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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SIMULATED HUMAN ERROR PROBABILITY AND ITS APPLICATION TO DYNAMIC HUMAN FAILURE EVENTS

Abstract Objectives: Human reliability analysis (HRA) methods typically analyze human failure events (HFEs) at the overall task level. For dynamic HRA, it is important to model human activities at the subtask level. There exists a disconnect between dynamic subtask level and static task level that presents issues when modeling dynamic scenarios. For example, the SPAR-H method is typically used to calculate the human error probability (HEP) at the task level. As demonstrated in this paper, quantification in SPAR-H does not translate to the subtask level. Methods: Two different discrete distributions were generated for each SPAR-H Performance Shaping Factor (PSF) to define the frequency of PSF levels. The first distribution was a uniform, or uninformed distribution that assumed the frequency of each PSF level was equally likely. The second non-continuous distribution took the frequency of PSF level as identified from an assessment of the HERA database. These two different approaches were created to identify the resulting distribution of the HEP. The resulting HEP that appears closer to the known distribution, a log-normal centered on 1E-3, is the more desirable. Each approach then has median, average and maximum HFE calculations applied. To calculate these three values, three events, A, B and C are generated from the PSF level frequencies comprised of subtasks. The median HFE selects the median PSF level from each PSF and calculates HEP. The average HFE takes the mean PSF level, and the maximum takes the maximum PSF level. The same data set of subtask HEPs yields starkly different HEPs when aggregated to the HFE level in SPAR-H. Results: Assuming that each PSF level in each HFE is equally likely creates an unrealistic distribution of the HEP that is centered at 1. Next the observed frequency of PSF levels was applied with the resulting HEP behaving log-normally with a majority of the values under 2.5% HEP. The median, average and maximum HFE calculations did yield different answers for the HFE. The HFE maximum grossly over estimates the HFE, while the HFE distribution occurs less than HFE median, and greater than HFE average. Conclusions: Dynamic task modeling can be perused through the framework of SPAR-H. Identification of distributions associated with each PSF needs to be defined, and may change depending upon the scenario. However it is very unlikely that each PSF level is equally likely as the resulting HEP distribution is strongly centered at 100%, which is unrealistic. Other distributions may need to be identified for PSFs, to facilitate the transition to dynamic task modeling. Additionally discrete distributions need to be exchanged for continuous so that simulations for the HFE can further advance. This paper provides a method to explore dynamic subtask to task translation and provides examples of the process using the SPAR-H method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter

Abstract Hydraulic stimulation of low‐permeability rocks in enhanced geothermal systems, shale resources, and CO 2 storage aquifers can trigger microseismic events, also known as microearthquakes (MEQs). The distribution of microseismic source locations in the reservoir may reveal important information about the distribution of hydraulic and geomechanical rock properties. In this paper, we present a framework for conditioning heterogeneous rock permeability and geomechanical property distributions on microseismic data. To simulate the multiphysics processes in these systems, we combine a fully coupled flow and geomechanical model with the Mohr‐Coulomb type rock failure criterion. The resulting multiphysics simulation constitutes the forecast model that relates microseismic source locations to reservoir rock properties. We adopt this forward model in an ensemble Kalman filter (EnKF) data assimilation framework to jointly estimate reservoir permeability and geomechanical property distributions from injection‐induced microseismic response measurements. We show that integration of a large number of spatially correlated microseismic data with practical ensemble sizes can lead to severe underestimation of ensemble spread, and eventually ensemble collapse. To mitigate the variance underestimation issue, two low‐rank data representation schemes are presented and discussed. In the first approach, microseismic data are projected onto a low‐dimensional subspace defined by the left singular vectors of the perturbed observation matrix. The second method uses a coarser grid for representing the microseismic data. A series of numerical experiments is presented to evaluate the performance of the proposed methods and to illustrate their applicability for assimilating microseismic data into coupled flow and geomechanical forward models to estimate multiphysics rock properties.

Tarrahi, Mohammadali↗
NASA NTRS1988

Trajectory computation during a maneuver: Thrust estimation with the Goddard Trajectory Determination System (GTDS)

Existing thrust modeling capabilities of the Goddard Trajectory Determination System (GTDS) have been enhanced to allow calibration of the onboard propulsion system. These enhancements provide one or more thrust scale factors, based on estimation using the batch least-squares technique, for the case of along-track thrust and the case of attitude-dependent thrust. The enhancements are evaluated using simulated tracking measurements for a test spacecraft and using actual tracking measurements for the Earth Radiation Budget Satellite (ERBS). The effects of tracking measurement noise and distribution on the accuracy of the estimation are investigated and found to be significant. Results and conclusions of the analysis are presented.

