Search NASA⌕ Search

SEARCH · Search NASA

Results for “quantitative risk assessment”

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.

At least 19 records

Updated Filter Leak Frequencies for Use in Risk Assessments

Quantitative risk assessment (QRA) is highly dependent on data, leading to more robust models as new and updated data is acquired. The Hydrogen Plus Other Alternative Fuels Risk Assessment (HyRAM+) QRA capabilities include calculations of individual risk from leaks in a gaseous hydrogen facility due to the potential effects of jet fires and explosions. Leak frequencies are acquired through statistical analysis of published data from a variety of sources and industries. The filter leak frequencies in previous versions of the HyRAM+ software are substantially greater than the leak frequencies of other components, leading to QRA results for gaseous hydrogen in which filters consistently dominate the overall risk. Data that were previously used to derive the filter leak frequencies were reevaluated for applicability and additional data points were added to update the filter leak frequencies. The new frequencies are more comparable to leak frequencies for other components.

08 HYDROGEN↗

Quantitative risk assessment system (QRAS)

A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS.

Weinstock, Robert M↗

Application of quantitative risk assessment to address stakeholder questions in geologic carbon storage

Ambitious international greenhouse gas emissions reduction targets demand a rapid transformation to a low-carbon economy. This transformation includes the accelerated adoption of carbon dioxide (CO2) capture and storage (CCS) technology. However, as with any large-scale engineering enterprise, the widespread commercial-scale deployment of geologic carbon storage (GCS) raises important questions about technology and cost-effectiveness, safety, environmental risk, and long-term liability. Effectively assessing and managing risks and liability associated with GCS projects is a key technical need throughout the project life cycle-from site selection and permitting to monitoring design, operational risk management, and post-operational site closure. This presentation highlights recent advancements in tools for quantitative risk assessment, being developed by the National Risk Assessment Partnership (NRAP). NRAP is a multi-year, multinational laboratory research collaboration sponsored by the U.S. Department of Energy's Office of Fossil Energy and Carbon Management. Our focus will be on these tools' applications in addressing critical stakeholder questions related to supporting permitting to ensure secure and environmentally protective storage; designing effective and efficient monitoring plans; evaluating the effectiveness of remedial actions and risk management alternatives; and informing liability assessment and investment decisions. This paper will detail the key functionality of NRAP’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), a computational framework for assessing leakage risk and containment assurance. This model features streamlined workflows for calculating leakage risk profiles, delineating risk-based area of review, and assessing contingency plans and post-injection site care requirements. ORION is an open-source, observation-based ensemble forecasting toolkit to help operators assess the seismic hazard at a carbon storage site. The State of Stress Analysis Tool (SOSAT), designed to assess subsurface stress conditions and evaluate geomechanical risk resulting from CO2 injection in an area of interest will also be presented. We will also introduce a prototype model to evaluate storage project costs and liability associated with risk management. The Technoeconomic and Liability Evaluation for Storage (TALES) model uses results from forecasts of leakage and induced seismicity risk to estimate the lifecycle cost of managing risk. Finally, a preliminary example of how the NRAP Risk-based Adaptive Monitoring Plan (RAMP) tool can be used to design efficient and effective site monitoring plans and estimate the detectability of fluid leakage will be provided. The relevance of these tools for addressing key stakeholder questions amidst uncertainty will be emphasized.

decision support↗

An Approach to Quantitative Risk Assessment for Combined Spaceflight Hazards: Evaluating the Impact of Short Sleep Durations on Space Crew Cardiovascular Health

Astronauts embarking on long-duration missions will be exposed to multiple spaceflight hazards including radiation, isolation and confinement, distance from Earth, hostile closed environments, and altered gravity. These hazards pose health risks to the crew in-mission and postflight, including risks to cardiovascular health. For radiation, quantitative risk models have been developed that are based on large-scale epidemiological evidence from exposed terrestrial populations, which are extrapolated to account for the difference in radiological effectiveness between ground-based and in-flight exposures. •Cardiovascular diseases (CVD) are multifactorial, therefore multiple risk factors can influence disease risk estimates. •Astronauts with spaceflight experience is a very small population. •To overcome limitations of cohort, population data from presumed equivalent stressors on Earth can be used to quantitatively assess possible risks. •Sleep disruption and short sleep duration are known consequences of spaceflight and are also established risk factors for cardiovascular disease on earth (Pateletal.,2020). •Coronary Heart Disease (CHD), Myocardial Infarction (MI), and stroke are negative health effects due to short sleep durations and sleep disruptions (Yinetal.,2017); (Cappuccio et al., 2010). •A combined CVD risk model including spaceflight stressor such as sleep, stress, radiation, etc.) will provide more precise estimate of risks.

