Results for “PROBABILISTIC ESTIMATION” — Page 38

DOE OSTI2014

The diskmass survey. VIII. On the relationship between disk stability and star formation

We study the relationship between the stability level of late-type galaxy disks and their star-formation activity using integral-field gaseous and stellar kinematic data. Specifically, we compare the two-component (gas+stars) stability parameter from Romeo and Wiegert (Q {sub RW}), incorporating stellar kinematic data for the first time, and the star-formation rate estimated from 21 cm continuum emission. We determine the stability level of each disk probabilistically using a Bayesian analysis of our data and a simple dynamical model. Our method incorporates the shape of the stellar velocity ellipsoid (SVE) and yields robust SVE measurements for over 90% of our sample. Averaging over this subsample, we find a meridional shape of σ{sub z}/σ{sub R}=0.51{sub −0.25}{sup +0.36} for the SVE and, at 1.5 disk scale lengths, a stability parameter of Q {sub RW} = 2.0 ± 0.9. We also find that the disk-averaged star-formation-rate surface density ( Σ-dot {sub e,∗}) is correlated with the disk-averaged gas and stellar mass surface densities (Σ {sub e,} {sub g} and Σ {sub e,} {sub *}) and anti-correlated with Q {sub RW}. We show that an anti-correlation between Σ-dot {sub e,∗} and Q {sub RW} can be predicted using empirical scaling relations, such that this outcome is consistent with well-established statistical properties of star-forming galaxies. Interestingly, Σ-dot {sub e,∗} is not correlated with the gas-only or star-only Toomre parameters, demonstrating the merit of calculating a multi-component stability parameter when comparing to star-formation activity. Finally, our results are consistent with the Ostriker et al. model of self-regulated star-formation, which predicts Σ-dot {sub e,∗}/Σ{sub e,g}∝Σ{sub e,∗}{sup 1/2}. Based on this and other theoretical expectations, we discuss the possibility of a physical link between disk stability level and star-formation rate in light of our empirical results.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1992

Effect of spectral shape on acoustic fatigue life estimates

Methods for estimating fatigue life due to random loading are briefly reviewed. These methods include a probabilistic approach in which the expected value of the rate of damage accumulation is computed by integrating over the probability density of damaging events and a method which consists of analyzing the response time history to count damaging events. It is noted that it is necessary to employ a time domain approach to perform Rainflow counting, while simple peak counting may be accomplished using the probabilistic method. Data obtained indicate that Rainflow counting produces significantly different fatigue life predictions than other methods that are commonly used in acoustic fatigue predictions. When low-frequency oscillations are present in a signal along with high-frequency components, peak counting will produce substantially shorter fatigue lives than Rainflow counting. It is concluded that Rainflow counting is capable of providing reliable fatigue life predictions for acoustic fatigue studies.

Miles, R. N.↗

NASA NTRS2013

The Challenges of Credible Thermal Protection System Reliability Quantification

The paper discusses several of the challenges associated with developing a credible reliability estimate for a human-rated crew capsule thermal protection system. The process of developing such a credible estimate is subject to the quantification, modeling and propagation of numerous uncertainties within a probabilistic analysis. The development of specific investment recommendations, to improve the reliability prediction, among various potential testing and programmatic options is then accomplished through Bayesian analysis.

Green, Lawrence L.↗

DOE OSTI2020

Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling

Global Sensitivity Analysis (GSA) aims to understand the relative importance of uncertain input variables to model response. Conventional GSA involves calculating sensitivity (Sobol’) indices for a model with known model parameter distributions. However, model parameters are affected by aleatory and epistemic uncertainty, with the latter often caused by lack of data. In this paper, we propose a new framework to quantify uncertainty in probability model-form and model parameters resulting from small datasets and integrate these uncertainties into Sobol’ index estimates. First, the process establishes, through Bayesian multimodel inference, a set of candidate probability models and their associated probabilities. Imprecise Sobol’ indices are calculated from these probability models using an importance sampling reweighting approach. This results in probabilistic Sobol’ indices, whose distribution characterizes uncertainty in the sensitivity resulting from small dataset size. The imprecise Sobol’ indices thus provide a measure of confidence in the sensitivity estimate and, moreover, can be used to inform data collection efforts targeted to minimize the impact of uncertainties. Through an example studying the parameters of a Timoshenko beam, we show that these probabilistic Sobol’ indices converge to the true/deterministic Sobol’ indices as the dataset size increases and hence, distribution-form uncertainty reduces. The approach is then applied to assess the sensitivity of the out-of-plane properties of an E-glass fiber composite material to its constituent properties. This second example illustrates the approach for an important class of materials with wide-ranging applications when data may be lacking for some input parameters.