Beri, A. C.↗
DOE OSTI2015

Uncertainty quantification for radiation measurements: Bottom-up error variance estimation using calibration information

One example of top-down uncertainty quantification (UQ) involves comparing two or more measurements on each of multiple items. One example of bottom-up UQ expresses a measurement result as a function of one or more input variables that have associated errors, such as a measured count rate, which individually (or collectively) can be evaluated for impact on the uncertainty in the resulting measured value. In practice, it is often found that top-down UQ exhibits larger error variances than bottom-up UQ because some error sources are present in the fielded assay methods used in top-down UQ that are not present (or not recognized) in the assay studies used in bottom-up UQ One would like better consistency between the two approaches in order to claim understanding of the measurement process. The purpose of this paper is to refine bottom-up uncertainty estimation by using calibration information so that if there are no unknown error sources, the refined bottom-up uncertainty estimate will agree with the top-down uncertainty estimate to within a specified tolerance. Then, in practice, if the top-down uncertainty estimate is larger than the refined bottom-up uncertainty estimate by more than the specified tolerance, there must be omitted sources of error beyond those predicted from calibration uncertainty. The paper develops a refined bottom-up uncertainty approach for four cases of simple linear calibration: (1) inverse regression with negligible error in predictors, (2) inverse regression with non negligible error in predictors, (3) classical regression followed by inversion with negligible error in predictors, and (4) classical regression followed by inversion with non-negligible errors in predictors. Our illustrations are of general interest, but are drawn from our experience with nuclear material assay by non-destructive assay. The main example we use is gamma spectroscopy that applies the enrichment meter principle. Previous papers that ignore error in predictors have shown a tendency for inverse regression to have lower error variance than classical regression followed by inversion. Our paper supports that tendency both with and without error in predictors. Furthermore, the paper shows that calibration parameter estimates using error in predictor methods perform worse than without using error in predictor methods in the case of inverse regression, but perform better than without using error in predictor methods in the case of classical regression followed by inversion. Both inverse and classical regression involve the ratio of dependent random variables; therefore, the assumed error distribution(s) will matter in parameter estimation and in uncertainty calculations. Mainly for that reason, calibration using a single predictor is distinct from simple regression, and it has not been thoroughly treated in the literature, nor in the ISO Guide to the Expression of Uncertainty in Measurements (GUM). This refined approach is based on simulation, because we illustrate that analytical approximations are not adequate when there are, for example, 10 or fewer calibration measurements, which is common in calibration applications, each consisting of measured responses from known quantities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Uranium particle age dating, aggregation, and model age best estimators

We present important aspects of uranium particle age dating by Large-Geometry Secondary Ion Mass Spectrometry (LG-SIMS) that can introduce bias and increase model age uncertainties, especially for small, young, and/or low-enriched particles. This metrology is important for applications related to International Nuclear Safeguards. We explore influential factors related to model age estimation, including the effects of evolving surface chemistry on inter-element measurements of particles (e.g., Th and U), detector background, and aggregation methods using simulated and actual particle samples. We introduce a new model age estimator, called “mid68”, that supplements 95% confidence intervals, providing a “best estimate” and uncertainty about the most likely age. The mid68 estimator can be calculated using the Feldman and Cousins method or Bayesian methods and provides a value with a symmetric uncertainty that can be used for calculations and approximate aggregation of processed model age values when the raw data and correction factors are not available. For particles yielding low 230 Th counts amidst nonzero detector background, their underlying model age probability distributions are asymmetric, so the mid68 estimator provides additional robust information regarding the underlying model age likelihood. This study provides a comprehensive and timely examination of critical aspects of uranium particle age dating as more laboratories establish particle chronometry capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

GPS Spoofing Mitigation and Timing Risk Analysis in Networked Phasor Measurement Units via Stochastic Reachability