Spaceflight Hazards↗

Space Shuttle Main Engine Quantitative Risk Assessment: Illustrating Modeling of a Complex System with a New QRA Software Package

During 1997, a team from Hernandez Engineering, MSFC, Rocketdyne, Thiokol, Pratt & Whitney, and USBI completed the first phase of a two year Quantitative Risk Assessment (QRA) of the Space Shuttle. The models for the Shuttle systems were entered and analyzed by a new QRA software package. This system, termed the Quantitative Risk Assessment System(QRAS), was designed by NASA and programmed by the University of Maryland. The software is a groundbreaking PC-based risk assessment package that allows the user to model complex systems in a hierarchical fashion. Features of the software include the ability to easily select quantifications of failure modes, draw Event Sequence Diagrams(ESDs) interactively, perform uncertainty and sensitivity analysis, and document the modeling. This paper illustrates both the approach used in modeling and the particular features of the software package. The software is general and can be used in a QRA of any complex engineered system. The author is the project lead for the modeling of the Space Shuttle Main Engines (SSMEs), and this paper focuses on the modeling completed for the SSMEs during 1997. In particular, the groundrules for the study, the databases used, the way in which ESDs were used to model catastrophic failure of the SSMES, the methods used to quantify the failure rates, and how QRAS was used in the modeling effort are discussed. Groundrules were necessary to limit the scope of such a complex study, especially with regard to a liquid rocket engine such as the SSME, which can be shut down after ignition either on the pad or in flight. The SSME was divided into its constituent components and subsystems. These were ranked on the basis of the possibility of being upgraded and risk of catastrophic failure. Once this was done the Shuttle program Hazard Analysis and Failure Modes and Effects Analysis (FMEA) were used to create a list of potential failure modes to be modeled. The groundrules and other criteria were used to screen out the many failure modes that did not contribute significantly to the catastrophic risk. The Hazard Analysis and FMEA for the SSME were also used to build ESDs that show the chain of events leading from the failure mode occurence to one of the following end states: catastrophic failure, engine shutdown, or siccessful operation( successful with respect to the failure mode under consideration).

Smart, Christian↗

Quantitative Risk Assessment for Fuel Cell Electric Bus Hydrogen Storage and Refueling Facility

It is necessary to understand the safety implications and risk mitigation options for fuel cell electric bus fleet deployment, especially for related facilities responsible for operations such as production, storage, compression, and dispensing of hydrogen for use by the buses. In this report, we present a quantitative risk assessment for a potential fuel cell electric bus fleet that was motivated by efforts to improve resilience at the Portland International Airport but can be applicable to a range of hydrogen case studies and use cases. We estimated risk for a facility that produces, stores, compresses, and dispenses hydrogen for the fleet of buses, with a focus on individual risk to people in terms of annual frequency of fatality. We considered the frequency of hydrogen leaks that could result in harmful physical outcomes like jet fires or explosions, and the consequences of those outcomes for people. We created customized fault trees to calculate the frequencies of different sizes of leaks and event sequence diagrams to calculate ignition probabilities for the various leak sizes. We also leveraged the HyRAM+ toolkit to use these inputs to calculate overall risk for the facility, which we separated into one section responsible for producing, storing, and compressing hydrogen, and one section responsible for dispensing the hydrogen to the buses. We found that the dispensing area seemed to have a higher risk than the production/storage/compression area of the facility, largely because of the inclusion of a component with a high leak frequency (the heat exchanger used to cool the hydrogen before entering the vehicle, to prevent overheating and expansion of hydrogen in the onboard tank). For the example production and refueling facility we evaluated and the data we used for the analysis, the leak frequency had a larger impact on the risk differences between the two sections on the facility, compared to the physical outcome consequence, which was slightly different due to the varying fuel conditions, but not substantially different. Actions can be taken to prevent these hazards (e.g., lowering leak frequencies in system components) or to mitigate the consequences if they do occur (e.g., installing barriers to protect people if ignition events occur). The choice of which actions to take depends not only on safety considerations but also on space, time, staffing, feasibility, and financial constraints. Therefore, the quantitative risk assessment approach can help understand relative risk contributions from different components, leak sizes, consequences, and human actions, to prioritize risk reduction strategies and balance these parameters. The outcomes of this report may be useful for a variety of stakeholders working in the hydrogen, transportation, vehicle, and aviation sector, including those responsible for aspects like facility design, operations, and regulations. There is not a single value of risk that determines whether a hypothetical system is “safe” or not. The insights about risk mitigations may be leveraged, and the quantitative risk assessment approach can be applied to other case studies to understand risk priorities and contributions specific to different FCEB and hydrogen facility uses.

