Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PROBABILISTIC ESTIMATION”

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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

RePOWERD: Restoration of Power Outage from Wide-area Severe Weather Disruptions

The purpose of the RePOWERD project is to develop a probabilistic and a simulation based power restoration forecasting model for tropical storms based on geographic utility service areas, impacts to energy infrastructures and transportation networks, time-varying customer outage number, crew information (i.e., crew size, crew staging requirements), and utility restoration plans. Energy infrastructure is a critical lifeline essential for the United States’ national and economic security. Like other critical infrastructures, this infrastructure is aging and is at a high risk of damages from extreme weather events. Based on the number of reported outages in EAGLE-I during 2020 and 2021, it is evident that the current energy infrastructure is not equipped to handle extreme event impacts and will contribute to significant outages. From a resilience perspective, it is essential to forecast restoration time in the event of a severe weather induced power outage to assist the US Department of Energy, emergency managers, and first responders with resource planning. This project contributes to this crucial need. This report details the models, that were developed in this pilot study, to determine the rate of restoration and estimated time of restoration (ETR) during tropical storm events within counties and utility service areas based on damage to energy infrastructures and total number of customers experiencing outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Design and Analysis of a Wake Steering Controller with Wind Direction Variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values. For a spacing of 5 rotor diameters and a turbulence intensity of 10%, robust yaw offsets optimized for variable wind directions yielded an energy gain improvement of 128%. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
NASA NTRS2020

Natural Hazard Risk Assessments at the Global Scale

Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around USD 260–310 billion per year. The scientific and policy communities recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and we specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and we identify potential ways in which different hazard communities can learn from each other. For example, there are a number of global risk studies focusing on hydrological, climatological, and meteorological hazards that have included future projections and disaster risk reduction measures (in the case of floods), whereas fewer exist in the peer-reviewed literature for global studies related to geological hazards. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage further dialogue on knowledge sharing between disciplines and communities working on different hazards and risk and at different spatial scales.

Philip J. Ward↗
DOE OSTI2014

Reduced order models for prediction of groundwater quality impacts from CO₂ and brine leakage

A careful assessment of the risk associated with geologic CO₂ storage is critical to the deployment of large-scale storage projects. A potential risk is the deterioration of groundwater quality caused by the leakage of CO₂ and brine leakage from deep subsurface reservoirs. In probabilistic risk assessment studies, numerical modeling is the primary tool employed to assess risk. However, the application of traditional numerical models to fully evaluate the impact of CO₂ leakage on groundwater can be computationally complex, demanding large processing times and resources, and involving large uncertainties. As an alternative, reduced order models (ROMs) can be used as highly efficient surrogates for the complex process-based numerical models. In this study, we represent the complex hydrogeological and geochemical conditions in a heterogeneous aquifer and subsequent risk by developing and using two separate ROMs. The first ROM is derived from a model that accounts for the heterogeneous flow and transport conditions in the presence of complex leakage functions for CO₂ and brine. The second ROM is obtained from models that feature similar, but simplified flow and transport conditions, and allow for a more complex representation of all relevant geochemical reactions. To quantify possible impacts to groundwater aquifers, the basic risk metric is taken as the aquifer volume in which the water quality of the aquifer may be affected by an underlying CO₂ storage project. The integration of the two ROMs provides an estimate of the impacted aquifer volume taking into account uncertainties in flow, transport and chemical conditions. These two ROMs can be linked in a comprehensive system level model for quantitative risk assessment of the deep storage reservoir, wellbore leakage, and shallow aquifer impacts to assess the collective risk of CO₂ storage projects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multicriterion benefit evaluation methodology for safety enhancements in nuclear power plants and application for FLEX strategies

Here we propose a multicriterion benefit evaluation (MCBE) methodology for evaluating costs and benefits of implementing safety enhancements in nuclear power plants. The MCBE methodology establishes a comprehensive evaluation scope using multiple evaluation criteria (including public risk, occupational risk, plant revenue, and plant cost) and multiple contexts (including plant normal operations, incidents, and accidents). The MCBE methodology formulates a benefit or cost element as the product of a monetary consequence and its occurrence-likelihood change. The MCBE methodology also incorporates a decision maker’ preferences toward occurrence likelihood and monetary consequence into cost and benefit estimations through a modified usage of cumulative prospect theory. This paper applies the MCBE methodology to the implementation of diverse and flexible coping strategy (FLEX) in a generic nuclear power plant as an illustrative example. The FLEX benefits are evaluated through three types of influence paths: plant safety enhancement, risk-informed activities, and potential additional credit of FLEX in supporting plant operation and maintenance-efficiency improvement. The benefit-to-cost ratio (BCR) of FLEX implementation is calculated. Sensitivity analyses are conducted to examine the impacts of modeling assumptions and input parameter values on the BCR.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2014

