SEARCH · Engineering Papers
Results for “propagation of uncertainty”
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.
Effects of finite sampling on fatigue damage estimation of wind turbine components: A statistical study
The variability of the wind turbine loads complicates fatigue assessment in the design phase, as performing simulations covering the entire lifetime is computationally expensive. The current work provides important information for assessing the uncertainty in fatigue damage estimation due to finite data. Here we study the sample size effect on mean, variance, and skewness of damage in each wind bin, identify the important wind bins, and study the uncertainty propagation from each wind bin to the lifetime damage using 3600 aeroelastic simulations and bootstrapping. To achieve less than 1% error in the damage estimation across all load channels in the current case study, at least 100 turbulence seeds are needed. Damage in different wind bins follows a lognormal distribution when using the conventional approach of six seeds. The provided insights and information allow the designer to achieve a specific level of accuracy for a given computational cost using strategic bin sampling.
Reactivity initiated accident uncertainty quantification for fuel assembly with subchannel code
UNIST CORE lab has developed a multiphysics coupling framework (MPCORE) consisting of Neutronics, Thermal Hydraulics and Fuel Performance modules. It can accommodate one-dimensional as well as sub-channel code Thermal Hydraulics (TH) module. Generally, running a transient requires more computational power due to the convergence of modules with each other. The difference between one-dimensional and sub-channel TH modules is studied in this research for a Reactivity Initiated Accident (RIA). Both TH modules are compared for a RIA uncertainty propagation in a single VERA fuel assembly with 2.11% enrichment. MPCORE is capable of analyzing the transient at any burnup point but for current work, only fresh fuel has been considered. The results have been obtained using dynamic gap heat conductance in FRAPTRAN. Peak centerline temperature, fuel enthalpy and DNBR are compared for both the approaches. The results indicate that the use of sub-channel code lead to greater safety margin for critical parameters. Computation time comparison is also presented for both the cases. (authors)
NeuNorm
NeuNorm is a scipp-based Python library for neutron imaging normalization and time-of-flight (TOF) data processing at Oak Ridge National Laboratory imaging facilities (MARS at HFIR and VENUS at SNS). NeuNorm 2.0 is a complete, scipp-based rewrite of the original NeuNorm normalization library, adding HDF5 output, automatic uncertainty propagation, and TOF/event-mode processing.
GDSA Framework Development and Process Model Integration FY2022
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 advances of the Geologic Disposal Safety Assessment (GDSA) performance assessment (PA) development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to assess probabilistically the performance of generic disposal options and generic sites. The modeling capability under development is called GDSA Framework (pa.sandia.gov). GDSA Framework is a coordinated set of codes and databases designed for probabilistically simulating the release and transport of disposed radionuclides from a repository to the biosphere for post-closure performance assessment. Primary components of GDSA Framework include PFLOTRAN to simulate the major features, events, and processes (FEPs) over time, Dakota to propagate uncertainty and analyze sensitivities, meshing codes to define the domain, and various other software for rendering properties, processing data, and visualizing results.
Terra-Populus v0.1: A Python Library for LandScan High-Definition Population Analysis and Modeling
The terra-populus library is designed for use by the LandScan HD technical team, offering a streamlined set of tools for generating and updating LandScan HD datasets from foundational building-level data, referred to as 'molecules,' provided by the building-level attribution team. This document serves as the primary technical documentation for terra-populus. Version 0.1 of the library includes the core modeling components necessary for LandScan HD production. It enables the generation of the LandScan HD Baseline dataset as well as corresponding confidence measures for the occupancy rates used. Parameters have been included for incorporating damaged building indicators and changes in population, to faciliate the creation of rapid updates for LandScan HD. Future iterations of terra-populus will introduce tools for creating a confidence index, and quantifying and propagating uncertainty, facilitating the creation of probabilistic LandScan HD outputs. This report provides an overview of the tools available in the library and the corresponding code implementations. One of the key advancements implemented in terra-populus is a redefinition of the atomic modeling unit for LandScan HD. Traditionally, the LandScan HD vector analytical framework has generated population estimates at the building sub-component (molecule) level. However, terra-populus adopts a building-level modeling approach. This shift is an operational decision aimed at aligning LandScan HD outputs with confidence measures, which are computed and validated at the building level (confidence measures are not included in this version of terra-populus, aside from those associated with the occupancy rates). Additional advancements to the LandScan HD modeling, as implemented by terra-populus, include a minimum population value parameter and an auto assignment of building floor counts. The population minimum value was implemented to prevent buildings and subsequent LandScan HD pixels that contained small values that may not rasterize in production. An 'auto' value has been included as a method for dealing with buildings lacking floor count information, where it is the average floor count of all other buildings with a residential building use type tag. The logic behind this is to remain consistent with the current logic employed for dealing with building use type null instances, where a null use type is defaulted to residential since it is the most common building type. The auto logic is intended to apply the most common building floor count of the most common type of buildings. The tools provided in terra-populus represent a significant step forward in improving the efficiency, reproducibility, and transparency of the LandScan HD modeling process. As the library evolves, it will continue to serve as a foundational resource for high-resolution population modeling.