To address phasor measurement unit (PMU) vulnerability to spoofing, we propose the use of a set-valued state estimation technique known as stochastic reachability (SR)-based distributed Kalman filter (DKF) that computes secure global positioning system (GPS) timing across a network of receivers. Utilizing SR, we estimate not only GPS time but also its stochastic reachable set, which is parameterized by probabilistic zonotope (p-Zonotope). While requiring known measurement error bounds in only non-spoofed conditions, we designed a two-tiered approach. We first performed measurement-level spoofing mitigation via deviation of a measurement innovation from its expected p-Zonotope. We then performed state-level timing risk analysis via a determination of the intersection probability of the estimated p-Zonotope with an unsafe set that violates IEEE C37.118.1a-2014 standards. Finally, we validated our SR-DKF algorithm by subjecting it to a simulated receiver network to coordinate signal-level spoofing. We demonstrate improved timing accuracy and successful spoofing mitigation via the use of our SR-DKF algorithm. We also validated the robustness of the estimated timing risk as the number of receivers were varied.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Estimation of Behind-the-Meter Solar Generation by Integrating Physical with Statistical Models

Accurate estimation of solar photovoltaic (PV) generation is crucial for distribution grid control and optimization. Unfortunately, most of the residential solar PV installations are behind-the-meter. Thus, utilities only have access to the net load readings. This paper presents an unsupervised framework for estimating solar PV generation by disaggregating the net load readings. The proposed framework synergistically combines a physical PV system performance model with a statistical model for load estimation. Specifically, our algorithm iteratively estimates solar PV generation with a physical model and electric load with the Hidden Markov model regression. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is validated against net load and solar PV generation data gathered from residential customers located in Austin, Texas. The validation results show that our method reduces mean squared error by 44% compared to the state-of-the-art disaggregation algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2011

Downscaling NASA Climatological Data to Produce Detailed Climate Zone Maps

The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of downscaling NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.

Chandler, William S.↗
NASA NTRS2018

Development and Validation of Physics Based Models for Ice Shedding

Calculations for ice accretion and shedding are presented for a model scale rotor in hover. The aerodynamic characteristics of the rotor are first computed using a combined blade element-momentum theory. The effective angles of attack, and the local flow velocity are used within the NASA Glenn solver LEWICE to estimate the collection efficiency. The computed convection efficiency and the surface pressure distribution from a panel method within LEWICE are used to estimate the ice accretion over the rotor blades over an elapsed time interval. Finally, a force balance approach is used to establish shedding events where the centrifugal force over the ice mass exceeds the adhesive forces at the rotor surface and the cohesive forces between adjacent masses of ice. Preliminary comparisons with test data acquired at the Pennsylvania State Icing Research Tunnel are presented. Sensitivity of the ice shedding events to surface roughness, adhesive strength, cohesive strength, and ambient conditions is discussed.

Gupta, Avani↗
NASA NTRS2021

Mapping Global Forest Age from Forest Inventories, Biomass and Climate Data

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Simon Besnard↗
DOE OSTI2017

Calibrating Physical Parameters in House Models Using Aggregate AC Power Demand

For residential houses, the air conditioning (AC) units are one of the major resources that can provide significant flexibility in energy use for the purpose of demand response. To quantify the flexibility, the characteristics of all the houses need to be accurately estimated, so that certain house models can be used to predict the dynamics of the house temperatures in order to adjust the setpoints accordingly to provide demand response while maintaining the same comfort levels. In this paper, we propose an approach using the Reverse Monte Carlo modeling method and aggregate house models to calibrate the distribution parameters of the house models for a population of residential houses. Given the aggregate AC power demand for the population, the approach can successfully estimate the distribution parameters for the sensitive physical parameters based on our previous uncertainty quantification study, such as the mean of the floor areas of the houses.

Sun, Yannan↗
NASA NTRS1988

MODIS Information, Data, and Control System (MIDACS) system specifications and conceptual design

The MODIS Information, Data, and Control System (MIDACS) Specifications and Conceptual Design Document discusses system level requirements, the overall operating environment in which requirements must be met, and a breakdown of MIDACS into component subsystems, which include the Instrument Support Terminal, the Instrument Control Center, the Team Member Computing Facility, the Central Data Handling Facility, and the Data Archive and Distribution System. The specifications include sizing estimates for the processing and storage capacities of each data system element, as well as traffic analyses of data flows between the elements internally, and also externally across the data system interfaces. The specifications for the data system, as well as for the individual planning and scheduling, control and monitoring, data acquisition and processing, calibration and validation, and data archive and distribution components, do not yet fully specify the data system in the complete manner needed to achieve the scientific objectives of the MODIS instruments and science teams. The teams have not yet been formed; however, it was possible to develop the specifications and conceptual design based on the present concept of EosDIS, the Level-1 and Level-2 Functional Requirements Documents, the Operations Concept, and through interviews and meetings with key members of the scientific community.

