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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.

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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

A Pilot Project for Quantifying the Effect of Medical Provider Knowledge, Skills, and Abilities on Outcomes for Spaceflight Using a Probabilistic Risk Assessment Tool

In order to enable the future of long-duration deep space exploration we must confront the uncertainty in medical risk. Limitations of communication, resupply, and evacuation in deep space will require a high degree of crew autonomy and accurate risk assessment will be critical to ensure adequate crew training and medical system design. To address this, NASA’s Human Research Program Exploration Medical Capability Element has developed the Informing Mission Planning via Analysis of Complex Tradespaces Tool (IMPACT). IMPACT is a suite of tools that can provide evidence-based, data-driven trade space assessments between available medical resources in the mass- and volume-constrained environment of a deep space exploration vehicle. In the current model, medical conditions either can or cannot be treated based on the availability of medical system resources and equipment. However, medical outcomes often depend just as much on the knowledge, skills, and abilities (KSA) of the provider operating the system. This paper presents a method for modeling and quantifying the effect of medical officer KSA on medically relevant mission risk outcomes during spaceflight.

capabilities↗
DOE OSTI2013

RAVEN AS A TOOL FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT: SOFTWARE OVERVIEW

RAVEN is a software tool under development at the Idaho National Laboratory (INL) that acts as the control logic driver and post-processing tool for the newly developed Thermo-Hydraylic code RELAP- 7. The scope of this paper is to show the software structure of RAVEN and its utilization in connection with RELAP-7. A short overview of the mathematical framework behind the code is presented along with its main capabilities such as on-line controlling/monitoring and Monte-Carlo sampling. A demo of a Station Black Out PRA analysis of a simplified Pressurized Water Reactor (PWR) model is shown in order to demonstrate the Monte-Carlo and clustering capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Probabilistic methods in a study of trip setpoints

Most early vintage Boiling Water Reactors have a high head and high capacity High Pressure Coolant Injection (HPCI) pump to keep the core covered following a loss of coolant accident (LOCA). However, the protection afforded by the HPCI pump for mitigating a LOCA introduces the potential that a spurious start of the HPCI pump could oversupply the reactor vessel and lead to an automatic trip of the main turbine due to high water level. A turbine trip and associated increase in moderator density could challenge the bases of fuel integrity operating limits. To prevent turbine trip during spurious operation of the HPCI pump, the reactor protection system includes instrumentation and logic to sense high water level and automatically trip the HPCI pump prior to reaching the turbine trip setpoint. This paper describes an analysis that was performed to determine if existing reactor vessel water level trip instrumentation, logic and setpoints result in a high probability that the HPCI pump will trip prior to actuation of the turbine trip. Using nominal values for the initial water level and for the HPCI pump and turbine trip setpoints, and using the probability distribution functions for measurement uncertainty in these setpoints, a Monte Carlo simulation was employed to determine probabilities of successfully tripping the HPCI pump prior to tripping of the turbine. The results of the analysis established that the existing setpoints, instrumentation and logic would be expected to reliably prevent a trip of the main turbine. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

RAVEN as a tool for dynamic probabilistic risk assessment: Software overview

RAVEN is a software tool under development at the Idaho National Laboratory (INL) that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. The scope of this paper is to show the software structure of RAVEN and its utilization in connection with RELAP-7. A short overview of the mathematical framework behind the code is presented along with its main capabilities such as on-line controlling/ monitoring and Monte-Carlo sampling. A demo of a Station Black Out PRA analysis of a simplified Pressurized Water Reactor (PWR) model is shown in order to demonstrate the Monte-Carlo and clustering capabilities. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

Hybrid method of deterministic and probabilistic approaches for continuous energy neutron transport problem

This paper presents a new hybrid method of continuous energy Monte Carlo (MC) and multi-group Method of Characteristics (MOC). For a continuous energy neutron transport analysis, the hybrid method employs a continuous energy MC for resonance energy range to treat the resonances accurately and a multi-group MOC for high and low energy ranges for efficiency. Numerical test with a model problem confirms that the hybrid method can produce consistent results with the reference continuous energy MC-only calculation as well as multi-group MOC-only calculation. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Statistical Methods for Effective Spatial and Temporal Scaling in Support of Probabilistic Performance Assessments - 17340