Markov Chain Monte Carlo Parameter Estimation of Deflagration Losses in a Rotating Detonation Engine
One of the practical challenges of the studies of rotating detonation engines (RDEs) is the direct estimation of losses from experimental measurements. This study attempts at resolving this limitation by combining a reduced order model (ROM) of the detonation wave characteristics with a Markov chain Monte Carlo parameter estimation framework. The model considers simple deflagration losses and the overall impact of deflagration on RDE performance. To evaluate this model, a Markov Chain Monte Carlo (MCMC) sampling technique was applied to estimate the loss parameters within the model for a set of conditions operated in hydrogen-air over a range of mass flow rates and equivalence ratios. The MCMC parameter estimation framework allowed for the determination of a posterior distribution of the loss parameters for each test condition, an examination of the correlation between the loss parameters and measured performance metrics of the RDE, and an uncertainty propagation of these parameters. The predicted model loss parameters were then compared to indirect experimental measurements of the deflagration combustion fractions to evaluating the validity of the approach, and shed light on the benefits and drawbacks of the model, measurement techniques, and the estimation framework.
Measurement of Piplus - Argon Absorption and Charge Exchange Interactions Using Protodune-sp
ProtoDUNE-SP is a prototype detector for the upcoming Deep Underground Neutrino Experiment (DUNE). It is a Liquid Argon Time Projection Chamber (LArTPC) with a similar configuration to DUNE's detector, and is designed to provide a test-bed for the future experiment. In addition to serving as a prototype, its 0.3 - 7 GeV/ c charged particle beam line provided the ability to perform physics measurements of pions, protons, kaons, muons, and electrons. Importantly, the LArTPC allowed for the measurement of hadronic interactions on argon nuclei.Pions are often present in the final state of neutrino interactions in the energy range of DUNE's neutrino beam. These particles can undergo various types of interactions with argon nuclei in the detector, and this can interfere with the characterization of neutrino interactions in DUNE's far detector. The rate of these so-called secondary interactionswill be accounted for using Monte Carlo simulation of neutrino interactions. Measurements of secondary interaction rates provide necessary data which can be used to estimate and propagate uncertainties or provide tunes of the secondary interaction model used within DUNE's experimental simulation.This analysis provides a simultaneous measurement of the piplus - Ar absorption and charge exchange cross sections using 1 GeV/ c piplus data taken by ProtoDUNE-SP during its initial run period in Fall 2018. This is one of the first hadronic interaction measurements provided by ProtoDUNE-SP. It is also the first piplus - Ar absorption measurement in 20 years and the first ever piplus - Ar charge exchange measurement.Read
Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models
In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.
Combining Astrometry and Elemental Abundances: The Case of the Candidate Pre-Gaia Halo Moving Groups G03-37, G18-39, and G21-22
While most moving groups are young and nearby, a small number have been identified in the Galactic halo. Understanding the origin and evolution of these groups is an important piece of reconstructing the formation history of the halo. Here we report on our analysis of three putative halo moving groups: G03-37, G18-39, and G21-22. Based on Gaia EDR3 data, the stars associated with each group show some scatter in velocity (e.g., Toomre diagram) and integrals of motion (energy, angular momentum) spaces, counter to expectations of moving-group stars. We choose the best candidate of the three groups, G21-22, for follow-up chemical analysis based on high-resolution spectroscopy of six presumptive members. Using a new Python code that uses a Bayesian method to self-consistently propagate uncertainties from stellar atmosphere solutions in calculating individual abundances and spectral synthesis, we derive the abundances of α- (Mg, Si, Ca, Ti), Fe-peak (Cr, Sc, Mn, Fe, Ni), odd-Z (Na, Al, V), and neutron-capture (Ba, Eu) elements for each star. We find that the G21-22 stars are not chemically homogeneous. Based on the kinematic analysis for all three groups and the chemical analysis for G21-22, we conclude the three are not genuine moving groups. The case for G21-22 demonstrates the benefit of combining kinematic and chemical information in identifying conatal populations when either alone may be insufficient. Comparing the integrals of motion and velocities of the six G21-22 stars with those of known structures in the halo, we tentatively associate them with the Gaia-Enceladus accretion event.