08 HYDROGEN↗

An Overview of Quantitative Risk Assessment of Space Shuttle Propulsion Elements

Since the Space Shuttle Challenger accident in 1986, NASA has been working to incorporate quantitative risk assessment (QRA) in decisions concerning the Space Shuttle and other NASA projects. One current major NASA QRA study is the creation of a risk model for the overall Space Shuttle system. The model is intended to provide a tool to estimate Space Shuttle risk and to perform sensitivity analyses/trade studies, including the evaluation of upgrades. Marshall Space Flight Center (MSFC) is a part of the NASA team conducting the QRA study; MSFC responsibility involves modeling the propulsion elements of the Space Shuttle, namely: the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). This paper discusses the approach that MSFC has used to model its Space Shuttle elements, including insights obtained from this experience in modeling large scale, highly complex systems with a varying availability of success/failure data. Insights, which are applicable to any QRA study, pertain to organizing the modeling effort, obtaining customer buy-in, preparing documentation, and using varied modeling methods and data sources. Also provided is an overall evaluation of the study results, including the strengths and the limitations of the MSFC QRA approach and of qRA technology in general.

Safie, Fayssal M.↗

Quantitative Risk Assessment (Topical Report)

Carbon capture, utilization, and storage (CCUS) in geological formations plays a key role in mitigating anthropogenic CO 2 emissions and achieving the aggressive goal of net-zero greenhouse gas emissions. Quantitative risk assessment is crucial for ensuring the safety and reliability of geologic carbon storage (GCS) by evaluating CO 2 migration in subsurface, forecasting potential leakage and induced seismicity risks, and optimizing operational and monitoring plans. We present the use, progress, and research trends of risk assessment from the Southwest Regional Partnership on Carbon Sequestration (SWP) as an example of large-scale CCUS projects in North America. The information provided in this report can help readers understand the significance of risk and uncertainty assessment and apply them effectively in large-scale CCUS projects.

42 ENGINEERING↗

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗

Quantitative risk assessment examples for underground hydrogen storage facilities

Hydrogen energy storage can be used to achieve goals of national energy security, renewable energy integration, and grid resilience. Adapting underground natural gas storage facility (UNGSF) infrastructure for underground hydrogen storage (UHS) is one method of storing large quantities of hydrogen that has already largely been proven to work for natural gas. There are currently some underground salt caverns in the United States that are being used for hydrogen storage by commercial entities, but it is still a fairly new concept in that it has not been widely deployed nor has it been done with other geologic formations like depleted hydrocarbon reservoirs. Assessments of UHS systems can help identify and evaluate risks to people both working within the facility and residing nearby. This report provides example risk assessment methodologies and analyses for generic wellhead and processing facility configurations, specifically in the context of the risks of unintentional hydrogen releases into the air. Assessment of the hydrogen containment in the subsurface is also critically important for a safety assessment for a UHS facility, but those geomechanical assessments are not included in this report.

08 HYDROGEN↗

Uncertainty Quantification and Sensitivity Analysis for Quantitative Risk Assessments of Hydrogen Infrastructure

Typical QRAs provide deterministic estimates and understanding of risks posed but are constructed using significant assumptions and uncertainties due to limited data availability and historical momentum of using nominal estimates. This report presents a hydrogen QRA analysis using HyRAM+ that incorporates uncertainty with Latin hypercube sampling and sensitivity analysis using linear regression.

08 HYDROGEN↗

Quantitative Risk Assessment for Hydrogen-Powered Locomotive Refueling

Hydrogen powered locomotives are being explored to reduce emissions in rail applications. The risks of operations like refueling should be understood to ensure safe environments for workers and members of the public. Sensitivity analyses were conducted using HyRAM+ to identify major drivers of risk and compare effects of system parameters on individual risk. The consequences of jet fires from full-bore leaks dominated the risk, compared to explosions or smaller leaks. Pipe size, leak detection capability, and leak frequencies of system components greatly affected risk while overpressure modeling parameters and ambient conditions had little effect. The effects of personal protective equipment (PPE) materials on individual risk were quantified by reducing the individual’s exposure time or absorbed thermal dose. PPE only showed a risk reduction in low-risk cases. This study highlighted target areas for risk mitigation, including leak detection equipment and component maintenance, and indicated that the minimal effects of other parameters on risk may not justify prescriptive requirements for refueling operators.

08 HYDROGEN↗

Machine Learning Enabled Quantitative Risk Assessment of Aerial Wildfire Response

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

machine learning↗

Machine Learning Enabled Quantitative Risk Assessment of Aerial Wildfire Response

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

machine learning↗