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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Characterizing and communicating uncertainty: lessons from NASA’s Carbon Monitoring System

Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9%, R 2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<10 3 km 2 ) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. Furthermore, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of PBR Spent Fuel Criticality and Dose Rate Compliance for Storage and Transportation

Spent tri-structural isotropic (TRISO)–based fuels have a strong track record in storage and transportation without documented incidents. This work seeks to reduce uncertainty to aid in more informed spent fuel management of TRISO-based fuels by modeling both fresh and spent pebble bed reactor (PBR) fuel and comparing the results to the regulatory standards from 10 CFR 71. SCALE was used for all modeling due to it having fast and accurate methods for handling PBR fuel modeling, as well as having an efficient method for shielding calculations in monaco with automated variance reduction using importance calculations (MAVRIC), which utilizes the consistent adjoint-driven importance sampling (CADIS) and the forward-weighted consistent adjoint-driven importance sampling (FW-CADIS) methods. KENO-VI was used for all criticality calculations, TSUNAMI was used for uncertainty quantification on k-effective, TRITON and the Oak Ridge isotope generation code (ORIGEN) were both used for depletion of the fuel, and MAVRIC was used for shielding calculations. For criticality assessments, this study focused on the requirement that the value of the neutron multiplication factor, k-effective (k-eff), would not exceed a peak value of 0.95, including uncertainty, with 95% confidence. Criticality was initially examined by modeling fresh fuel from three different designs—HTR-10 fuel, PBMR-400 fuel, and demonstration fuel representative of a TRISO-fueled modern high-temperature gas reactor (HTGR) design, henceforth referred to as Demo HTGR—and placing them into various sized containers with conditions described in 10 CFR 71 to quantify the peak k-eff state. When the peak value of 0.95 k-eff was exceeded, mitigation methods were examined in those scenarios. Burnup credit, pebble displacement in areas of strong neutron multiplication, and random pebble replacement using pebbles of various compositions and replacement fractions were examined. In summary, the criticality of PBR fuels can be well accounted for by restricting container size, taking credit for burnup, or by displacing/replacing pebbles. Uncertainty of the k-eff due to nuclear data uncertainties was recorded at ~0.6644%Δk/k, or roughly 664% mil (pcm). The nuclear data–induced uncertainty was relatively small and should not require significant modification in the design to be accounted for. Revisions to the evaluated nuclear data file values have been shown to have a larger impact than nuclear data–induced uncertainty. For dose rate aspects, U.S. Nuclear Regulatory Commission regulations require a maximum dose rate of 10 millirem per hour (mrem/h) at 2 meters. In examining the dose rate behavior of spent PBR fuel, the representative Demo HTGR fuel was modeled exclusively due to it possessing the highest target burnup of the examined fuels. Equilibrium cycle modeling methods were used to produce a higher-fidelity discharge isotopic composition than simple assumptions, such as reflected pebbles. The discharge composition was used as a source term in the fixed-source transport shielding calculations, and dose rates were calculated at 2 m for the shortest possible cooling time. The low concentration of fuel material led to dose rates that were in line with regulatory limits, despite the high burnup when compared to traditional light water reactor fuels. In conclusion, the methods employed in this study would require more work to further verify and validate and are limited to the criticality and dose rate analyses performed.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Nondestructive Assay Technical Infrastructure Program Mission and Vision

The Nondestructive Assay Program (NDAP) mission and vision is achieved by identifying and accomplishing a set of programmatic goals that correspond with eight broad technical program elements. The NDAP was created as a result of Defense Nuclear Facility Safety Board Recommendation 2007-1, entitled “Safety-Related In Situ Nondestructive Assay of Radioactive Materials,” which emphasized the need to improve in situ measurements of radioactive material at US Department of Energy (DOE) defense nuclear facilities. An NDAP five-year plan will define tasks to accomplish specific goals identified in the NDAP mission and vision. This mission and vision is applicable for eight technical program elements: Hardware/Software Development, Algorithm Development and Nuclear Data, Uncertainty Quantification, Nuclear Materials, Staffing, Personnel, And Training, Data Management, Requirement and Standards, and Information Preservation and Dissemination. An additional program element is technical support, which provides daily execution management support for the NDAP, site scope, and deliverable tracking via site task managers, along with technical advisement and support from the technical support group. The NDAP mission and vision provides attributes with specific goals for each program element, and some goals benefit multiple program elements. The goals and attributes defined herein are implemented via an NDAP five-year execution plan that defines site work scope, budget, and deliverables, all of which are updated annually. The NDAP is designed to benefit nondestructive assay (NDA) needs to support DOE nuclear criticality safety programs, ensuring that NDA technology is sufficiently capable of guaranteeing the safety of those who handle, store, process, or transport fissionable materials in the complex. Especially important for the NDAP is to maximize capabilities to identify, characterize, and manage in situ fissile material deposits in process equipment to ensure nuclear criticality safety at processing facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The impact of autonomous vehicles on commute ridesharing with uncertain work end time