Performance assessments (PAs) for the disposal of low-level radioactive waste have traditionally been performed in a deterministic manner, with inputs specified as single values as if they carry no uncertainty. Modern PAs, however, explicitly incorporate uncertainty through use of probability distributions for inputs instead of single values. While this is clearly an improvement, challenges remain in specifying appropriate distributions, many of which relate to capturing the appropriate spatial and/or temporal scale given the PA model, and matching the scale(s) of data and information to that specified by the model. More careful attention to scaling will lead to more defensible characterizations of physical variability and uncertainty in model inputs, thus leading to more defensible information regarding outcomes of interest to be used in decision making, and hence more defensible decisions. This work introduces general strategies and considerations for explicitly incorporating considerations of scale into distribution development. The strategies are explained as a series of steps and then presented with an example that starts to illustrate the new statistical concepts. This work frames the problem in terms of hierarchical statistical modeling, providing important connections to current statistical approaches to formally and rigorously account for temporal and spatial scaling. The larger goal is to build a more formal and unified approach to distribution development to help teams of modelers, subject matter experts, and statisticians work together to develop distributions and justify the choices made in the process, ultimately leading to more informed decision-making. Without this rigor, statistical distributions are not specified correctly and can have unexpected consequences for modeling and sensitivity analysis. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Probabilistic geomechanical analysis of compartmentalization at the Snøhvit CO 2 sequestration project

Abstract Pressure buildup caused by large‐scale CO 2 injection is a key concern during a carbon sequestration project. Large overpressures can compromise seal integrity, reactivate faults, and induce seismicity. Furthermore, pressure buildup is directly related with storage capacity. In this work we study the geomechanical response to CO 2 injection at Snøhvit, to understand the potential for fault reactivation, leakage, and contamination of the producing interval through bounding faults. Furthermore, we evaluate the potential contribution of a structural component to reservoir compartmentalization. We combine simplified analytical models, based on critically stressed fracture theory and a Mohr‐Coulomb failure criterion, with a rigorous sensitivity analysis. Large stress uncertainties are present and reflected in the modeling results. It was found that under the most likely stress state the faults are fairly stable and caprock hydrofracturing would be expected before fault reactivation. In most of the analyzed cases, the critical pressure perturbation needed for reactivation is above 13 MPa, which was the limiting pressure increase before reaching the fracture pressure. Faults were found to be ~ 20% less stable when considering variations in S Hmax orientation. In those cases, fault reactivation could be expected before caprock failure if injection continued. However, if the pressure increase did reach the critical values for seal failure estimated under the worst case (and least likely) stress state, no indication of such failure can be observed in the measured pressure response. Finally, the potential role of a structural component in the compartmentalization and fluid migration is difficult to assess due to the stress state uncertainty.

Chiaramonte, Laura↗
DOE OSTI2019

A probabilistic assessment of geomechanical reservoir integrity during CO 2 sequestration in flood basalt formations

Recent field experiments in Iceland and Washington State (USA) show that basalt formations may be favorable targets for carbon capture and sequestration (CCS) because CO 2 mineralization reactions proceed rapidly. These results imply that there is tremendous opportunity for implementing CCS in large igneous provinces. However, the magnitude of this opportunity comprises commensurate levels of uncertainty because basalt reservoirs are characterized by highly heterogeneous, fracture-controlled hydraulic properties. This geologic uncertainty is propagated as parametric uncertainty in quantitative risk models, thus limiting the efficacy of models to predict CCS performance attributes, such as reservoir integrity and storage potential. To overcome these limitations, here we present a stochastic approach for quantifying the geomechanical performance attributes of CCS operations in a highly heterogeneous basalt reservoir. We utilize geostatistical reservoir characterization to develop an ensemble of equally probable permeability distributions in a flood basalt reservoir with characteristics of the Wallula Basalt Pilot Project. We then simulate industrial-scale CO 2 injections within the ensemble and calculate the mean and variance of fluid pressure over a 1-year injection period. These calculations are combined with the state of stress in southeast Washington State to constrain the spatial extent at which shear failure, fracture initiation, and borehole breakdown may occur. Results from this study show that (i) permeability uncertainty alone causes injection pressure to vary over 25 MPa, (ii) shear failure is likely to occur at 7 times greater distances from the injection than the CO 2 migrates, and (iii) joint initiation pressures are localized within the volume comprising the CO 2 plume.

03 NATURAL GAS↗