Han, D.↗
NASA NTRS1978

Optimal estimation for discrete time jump processes

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (DTJP) are derived. The approach used is based on the a posteriori probability of a nonobservable event expressed in terms of the a priori probability of that event and of the sample function probability of the DTJP. Thus a general representation is obtained for optimum estimates, and recursive equations are derived for minimum mean-squared error (MMSE) estimates. In general, MMSE estimates are nonlinear functions of the observations. The problem is considered of estimating the rate of a DTJP when the rate is a random variable with a beta probability density function and the jump amplitudes are binomially distributed. It is shown that the MMSE estimates are linear. The class of beta density functions is rather rich and explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.

Vaca, M. V.↗
NASA NTRS2017

Analysis of in Vitro Evolution Reveals the Underlying Distribution of Catalytic Activity Among Random Sequences

The emergence of catalytic RNA is believed to have been a key event during the origin of life. Understanding how catalytic activity is distributed across random sequences is fundamental to estimating the probability that catalytic sequences would emerge. Here, we analyze the in vitro evolution of triphosphorylating ribozymes and translate their fitnesses into absolute estimates of catalytic activity for hundreds of ribozyme families. The analysis efficiently identified highly active ribozymes and estimated catalytic activity with good accuracy. The evolutionary dynamics follow Fisher’s Fundamental Theorem of Natural Selection and a corollary, permitting retrospective inference of the distribution of fitness and activity in the random sequence pool for the first time. The frequency distribution of rate constants appears to be log-normal, with a surprisingly steep dropoff at higher activity, consistent with a mechanism for the emergence of activity as the product of many independent contributions.

Abe Pressman↗
DOE OSTI2020

Fast Automatic Knot Placement Method for Accurate B-spline Curve Fitting

The choice of knot vector has immense influence on the resulting accuracy of a B-spline approximation of a curve. However, despite the significance of this problem and the various solutions that were proposed in the literature, optimizing the number and placement of knots remains a difficult task. This paper presents a novel method for the approximation of a curve by a B-spline of arbitrary order, which automatically determines a knot vector that achieves high approximation quality. Additionally, at the core of our approach is a feature function that characterizes the amount and spatial distribution of geometric details in the input curve by estimating its derivatives. Knots are then selected in such a way as to evenly distribute the feature contents across their intervals. A comparison to the state of the art for a wide variety of curves shows that our method is faster and achieves more accurate reconstruction results, while typically reducing the number of necessary knots.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Top-of-Atmosphere Direct Radiative Effect of Aerosols over Global Oceans from Merged CERES and MODIS Observations

The direct radiative effect of aerosols (DREA) is defined as the difference between radiative fluxes in the absence and presence of aerosols. In this study, the direct radiative effect of aerosols is estimated for 46 months (March, 2000 to December, 2003) of merged CERES and MODIS Terra global measurements over ocean. This analysis includes the contribution from clear regions in both clear and partly cloudy CERES footprints. MODIS-CERES narrow-to-broadband regressions are developed to convert clear-sky MODIS narrowband radiances to broadband SW radiances, and CERES clear-sky Angular Distribution Models (ADMs) are used to estimate the corresponding TOA radiative fluxes needed to determine the DREA. Clear-sky MODIS pixels are identified using two independent cloud masks: (i) the NOAA-NESDIS algorithm used for inferring aerosol properties from MODIS on the CERES Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product (NOAA-SSF); and (ii) the standard algorithm used by the MODIS aerosol group to produce the MODO4 product (MODO4). Over global oceans, direct radiative cooling by aerosols for clear scenes identified from MODO4 is estimated to be 5.5 W m-2, compared to 3.8 W m-2 for clear scenes from NOAA-SSF. Regionally, differences are largest in areas affected by dust aerosol, such as oceanic regions adjacent to the Saharan and Saudi Arabian deserts, and in northern Pacific Ocean regions influenced by dust transported from Asia. The net total-sky (clear and cloudy) DREA is negative (cooling) and is estimated to be -2.0 W m-2 from MOD04, and -1.6 W m-2 from NOAA-SSF. The DREA is shown to have pronounced seasonal cycles in the Northern Hemisphere and large year-to-year fluctuations near deserts. However, no systematic trend in deseasonalized anomalies of the DREA is observed over the 46-month time series considered.

Loeb, N. G.↗
DOE OSTI2024

Density estimation via measure transport: Outlook for applications in the biological sciences

Abstract One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.

gene expression data↗