SEARCH · Search NASA
Results for “Probabilistic risk assessment (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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
How Can You Support RIDM/CRM/RM Through the Use of PRA
Probabilistic Risk Assessment (PRA) is one of key Risk Informed Decision Making (RIDM) tools. It is a scenario-based methodology aimed at identifying and assessing Safety and Technical Performance risks in complex technological systems.
Risk Management at NASA and Its Applicability to the Oil and Gas Industry
NASA has a world-class capability for quantitatively assessing the risk of highly-complex, isolated engineering structures operated in extremely hostile environments. In particular, the International Space Station (ISS) represents a reasonable risk analog for High Pressure, High Temperature drilling and production operations on deepwater rigs. Through a long-term U.S. Government Interagency Agreement, BSEE has partnered with NASA to modify NASA's Probabilistic Risk Assessment (PRA) capabilities for application to deepwater drilling and production operations. The immediate focus of the activity will be to modify NASA PRA Procedure Guides and Methodology Documents to make them applicable to the Oil &Gas Industry. The next step will be for NASA to produce a PRA for a critical drilling system component, such as a Blowout Preventer (BOP). Subsequent activities will be for NASA and industry partners to jointly develop increasingly complex PRA's that analyze other critical drilling and production system components, including both hardware and human reliability. In the presentation, NASA will provide the objectives, schedule, and current status of its PRA activities for BSEE. Additionally, NASA has a Space Act Agreement with Anadarko Petroleum Corporation to develop a PRA for a generic 20K BOP. NASA will summarize some of the preliminary insights gained to date from that 20K BOP PRA as an example of the distinction between quantitative versus qualitative risk assessment.
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.
In-Depth Modeling and Simulation Analysis of Artemis Missions Using the Impact Probabilistic Risk Assessment Tool
BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. During an extended simulation modeling phase, IMPACT generated analytical results for medical risks, and the medical resources and capabilities to address those risks, for every segment of every crewed Artemis mission. This presentation will highlight the reliability, consistency and validity of IMPACT’s computational modeling techniques and will showcase the library of analytical outcomes generated for the Artemis missions. OVERVIEW During the early stages of IMPACT’s design, architecture and technical requirements collection, “scenarios” (use cases) - achievement goals required for acceptance testing, were identified by stakeholders. IMPACT successfully completed the scenario testing requirements and undertook an extensive operational run phase utilizing a wide range of input combinations with a goal of delivering a cohesive, trustworthy, reliable, vast, and diverse body of evidence. The intent of these modeling runs was to validate consistency in output, ensure solidity of executable operations and to streamline processes by identifying areas requiring efficiency improvements. Using the many missions of Artemis, IMPACT ran variations of operational runs to assess the output for acceptable, as well as unusual characteristics. This rigorous long-term “shakedown” analysis was implemented to help build a collective body of evidence to aid in securing a high level of confidence, reliability, and validity in the output, whether from the applicational components of IMPACT, or the entirety of the operational process. ANTICIPATED ANALYSIS AND CONCLUSION This presentation will discuss the various categories of input criteria; the comparisons in the application of these input criteria to various Artemis missions; the preparation and collection of the body of evidence, and reliability of the computational modeling techniques. This paper serves as an initial analytical overview of IMPACT’s probabilistic risk assessment (PRA) medical risk outputs covering Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.
Comparison of Artemis 2 and Artemis 5 Model Outcomes Using the Impact Probabilistic Risk Assessment Tool
BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. This presentation will seek to compare IMPACT’s computational results upon potential complex space medical conditions (e.g., sprain/strain back or sleep disturbance) using IMPACT’s risk metrics and the associated optimized medical sets/kits between two Artemis missions: single segment Artemis 2 and multi-segmented Artemis 5. OVERVIEW By identifying potential medical conditions in space using input criteria such as crew quantity and composition, certain crew physical characteristics, mission duration and mission activities, IMPACT can produce analyses on the type of medical resources and capabilities needed to produce an optimized medical set/kit to address those medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. IMPACT’s risk metrics include loss of crew life (LOCL) – a measure of crew mortality due to medical conditions in space, return to definitive care (RTDC) – the need to perform crew evacuation, and task time lost (TTL) – a measure of the inability to perform activities due to crew disability. These risk metrics are applied to every medical condition identified by IMPACT’s computation analyses for every segment of the mission. Medical sets/kits are optimized to address these medical conditions but must fit within the stated Artemis Design Reference Mission (DRM) request for mass and volume physical size constraints. ANTICIPATED ANALYSIS AND CONCLUSION Using two Artemis missions, Artemis 2 and Artemis 5, IMPACT will provide the analyses for comparison of medical set/kit contents based upon mass and/or volume requirements and identify the at-risk medical conditions within both missions. This paper serves as an initial exploration of probabilistic risk assessment (PRA) medical risk calculations between two crewed Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.