Eulerian Mapping Closure Approach for Probability Density Function of Concentration in Shear Flows
The Eulerian mapping closure approach is developed for uncertainty propagation in computational fluid mechanics. The approach is used to study the Probability Density Function (PDF) for the concentration of species advected by a random shear flow. An analytical argument shows that fluctuation of the concentration field at one point in space is non-Gaussian and exhibits stretched exponential form. An Eulerian mapping approach provides an appropriate approximation to both convection and diffusion terms and leads to a closed mapping equation. The results obtained describe the evolution of the initial Gaussian field, which is in agreement with direct numerical simulations.
Applications of Computational Methods for Dynamic Stability and Control Derivatives
Initial steps in the application o f a low-order panel method computational fluid dynamic (CFD) code to the calculation of aircraft dynamic stability and control (S&C) derivatives are documented. Several capabilities, unique to CFD but not unique to this particular demonstration, are identified and demonstrated in this paper. These unique capabilities complement conventional S&C techniques and they include the ability to: 1) perform maneuvers without the flow-kinematic restrictions and support interference commonly associated with experimental S&C facilities, 2) easily simulate advanced S&C testing techniques, 3) compute exact S&C derivatives with uncertainty propagation bounds, and 4) alter the flow physics associated with a particular testing technique from those observed in a wind or water tunnel test in order to isolate effects. Also presented are discussions about some computational issues associated with the simulation of S&C tests and selected results from numerous surface grid resolution studies performed during the course of the study.
Hybrid Gibbs Sampling and MCMC for CMB Analysis at Small Angular Scales
A) Gibbs Sampling has now been validated as an efficient, statistically exact, and practically useful method for "low-L" (as demonstrated on WMAP temperature polarization data). B) We are extending Gibbs sampling to directly propagate uncertainties in both foreground and instrument models to total uncertainty in cosmological parameters for the entire range of angular scales relevant for Planck. C) Made possible by inclusion of foreground model parameters in Gibbs sampling and hybrid MCMC and Gibbs sampling for the low signal to noise (high-L) regime. D) Future items to be included in the Bayesian framework include: 1) Integration with Hybrid Likelihood (or posterior) code for cosmological parameters; 2) Include other uncertainties in instrumental systematics? (I.e. beam uncertainties, noise estimation, calibration errors, other).
Carbon Consequences of Forest Disturbance and Recovery Across the Conterminous United States
Forests of North America are thought to constitute a significant long term sink for atmospheric carbon. The United States Forest Service Forest Inventory and Analysis (FIA) program has developed a large data base of stock changes derived from consecutive estimates of growing stock volume in the US. These data reveal a large and relatively stable increase in forest carbon stocks over the last two decades or more. The mechanisms underlying this national increase in forest stocks may include recovery of forests from past disturbances, net increases in forest area, and growth enhancement driven by climate or fertilization by CO2 and Nitrogen. Here we estimate the forest recovery component of the observed stock changes using FIA data on the age structure of US forests and carbon stocks as a function of age. The latter are used to parameterize forest disturbance and recovery processes in a carbon cycle model. We then apply resulting disturbance/recovery dynamics to landscapes and regions based on the forest age distributions. The analysis centers on 28 representative climate settings spread about forested regions of the conterminous US. We estimate carbon fluxes for each region and propagate uncertainties in calibration data through to the predicted fluxes. The largest recovery-driven carbon sinks are found in the South central, Pacific Northwest, and Pacific Southwest regions, with spatially averaged net ecosystem productivity (NEP) of about 100 g C / square m / a driven by forest age structure. Carbon sinks from recovery in the Northeast and Northern Lake States remain moderate to large owing to the legacy of historical clearing and relatively low modern disturbance rates from harvest and fire. At the continental scale, we find a conterminous U.S. forest NEP of only 0.16 Pg C/a from age structure in 2005, or only 0.047 Pg C/a of forest stock change after accounting for fire emissions and harvest transfers. Recent estimates of NEP derived from inventory stock change, harvest, and fire data show twice the NEP sink we derive from forest age distributions. We discuss possible reasons for the discrepancies including modeling errors and the possibility of climate and/or fertilization (CO2 or N) growth enhancements.
Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction
Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.
Impact of Land Model Calibration on Coupled Land-Atmosphere Prediction
Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry and wet land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystem in NASA's Land Information System (LIS-OPT/UE). The impact of the calibration on the a) spinup of the land surface used as initial conditions, and b) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. Changes in ambient weather and land-atmosphere coupling are evaluated along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Results indicate that the offline calibration leads to systematic improvements in land-PBL fluxes and near-surface temperature and humidity, and in the process provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.
Impact of Calibrated Land Surface Model Parameters on the Accuracy and Uncertainty of Land-Atmosphere Coupling in WRF Simulations
Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.
The NASA Langley Multidisciplinary Uncertainty Quantification Challenge
This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.