaneously, rather than considering one scenario at a time, (ii) contribute to more explicit and accurate treatment of dependencies at multiple levels of Fire PRA, (iii) facilitate the sampling-based uncertainty quantification for Fire PRA, and (iv) help generating both “industry-wide” and "plant-specific" ranking of uncertainty sources in Fire PRA. Future research should be done to include additional parameters such as detection/suppression or cable fire spread. Adding a PRA software such as SAPHIRE to the RAVEN platform would help with plant model integration and improve treatment of fire-induced dependency. The I-PRA risk importance ranking methodology offered in this report can provide valuable information for efficiently (a) enhancing the realism of Fire PRA for existing plants and (b) supporting the development of Dynamic Fire PRA for advanced reactors and new plants.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

DYNAMIC PRA WITH COMPONENT AGING AND DEGRADATION MODELED UTILIZING PLANT RISK MONITORING DATA

In the nuclear industry, risk monitors are intended to provide a point-in-time estimate of the system risk given the current plant configuration. Current risk monitors are limited in that they do not properly take into account the deteriorating states of plant systems, structures, and components (SSCs), which are unit-specific. Current approaches to computing risk monitors use probabilistic risk assessment (PRA) techniques, but the assessment is typically a snapshot in time. Living PRA models attempt to address limitations of traditional PRA models in a limited sense by including temporary changes in plant and system configurations. However, information on plant component health are not considered. This often leaves risk monitors using living PRA models incapable of conducting evaluations with dynamic degradation scenarios evolving over time. There is a need to develop enabling approaches to solidify risk monitors to provide time and condition-dependent risk by integrating traditional PRA models with condition monitoring and prognostic techniques [1]. This paper presents a novel hazard-rate based model of incorporating degradation of nuclear SSCs into an existing PRA model. Time-varying instantaneous hazard-rate of component is used to model degradation based on time-varying performance measure. The instantaneous hazard-rate is used to determine and update time-varying probability of failure of the components in their PRA models. The method is applied to estimate evolution of probability of failure of an in-service pump using vibration measurement taken while the pump undergoes degradation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2010

A Cabonaceous Chondrite Dominated Lithology from the HED Parent; PRA 04401

The paired howardite breccias Mt. Pratt (PRA) 04401 and PRA 04402 are notable for their high proportion of carbonaceous chondrite clasts [1]. They consist predominantly of coarse (0.1-7 mm) diogenite (orthopyroxene), eucrite (plagioclase + pyroxene), and carbonaceous chondrite clasts set in a finer grained matrix of these same materials. Coarse C-chondrite clasts up to 7 mm are composed mainly of fine-grained phyllosilicates with lesser sulfides and high-mg# anhydrous magnesian silicates. Most of these clasts appear to be texturally consistent with CM2 classification [1] and some contain relict chondrules. The clasts are angular and reaction or alteration textures are not apparent in the surrounding matrix. PRA 04401 contains about 70 modal% C-chondrite clasts while PRA 04402 contains about 7%. Although many howardites are known to contain abundant C-chondrite clasts [2,3,4], PRA 04401 is, to our knowledge, the most chondrite-rich howardite lithology identified to date. Low EPMA totals from CM2-type clasts in other howardites suggest that they frequently contain 10 wt% or more water [2], a figure consistent with their mineralogy. PRA 04401, therefore, demonstrates the potential for hydrous lithologies with greater than 5 wt% water to occur locally within the nominally anhydrous HED parent body. Since the origin of this water is xenogenic, it might therefore be concentrated in portions of the asteroid surface where it would be more readily observable by remote sensing techniques. We plan to further examine C-chondrite clasts in PRA 04401/2 with the intent of establishing firm chemical classification, estimating water content, and evaluating their relationship with the host breccia. To help place them in context of the HED parent, we will also compare these breccias with other howardites to evaluate which lithologies are likely to be more prevalent on the asteroid surface.

Herrin, Jason S.↗

DOE OSTI2017

DYNAMIC PRA OF A MULTI-UNIT PLANT

Dynamic Probabilistic Risk Analysis (PRA) methods couple stochastic methods (e.g., RAVEN, ADAPT, ADS, MCDET) with safety analysis codes (RELAP5-3D, MELCOR, MAAP) to determine risk associated to complex systems such as nuclear plants. Compared to classical PRA methods, which are based on static logic structures (e.g., Event-Trees, Fault-Trees), they can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. Recently, special attention has been given to nuclear plants which consist of multiple units and, in particular, on the safety impact of system dependencies, shared systems and common resources on core damage frequencies. In the literature, while classical PRA methods have been employed to model multi-unit plants, Dynamic PRA methods have never been applied to analyze a full multi-unit model. This paper presents a PRA analysis of a multi-unit plant using Dynamic PRA methods. We employ RAVEN as stochastic method coupled with RELAP5-3D. The plant under consideration consists of the three units and their associated spent fuel pools (SFPs). The studied initiating event is a seismic induced station blackout event. We will describe in detail how the multi-unit plant has been constructed and, in particular, how unit dependencies and shared resources are modeled.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA

Risk Monitors (RMs) are software tools that are employed in nuclear power plants to monitor the safety properties of the plant itself during operation. These safety properties are generated using classical PRA methods which are typically based on static Boolean structures such as Fault-Trees (FTs) and Event-Trees (ETs). The outcomes of these methods are Core Damage Frequency (CDF) and risk importance values of plant components (e.g., valves and pumps). Examples of Risk Importance Measures (RIMs) commonly used are: Risk Achievement Worth (RAW), Risk Reduction Worth (RRW), Birnbaum (B) and Fussell-Vesely (FV) [1,2]. In a RM these values are updated in real time once plant components status is updated (e.g., under maintenance, off-line, testing). In addition, RMs can be used to optimize maintenance schedules so those safety features of the plant are below plant standard values. This paper presents an approach to create temporal profiles of RIMs (i.e., RM capabilities) given a temporal change of the plant configuration. Instead of employing data generated by ETs and FTs, the proposed approach will employ data generated by Dynamic PRA methods. In contrast to classical PRA methods, Dynamic PRA methods couple stochastic methods with safety analysis codes to determine risk associate to complex systems such as nuclear plants. Compared to classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. We will describe how classical RIMs can be generated by Dynamic PRA data and then we will show how the temporal profiles of the RIMs can be generated given a temporal profile of the system configuration.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2006

Probabilistic Risk Assessment (PRA): A Practical and Cost Effective Approach

The Lunar Reconnaissance Orbiter (LRO) is the first mission of the Robotic Lunar Exploration Program (RLEP), a space exploration venture to the Moon, Mars and beyond. The LRO mission includes spacecraft developed by NASA Goddard Space Flight Center (GSFC) and seven instruments built by GSFC, Russia, and contractors across the nation. LRO is defined as a measurement mission, not a science mission. It emphasizes the overall objectives of obtaining data to facilitate returning mankind safely to the Moon in preparation for an eventual manned mission to Mars. As the first mission in response to the President's commitment of the journey of exploring the solar system and beyond: returning to the Moon in the next decade, then venturing further into the solar system, ultimately sending humans to Mars and beyond, LRO has high-visibility to the public but limited resources and a tight schedule. This paper demonstrates how NASA's Lunar Reconnaissance Orbiter Mission project office incorporated reliability analyses in assessing risks and performing design tradeoffs to ensure mission success. Risk assessment is performed using NASA Procedural Requirements (NPR) 8705.5 - Probabilistic Risk Assessment (PRA) Procedures for NASA Programs and Projects to formulate probabilistic risk assessment (PRA). As required, a limited scope PRA is being performed for the LRO project. The PRA is used to optimize the mission design within mandated budget, manpower, and schedule constraints. The technique that LRO project office uses to perform PRA relies on the application of a component failure database to quantify the potential mission success risks. To ensure mission success in an efficient manner, low cost and tight schedule, the traditional reliability analyses, such as reliability predictions, Failure Modes and Effects Analysis (FMEA), and Fault Tree Analysis (FTA), are used to perform PRA for the large system of LRO with more than 14,000 piece parts and over 120 purchased or contractor built components.

Lee, Lydia L.↗

NASA NTRS2011

Medical Updates Number 5 to the International Space Station Probability Risk Assessment (PRA) Model Using the Integrated Medical Model

The Integrated Medical Model (IMM) Project has been developing a probabilistic risk assessment tool, the IMM, to help evaluate in-flight crew health needs and impacts to the mission due to medical events. This package is a follow-up to a data package provided in June 2009. The IMM currently represents 83 medical conditions and associated ISS resources required to mitigate medical events. IMM end state forecasts relevant to the ISS PRA model include evacuation (EVAC) and loss of crew life (LOCL). The current version of the IMM provides the basis for the operational version of IMM expected in the January 2011 timeframe. The objectives of this data package are: 1. To provide a preliminary understanding of medical risk data used to update the ISS PRA Model. The IMM has had limited validation and an initial characterization of maturity has been completed using NASA STD 7009 Standard for Models and Simulation. The IMM has been internally validated by IMM personnel but has not been validated by an independent body external to the IMM Project. 2. To support a continued dialogue between the ISS PRA and IMM teams. To ensure accurate data interpretation, and that IMM output format and content meets the needs of the ISS Risk Management Office and ISS PRA Model, periodic discussions are anticipated between the risk teams. 3. To help assess the differences between the current ISS PRA and IMM medical risk forecasts of EVAC and LOCL. Follow-on activities are anticipated based on the differences between the current ISS PRA medical risk data and the latest medical risk data produced by IMM.

Butler, Doug↗

NASA NTRS2017

Dynamic Positioning System (DPS) Risk Analysis Using Probabilistic Risk Assessment (PRA)

The National Aeronautics and Space Administration (NASA) Safety & Mission Assurance (S&MA) directorate at the Johnson Space Center (JSC) has applied its knowledge and experience with Probabilistic Risk Assessment (PRA) to projects in industries ranging from spacecraft to nuclear power plants. PRA is a comprehensive and structured process for analyzing risk in complex engineered systems and/or processes. The PRA process enables the user to identify potential risk contributors such as, hardware and software failure, human error, and external events. Recent developments in the oil and gas industry have presented opportunities for NASA to lend their PRA expertise to both ongoing and developmental projects within the industry. This paper provides an overview of the PRA process and demonstrates how this process was applied in estimating the probability that a Mobile Offshore Drilling Unit (MODU) operating in the Gulf of Mexico and equipped with a generically configured Dynamic Positioning System (DPS) loses location and needs to initiate an emergency disconnect. The PRA described in this paper is intended to be generic such that the vessel meets the general requirements of an International Maritime Organization (IMO) Maritime Safety Committee (MSC)/Circ. 645 Class 3 dynamically positioned vessel. The results of this analysis are not intended to be applied to any specific drilling vessel, although provisions were made to allow the analysis to be configured to a specific vessel if required.

Thigpen, Eric B.↗

NASA NTRS2017

Dynamic Positioning System (DPS) Risk Analysis Using Probabilistic Risk Assessment (PRA)

Thigpen, Eric B.↗

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↗