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.
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.
An Overview of PRA with Applications to Aerospace Systems
Probabilistic Risk Assessment (PRA) is a systematic process for evaluating the probabilities and consequences of undesirable events that can occur in a process or system along with providing a measure of the uncertainty associated with these probability estimates. In the past it was looked at with suspicion by many at NASA, perhaps because of bad experiences with unsuccessful quantitative methods during the Apollo era, but since the Challenger accident NASA has mandated that it be used, and it has been very successful. With NASA's new "faster, better, cheaper" philosophy, it is vital that a tool be in place that can help to achieve these goals in the reliability area. This paper describes the history of PRA, gives examples of its aerospace applications to date, and gives suggestions for how it can be used in the future, both for space shuttle upgrades and for totally new technologies such as the Second Generation Reusable Launch Vehicle.
A Strategy to Integrate Probabilistic Risk Assessment into Design and Development Processes for Aerospace Based pon Mars Exploration Rover Experiences
This paper will discuss the Probabilistic Risk Assessment (PRA) effort and its involvement with related activities during the development of the Mars Exploration Rover (MER). The Rovers were launched 2003.June.10 (Spirit) and 2003.July.7 (Opportunity), and both have proven very successful. Although designed for a 90-day mission, the Rovers have been operating for over two earth years. This paper will review aspects of how the MER project integrated PRA into the design and development process. A companion paper (Development of the Mars Exploration Rover PRA) will describe the MER PRA and design changes from those results.
Expectation vs. Reality: The Sunrise Problem Applied to Probabilistic Risk Assessment
The Sunrise Problem is succinctly stated as a question: “what is the probability that the sun will rise tomorrow?” Attempts to answer this seemingly simple question reveal unexpected incongruities between statistical/probabilistic estimates and “real-world” expectations. Rather than being a purely philosophical problem, similar incongruities can be found in reliability and probabilistic risk assessment (PRA) analyses. These differences between statistical predictions and demonstrated reliability can result in analysis shortcomings that should be addressed and resolved. This presentation will begin by discussing the nature of the Sunrise Problem and how it applies to PRA and reliability. Examples of the Sunrise Problem in a PRA estimate will also be included, along with tips to identify and resolve the shortcomings that result from these anomalous situations.
Proof-of-Concept for a Long-Term Health Metric to Quantify End of Mission Health Status in Astronauts
NASA has long used Probabilistic Risk Assessment (PRA) when high-stakes decisions need to be made about complex systems. For spaceflight medical risk, the Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a significant step towards robustly quantifying the risk to crew health during exploration missions. However, there remains a significant gap in the ability to comprehensively characterize and assess risk across the disparate functionalities and capabilities which comprise the entire Crew Health and Performance (CHP) system. To fill this gap, the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project aims to perform risk characterization for the CHP system by assessing performance risk in addition to medical risk. This effort also includes quantifying Long-Term Health (LTH) risk in addition to in-mission risk outcomes within the CHP-PRA results. LTH risk encompasses the timeframe from immediately post-flight, through the rest of an astronaut’s career, through retirement, and until death. A proof-of-concept LTH risk metric is based on medical condition end-state, as defined by the Evidence Library, capturing the spaceflight specific medical impacts persisting into post-flight[1]. Condition outcomes in the Evidence Library progress through three Clinical Phases (CP): the diagnostic phase (CP1), the treatment/convalescent phase (CP2), and the end-state phase (CP3) which represents the detrimental effects of the condition after the crew member has recovered to the maximal extent. Each CP has an associated Task Impairment (TI), defined as the degree of crew incapacity due to experiencing the condition, and is quantified with a 0-1 range. Conditions with an associated CP3 (e.g. Sepsis, Traumatic Hypovolemic Shock, Sudden Cardiac Arrest, etc.) typically have serious consequences that can cause an astronaut to be fully or partially debilitated throughout the remainder of the mission. Consequently, the Cumulative CP3 TI End-of-Mission Health Status Metric is developed by CHP-PRA to quantify the cumulative effects of all conditions which progressed to the CP3 state throughout the entirety of the mission. Hence, this End-of-Mission Health Status Metric attempts to serve as an indicator of an astronaut’s health state at the time of landing. The severity of the lingering effects of in-mission medical events are dependent on mission activities and the level of available in-mission medical care. This allows the associated cumulative TI metric to be used in comparison with the crew’s end of mission health status for different levels of in-mission resources. This presentation provides the strategy for using CP3 as an LTH metric component, as well as a proof-of-concept demonstration of LTH risk characterization using this component.