The uncertainty in work end time can prevent ridesharing between two commuters with identical home and work locations. This effect can be alleviated by autonomous vehicles (AV): when two commuters share a ride from home to work, if the realized work end times result in a long wait between the two, an additional AV can be easily repositioned from home to work, whereas with regular human-driven vehicles one has to either bear the long wait or request expensive taxi service. This observation implies that AVs have great potential for increasing commute ridesharing. To study this effect, we develop evening commute bottleneck models with uncertain work end time and establish mode choice equilibrium for the round-trip commute. We find that, with regular vehicles, because of the expected waiting cost caused by work end time uncertainty, commuters typically do not share rides even though they have the same home and work locations. With AVs, commuters always share rides for the morning commute, and they also share rides for the evening commute if their work end times turn out to be close; otherwise, they reposition an additional AV to pick one of them up. Finally, our numerical examples show that AVs can encourage commute ridesharing and significantly reduce the expected round-trip travel costs for commuters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

STAT7 v1.2 User Guide: The STAT7 Code for Statistical Propagation of Uncertainties in Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors

The STAT7 software was developed to perform steady-state, single-phase thermal hydraulics analysis of plate-fueled reactors based on statistical propagation of uncertainties. Application of the software is for non-power research and test reactors, including conversion to low-enriched uranium fuel of U.S. High-Performance Research Reactors such as Massachusetts Institute of Technology Research Reactor. Since it can be necessary to repeat analysis during fuel reloading, STAT7 accommodates flexibility in analyzing many realistic aspects of reactor fuel management. STAT7 uses a Monte Carlo approach to model uncertainty in common fuel fabrication parameters and other key reactor operating parameters required for thermal hydraulics analyses of research and test reactors. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux, or onset of flow instability. STAT7 supports water properties based on the IAPWS-IF97 functions (The International Association for the Properties of Water and Steam Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam) in addition to the fit functions. STAT7 predicts axial profiles of fuel, cladding, and coolant temperature along a lateral stripe that runs the full length of the fuel plate from the bottom to the top. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one latera stripe of a fuel element. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis can be performed by analyzing the margin to onset of nucleate boiling and onset of flow instability for each axial node of each stripe of each plate of each fuel element in the core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

STAT7 v2.0 User Guide

The STAT7 software was developed to perform steady-state, single-phase thermal hydraulics analysis of plate-fueled reactors based on statistical propagation of uncertainties. Application of the software includes non-power research and test reactor analysis, and it has been used for the conversion to low- enriched uranium fuel of U.S. High Performance Research Reactors such as the Massachusetts Institute of Technology Research Reactor. Since it can be necessary to repeat reactor safety analysis, such as during fuel reloading, STAT7 accommodates flexibility in analyzing many practical aspects of reactor fuel management. STAT7 uses a Monte Carlo approach to model uncertainty in common fuel fabrication parameters and other key reactor operating parameters required for reactor thermal hydraulics analysis. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux, or onset of flow instability. STAT7 supports water properties based on the IAPWS-IF97 functions (The International Association for the Properties of Water and Steam Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam) in addition to fitted functions. STAT7 predicts axial profiles of fuel, cladding, and coolant temperature along a lateral stripe that runs the full length of the fuel plate from the bottom to the top. STAT7 can simultaneously analyze every axial node in each lateral stripe of all fuel plates and coolant channels in every fuel element of an entire reactor core. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. In a single execution, STAT7 can be used to perform full core analysis by analyzing the margin to onset of nucleate boiling and onset of flow instability for each axial node of each stripe of each plate of each fuel element in the core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Simplified Green-Ampt Model, Imbibition-Based Estimates of Permeability, and Implications for Leak-off in Hydraulic Fracturing

Predicting water imbibition into porous materials is important in a wide variety of fields, and hydraulic fracturing of low permeability hydrocarbon reservoirs has emerged as an application that is imposing a large water footprint. Furthermore, reliable predictions of imbibition are needed to better manage water use, yet are challenging because of uncertainties in both the permeability and capillary pressure driving force. Here, this uncertainty is reduced through evaluating correlations between the permeability and the effective capillary pressure associated with the wetting front, P c,f . These correlations allow elimination of P c,f from the Green and Ampt equation and concentrate all uncertainties in fluxes on the effective permeability k . Over a wide range of k and porosities n , imbibition scales approximately with k 1/3 . Although Leverett k 1/4 scaling for predicting P c,f is shown to be inferior when tested with data spanning a wide range of n , it nevertheless predicted imbibition fairly well. From simple imbibition measurements, both the empirical and Leverett scaling approaches allow estimates of k that have root-mean-square deviations of about one order of magnitude relative to measurements that ranged over 10 orders of magnitude in k .