Dynamical systems probabilistic risk assessment

Probabilistic Risk Assessment (PRA) is the primary tool used to risk-inform nuclear power regulatory and licensing activities. Risk-informed regulations are intended to reduce inherent conservatism in regulatory metrics (e.g., allowable operating conditions and technical specifications) which are built into the regulatory framework by quantifying both the total risk profile as well as the change in the risk profile caused by an event or action (e.g., in-service inspection procedures or power uprates). Dynamical Systems (DS) analysis has been used to understand unintended time-dependent feedbacks in both industrial and organizational settings. In dynamical systems analysis, feedback loops can be characterized and studied as a function of time to describe the changes to the reliability of plant Structures, Systems and Components (SSCs). While DS has been used in many subject areas, some even within the PRA community, it has not been applied toward creating long-time horizon, dynamic PRAs (with time scales ranging between days and decades depending upon the analysis). Understanding slowly developing dynamic effects, such as wear-out, on SSC reliabilities may be instrumental in ensuring a safely and reliably operating nuclear fleet. Improving the estimation of a plant's continuously changing risk profile will allow for more meaningful risk insights, greater stakeholder confidence in risk insights, and increased operational flexibility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A metric for evaluating conformance robustness during geologic CO 2 sequestration operations

A metric for quantifying the robustness of a designation of conformance of a geologic CO 2 sequestration (GCS) project during its operational phase is developed and demonstrated. Conformance in this context is a measure of the degree to which the sequestration system is understood and can be accurately modeled along with the degree to which the storage system is performing as designed. The robustness of conformance quantifies the degree to which parameter values can deviate from their current nominal estimates and still produce model forecasts that meet the performance criteria for the GCS operation. We develop and demonstrate the approach on a simplified scenario to illustrate the concept using a single uncertain parameter (homogeneous reservoir permeability) and a single performance criterion (critical pressure at a monitoring well in the reservoir; i.e., one that may displace brine from the reservoir to an overlying drinking water aquifer for example). Increased confidence in conformance assessment as more monitoring data are obtained is incorporated through the standard error of the coefficient (reservoir permeability in the case presented here), which we designate as the concordance metric. As more monitoring data become available during the course of the GCS operation, the standard error of the coefficient decreases (in general), thereby leading to increased conformance robustness as a larger deviation from nominal is required to fail to meet performance criteria. Increasing conformance robustness over time builds confidence that a GCS project will continue to meet performance criteria during the life-span of the project, thereby supporting designations of conformance. Furthermore a lack of conformance robustness provides a critical warning that the performance criteria of the GCS operation are not robust against probabilistic and non-probabilistic uncertainty in model conceptualization and/or model parameters.

58 GEOSCIENCES↗
DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗
DOE OSTI2018

Assessment of the Area of Review and Leakage Impact for Site 7 using the NRAP-IAM-CCS Tool, Northern Michigan Basin-CarbonSAFE Phase 1 Pre-Feasibility Study