Impact, a Tool Suite for Crew Health and Performance System Trade Analyses and Decision Support - Status of Development
Mission planners, systems engineers, and clinicians that support crew health and performance face very difficult choices on upcoming exploration missions. Given that there will be a heavily constrained mass and volume allocation for a medical system on these missions, what medical capability should be manifested to minimize both medical risk and mission risk? Given that not all promising research and technology proposals can be funded, how can proposals be prioritized so that those funded research investments produce the maximum benefit in reducing overall medical risk? The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project seeks to answer these kinds of questions and others to support upcoming exploration missions. IMPACT enables risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This presentation will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include an updated medical database (consisting of an Evidence Library for medical conditions and a medical item database (MedID) for medical resources), dynamic Probabilistic Risk Assessment (PRA) capabilities, System Modeling Language (SysML) models, and contextual data visualizations of output data. IMPACT is the result of a multi-center collaborative effort. The trade space analyses performed by IMPACT can directly inform mission, vehicle, and habitat development by quantifying medical risk, given a design reference mission, crew attributes and a set of medical capabilities. This presentation will update the audience on the development status of the tool suite, its constituent parts and the plans for when it will deploy as an operational product, currently scheduled for the end of FY22. Recent development successes on the IMPACT project include the ability to cluster medical resources into medical capabilities and mutually-dependent bundles, the ability to perform trade analyses on different medical sets, different DRMs, and different crew complements, and the ability to specify mission segments as periods of time assigned to one or more members of the crew during which unique mission events occur (e.g., extravehicular activities, surface operations, gravity well adaptations, etc.). IMPACT is gearing up for its verification and validation phase to compile the data package necessary for a successful Transition to Operations (TtO), per the guidance in NPR 8900.1B .
Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment
Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.
Probabilistic Risk Analysis (PRA) of a Mobile Offshore Drilling Unit (MODU) Dynamic Positioning System (DPS)
Probabilistic Risk Assessment (PRA) has been utilized by NASA in a variety of space oriented projects. It has served as one of the primary risk identification and ranking tools. 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. As a result, NASA has entered into an agreement with Anadarko Petroleum Company (APC) to collaboratively develop PRAs for different aspects of the subsea drilling and completion process of well development. This paper documents how PRA was applied to estimate the probability that a Mobile Offshore Drilling Unit (MODU) equipped with a generically configured Dynamic Positioning System (DPS) loses location and needs to initiate an emergency disconnect. Since this project was in essence a pilot project, 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.
Predictive Modeling to Assess and Address Challenges and Limitations Associated with Clinical Care and Decision Support in Deep Space
Probabilistic risk assessment (PRA) is a method for assessing and integrating the risk of failure in a multivariate system. While it is often applied by engineers designing complex machines, it could also be applied to humans to assess the probability of a health “failure” treating diseases as the multiple “variables” and the human as the “complex machine.” The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was developed to apply PRA to assess medical risk for exploration spaceflight and inform the design of medical systems for space flight. The NASA engineering community utilizes event-driven and fault tree probabilistic techniques to classify risk in the space flight environment by leveraging the inherent knowledge of complex space flight system design and testing to quantify risk. However, in harmonizing the risk of human space flight, answering the question of “How do we balance astronaut health, performance and resource risks with other engineering risks on exploration space missions?” remains a profoundly challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the Human Research Program’s efforts to represent space fight human health and performance risks quantitatively.
Use of Preliminary PRA to Inform Decisions During Initial NASA Gateway Development
How do you use Probabilistic Risk Assessment (PRA) to support a program in development before designs are even known? Traditional PRAs require detailed design information and early in a program life cycle such information is not available. National Aeronautics and Space Administration (NASA) Safety and Mission Assurance (SMA) developed a preliminary PRA for the Gateway Program based upon NASA reference designs utilizing data and models from pervious PRA studies as surrogates for the Gateway systems. Gateway will be a lunar outpost that supports missions to the moon and includes several elements/modules developed by NASA and International Partners. NASA SMA began supporting Gateway in fall 2017 during initial formulation and continues to support the Gateway Program today. This paper will explore how the NASA SMA developed preliminary PRA was used to inform Gateway Program decisions with specific examples provided.
Use of Preliminary PRA to Inform Decisions During Initial NASA Gateway Development
How do you use Probabilistic Risk Assessment (PRA) to support a program in development before designs are even known? Traditional PRAs require detailed design information and early in a program life cycle such information is not available. National Aeronautics and Space Administration (NASA) Safety and Mission Assurance (SMA) developed a preliminary PRA for the Gateway Program based upon NASA reference designs utilizing data and models from previous PRA studies as surrogates for the Gateway systems. Gateway will be a lunar outpost that supports missions to the moon and includes several elements/modules developed by NASA and International Partners. NASA SMA began supporting Gateway in fall 2017 during initial formulation and continues to support the Gateway Program today. This paper will explore how the NASA SMA developed preliminary PRA was used to inform Gateway Program decisions with specific examples provided.