58 GEOSCIENCES↗
DOE OSTI2020

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Enhancing Resilience of Urban Systems Against Climate-Induced Floods Using Advanced Data-Driven and Computing Techniques: A Driver-Pressure-State-Impact-Response (DPSIR) Framework

Focal Area: This white paper proposes advanced data-driven and computing techniques to improve the predictability of extreme climate events (precipitation) and focuses on comprehending their impact dynamics to enhance resilience of urban systems. Science Challenge: In urban systems, climate change has presented significant challenges related to accurate spatiotemporal prediction of extreme climate events (heavy precipitation) and their impacts, i.e. flooding. The occurrence of extreme climate events and their compounding repercussions on the built environment make urban systems increasingly more vulnerable. The interactions between extreme climate events, urban systems, and the corresponding impacts are irregular, non-linear, and dynamic, making it difficult to standardize resilience. As it stands, the management of complex urban systems is affected by uncertainties related to the nature of short- and long-term consequences caused by extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impacts of Forest Management-Induced Productivity Changes on Future Land Use and Land Cover Change

Anthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under-investigated. This study explored the impacts of forest management-induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management-induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management-induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (-7.5%) and the smallest decrease under SSP3 (-0.7%) in 2100. Among land use types, considering forest management-induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management-induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2025

Community-Informed Urban Flood Modeling for Impact Mitigation

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.

Ava, Spangler [Pennsylvania State University]↗
DOE OSTI2020

Integrated Hydro-Terrestrial Modeling: Development of a National Capability

Water is one of our most important natural resources and is essential to our national economy and security. Multiple federal government agencies have mission elements that address national needs related to water. Each water-related agency champions a unique science and/or operational mission focused on advancing a portion of the nation’s ability to meet our water-related challenges. These diverse mission needs have engendered a rich and extensive base of water-related data and modeling capabilities. While useful for their intended purposes, these capabilities are not well integrated to address complex regional problems and overarching national problems. These major investments by a number of federal agencies, however, lay the foundation for an integrated hydro-terrestrial modeling and data infrastructure that will enhance knowledge, understanding, prediction, and management of the nation’s diverse water challenges. Creating a more seamless national hydro-terrestrial modeling and data capacity presents an enormous opportunity to advance operational as well as research capabilities leading to more effective water management. Advances are necessary not only in operational tools for forecasting but also in research to identify and resolve knowledge and data gaps that lead to unacceptable uncertainties in forecast outcomes. As such, close coordination across scientific, operational, and resource management communities is required. To this end, an interagency workshop on “Integrated Hydro-Terrestrial Modeling: Development of a National Capacity” was held at the National Science Foundation (NSF) headquarters in Alexandria, Virginia in September 2019, led jointly by the NSF, the U.S. Department of Energy (DOE) and the U.S. Geological Survey (USGS) and with broader interagency support provided through an interagency steering committee. This workshop provided a venue to bring together representatives of water-related agencies and their scientific partners (including university researchers) to initiate and refine a vision for a national Integrated Hydro-Terrestrial Modeling (IHTM) and data infrastructure and advance ideas towards its development. The workshop was designed to address three critical foci to advance the development of a national IHTM capacity: “Priority Water Challenges” around which to motivate and initiate development; Technical and methodological obstacles related to data and modeling; Organizational, structural, and cultural barriers that heretofore have impeded integration of capabilities across the federal and research landscapes. The following “Priority Water Challenge” domain areas represent targets for initiating development of the IHTM and were identified and selected in alignment with priorities of the administration’s Water Sub-Cabinet: (1) Nutrient loading, hypoxia, and harmful algal blooms; (2) Water availability in the western United States; and (3) Extreme weather-related water hazards. These water challenges span agency mission boundaries and encompass a broad range of geographies, complex system dynamics and feedbacks, and critical processes spanning hydrological, climatic, and biophysical systems as well as land-use/land-cover, agricultural, built infrastructure, societal, economic, and decisional environments. These three Priority Water Challenges cannot be fully addressed without leveraging complementary and synergistic capabilities across multiple agencies.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Threshold Helium Leakage Detection Switch with Ultra Low Power Operation

Detecting helium leakage is important in many applications, such as in dry cask nuclear waste storage systems. This work develops a helium detection system based on the relative permittivity (dielectric constant) difference between air and helium. This difference changes the status of an electrostatic microelectromechanical system (MEMS) switch. The switch is a capacitive-based device and requires a very negligible amount of power. Exciting the switch’s electrical resonance enhances the MEMS switch sensitivity to detect low helium concentration. This work simulates two different MEMS switch configurations: a cantilever-based MEMS modeled as a single-degree-freedom model and a clamped-clamped beam MEMS molded using the COMSOL Multiphysics finite-element software. While both configurations demonstrate the switch’s simple operation concept, the clamped-clamped beam was selected for detailed parametric characterization due to its comprehensive modeling approach. The beam detects at least 5% helium concentration levels when excited at 3.8 MHz, near electrical resonance. The switch performance decreases at lower excitation frequencies or increases the circuit resistance. The MEMS sensor detection level was relatively immune to beam thickness and parasitic capacitance changes. However, higher parasitic capacitance increases the switch’s susceptibility to errors, fluctuations, and uncertainties.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