U.S. Environmental Protection Agency’s (EPA’s) Class VI regulations require owners or operators of carbon storage projects to determine an Area of Review (AoR) representative of project risk to underground sources of drinking water (USDWs). The AoR is an estimate of the project footprint and is used to develop monitoring plans to ensure protection of USDWs. In this syudy, the NRAP-IAM-CS software tool was applied to estimate the AoR and the leakage potential of legacy wells located within the AoR to impact groundwater quality at a carbon storage screening site for the NMB-CS, Phase 1 project. The NRAP-IAM-CS is a science-based toolset developed by the U.S. Department of Energy for quantitative risk assessment of geologic sequestration of carbon dioxide (CO 2 ) (Pawar et al., 2016). The toolset adopts a stochastic approach in which predictions include site uncertainties using storage reservoir, leakage scenario, and shallow groundwater impact reduced order models (ROMs). Risk-based analysis done using the NRAP-IAM-CS yielded an AoR that was comparable to estimates defined by the critical pressure needed move fluid from the reservoir to the overlying USDW through an open wellbore. The risk-based AoR was slightly smaller than that based on the critical pressure (234 km 2 compared to 269 km 2 ), because small fluxes did not impact groundwater quality. Leakage from two legacy wells located within the AoR should not impact groundwater quality over the 30-year injection period. Legacy Well 1 penetrates the simulated CO 2 plume and would require a permeability of 5 x 10 -12 m 2 (~5 Darcy) to impact groundwater quality after about 20 years of injection. Legacy Well 2 falls outside of the CO 2 plume footprint, where reservoir pressures are too small to generate large enough leakage flux to change groundwater quality even with well permeabilities as high as 5 x 10 -11 m 2 (~50 Darcy). This work represents one of the first applications of the NRAP toolset for the screening of potential CO 2 storage sites. The toolset provides a risk-based method of evaluating the AoR and the impact of CO 2 or brine leakage through legacy wells. The following recommendations will strengthen the use of probabilistic assessments for site selection and permitting of Class VI CO 2 injection wells.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

A probabilistic application of oil and gas data for exploration stage geothermal reservoir assessment in the Appalachian Basin

Geothermal energy is a renewable, widespread, baseload energy source that has the potential to supply a large portion of the world’s energy needs. High development risk, specifically high drilling costs combined with uncertainty in subsurface flow properties, have hampered growth of geothermal energy development. One option to reduce subsurface risk is to target locations where reservoir data are already available, including sedimentary basins where there has been extensive hydrocarbon production. Sedimentary basins are widespread and often have suitable geothermal gradients. This chapter presents a low-cost methodology that can be used at the pre-drilling exploration phase of a low-temperature geothermal project to predict the location of high-productivity and low-risk reservoirs, applied to a case study of the Appalachian Basin in the eastern United States. Because only averaged reservoir data were available for analysis, the technique uses a Monte Carlo simulation of the Reservoir Flow Capacity (RFC) and Reservoir Productivity Index (RPI) for over 1800 individual reservoirs, using liquid water or supercritical carbon dioxide (sCO 2 ) as the reservoir fluid. RFC is used as an initial screening tool to identify high-priority geologic formations and reservoirs. Here our results indicate that 99% of the reservoirs in the basin are of insufficient quality for geothermal heat production with water as the assumed fluid and without using stimulation technologies associated with Enhanced Geothermal Systems (EGS). RPI is also used as an alternative metric to identify high-productivity reservoirs. When comparing the RPI results to natural gas production volumes from several selected reservoirs, the predicted RPI proves to be a good estimate of flow potential for porous reservoirs, but overestimates flow by a factor of three for fractured or vuggy reservoirs. A greater uncertainty ought to be utilized when applying the RPI equation to non-porous media, therefore we recommend utilizing the RPI metric when higher resolution data are available for analysis. This methodology can be utilized in other basins that have experienced hydrocarbon exploration and production, to highlight low-risk reservoirs or geologic formations for further geothermal development.

15 GEOTHERMAL ENERGY↗
NASA NTRS2021

Data-driven landslide nowcasting at the global scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

XGBoos↗
NASA NTRS2006

Trade Studies of Space Launch Architectures using Modular Probabilistic Risk Analysis

A top-down risk assessment in the early phases of space exploration architecture development can provide understanding and intuition of the potential risks associated with new designs and technologies. In this approach, risk analysts draw from their past experience and the heritage of similar existing systems as a source for reliability data. This top-down approach captures the complex interactions of the risk driving parts of the integrated system without requiring detailed knowledge of the parts themselves, which is often unavailable in the early design stages. Traditional probabilistic risk analysis (PRA) technologies, however, suffer several drawbacks that limit their timely application to complex technology development programs. The most restrictive of these is a dependence on static planning scenarios, expressed through fault and event trees. Fault trees incorporating comprehensive mission scenarios are routinely constructed for complex space systems, and several commercial software products are available for evaluating fault statistics. These static representations cannot capture the dynamic behavior of system failures without substantial modification of the initial tree. Consequently, the development of dynamic models using fault tree analysis has been an active area of research in recent years. This paper discusses the implementation and demonstration of dynamic, modular scenario modeling for integration of subsystem fault evaluation modules using the Space Architecture Failure Evaluation (SAFE) tool. SAFE is a C++ code that was originally developed to support NASA s Space Launch Initiative. It provides a flexible framework for system architecture definition and trade studies. SAFE supports extensible modeling of dynamic, time-dependent risk drivers of the system and functions at the level of fidelity for which design and failure data exists. The approach is scalable, allowing inclusion of additional information as detailed data becomes available. The tool performs a Monte Carlo analysis to provide statistical estimates. Example results of an architecture system reliability study are summarized for an exploration system concept using heritage data from liquid-fueled expendable Saturn V/Apollo launch vehicles.

Mathias, Donovan L.↗
DOE OSTI2017

Probabilistic Risk Assessment of CO2 Trapping Mechanisms in a Sandstone CO2-EOR Field in Northern Texas, USA

This study employs a polynomial chaos expansion (PCE) technique to develop the reduced order models (ROMs) integrated with the Monte Carlo simulations to quantify uncertainties of CO 2 storage associated with enhanced oil recovery (EOR) in a commercial-scale CO 2 -EOR field. The Morrow B sandstone at the Farnsworth Unit (FWU) EOR field in northern Texas was selected as a case study. A 3-D reservoir model, including simulation of a five-spot injection/production well pattern, was constructed based on the data from well logs and cores, and an updated seismic interpretation in the FWU. Porosity and permeability were considered as sources of uncertainty. The 1000-realization heterogeneous random fields of porosity and permeability were generated based on the variogram analysis on the core measurements of the FWU. Three model outputs of interest include the amount of CO 2 trapped by three trapping mechanisms, i.e. hydrodynamic trapping, oil dissolution trapping, and aqueous dissolution trapping. The total of 25 runs were conducted with the simulations of a 20-year CO 2 -EOR period, and a 20-year monitoring period to develop the ROMs between uncertain model input parameters (i.e., porosity and permeability in this study) and model outputs at the end of simulation for each grid cell using PCE approach. The high coefficient of determination (R 2 ) and small normalized root mean square error (NRMSE) between the simulated (using reservoir modeling) and predicted (using ROMs) CO 2 storage indicate that the ROMs are acceptable and reliable to be used for the predictions. Given the 1000 Monte Carlo samplings of the model input parameters, cumulative distribution functions (CDFs) and uncertainty bounds (10 th and 90 th percentiles) of model outputs were estimated based on ROMs. At the end of simulation, the most injected CO 2 at the FWU was stored by hydrodynamic trapping (between 121,400 and 166,860 tonnes), followed by oil dissolution trapping (between 55,303 and 76,816 tonnes), and aqueous dissolution trapping (between 1979 and 2751 tonnes).

58 GEOSCIENCES↗
DOE OSTI2021

Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real-time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high-fidelity modeling in real-time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time-critical decision-making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high-fidelity computations in real-time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate

In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest (QoI) remains a challenge. In this paper, we present a forward propagation uncertainty quantification (UQ) process to produce a probabilistic distribution for the observed regression rate r. We characterized two input data uncertainty sources from the experiment (the distortion from the camera U c and the non-zero-angle fuel placement U Y ), the prediction and model form uncertainty from the deep neural network (U m ), as well as the variability from the manually segmented images used for training it (U s ). Here, we conducted seven case studies on combinations of these uncertainty sources with the model form uncertainty. The main contribution of this paper is the investigation and inclusion of the experimental image data uncertainties involved, and how to include them in a workflow when the QoI is the result of multiple sequential processes.

42 ENGINEERING↗
NASA NTRS2008

Estimating the Reliability of Electronic Parts in High Radiation Fields

Radiation effects on materials and electronic parts constrain the lifetime of flight systems visiting Europa. Understanding mission lifetime limits is critical to the design and planning of such a mission. Therefore, the operational aspects of radiation dose are a mission success issue. To predict and manage mission lifetime in a high radiation environment, system engineers need capable tools to trade radiation design choices against system design and reliability, and science achievements. Conventional tools and approaches provided past missions with conservative designs without the ability to predict their lifetime beyond the baseline mission.This paper describes a more systematic approach to understanding spacecraft design margin, allowing better prediction of spacecraft lifetime. This is possible because of newly available electronic parts radiation effects statistics and an enhanced spacecraft system reliability methodology. This new approach can be used in conjunction with traditional approaches for mission design. This paper describes the fundamentals of the new methodology.

radiation↗