42 ENGINEERING↗

NASA NTRS2002

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Non-NASA Center↗

DOE OSTI2017

Validation of PV-RPM Code in the System Advisor Model.

This paper describes efforts made by Sandia National Laboratories (SNL) and the National Renewable Energy Laboratory (NREL) to validate the SNL developed PV Reliability Performance Model (PV - RPM) algorithm as implemented in the NREL System Advisor Model (SAM). The PV - RPM model is a library of functions that estimates component failure and repair in a photovoltaic system over a desired simulation period. The failure and repair distributions in this paper are probabilistic representations of component failure and repair based on data collected by SNL for a PV power plant operating in Arizona. The validation effort focuses on whether the failure and repair dist ributions used in the SAM implementation result in estimated failures that match the expected failures developed in the proof - of - concept implementation. Results indicate that the SAM implementation of PV - RPM provides the same results as the proof - of - concep t implementation, indicating the algorithms were reproduced successfully.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2006

Impact of Probabilistic Weather on Flight Routing Decisions

Flight delays in the United States have been found to increase year after year, along with the increase in air traffic. During the four-month period from May through August of 2005, weather related delays accounted for roughly 70% of all reported delays, The current weather prediction in tactical (within 2 hours) timeframe is at manageable levels, however, the state of forecasting weather for strategic (2-6 hours) timeframe is still not dependable for long-term planning. In the absence of reliable severe weather forecasts, the decision-making for flights longer than two hours is challenging. This paper deals with an approach of using probabilistic weather prediction for Traffic Flow Management use, and a general method using this prediction for estimating expected values of flight length and delays in the National Airspace System (NAS). The current state-of-the-art convective weather forecasting is employed to aid the decision makers in arriving at decisions for traffic flow and flight planing. The six-agency effort working on the Next Generation Air Transportation System (NGATS) have considered weather-assimilated decision-making as one of the principal foci out of a list of eight. The weather Integrated Product Team has considered integrated weather information and improved aviation weather forecasts as two of the main efforts (Ref. 1, 2). Recently, research has focused on the concept of operations for strategic traffic flow management (Ref. 3) and how weather data can be integrated for improved decision-making for efficient traffic management initiatives (Ref. 4, 5). An overview of the weather data needs and benefits of various participants in the air traffic system along with available products can be found in Ref. 6. Previous work related to use of weather data in identifying and categorizing pilot intrusions into severe weather regions (Ref. 7, 8) has demonstrated a need for better forecasting in the strategic planning timeframes and moving towards a probabilistic description of weather (Ref. 9). This paper focuses on. specified probability in a local region for flight intrusion/deviation decision-making. The process uses a probabilistic weather description, implements that in a air traffic assessment system to study trajectories of aircraft crossing a cut-off probability contour. This value would be useful for meteorologists in creating optimum distribution profiles for severe weather, Once available, the expected values of flight path and aggregate delays are calculated for efficient operations. The current research, however, does not deal with the issue of multiple cell encounters, as well as echo tops, and will be a topic of future work.

Sheth, Kapil↗

NASA NTRS2015

Sensitivity to Factors Underlying the Hiatus

Recent trends in global mean surface air temperature fall outside the 90 range predicted by models using the CMIP5 forcings and scenarios; this recent period of muted warming is dubbed the hiatus. The hiatus has attracted broad attention in both the popular press and the scientific literature, primarily because of its perceived implications for understanding long-term trends. Many hypotheses have been offered to explain the warming slowdown during the hiatus, and comprehensive studies of this period across multiple variables and spatial scales will likely improve our understanding of the physical mechanisms driving global temperature change and variability.We argue, however, that decadal temperature trends by themselves are unlikely to constrain future trajectories of global mean temperature and that the hiatus does not significantly revise our understanding of overall climate sensitivity. Instead, we demonstrate that, because of the poorly constrained nature of the hiatus, model-observation disagreements over this period may be resolvable via uncertainties in the observations, modeled internal variability, forcing estimates, or (more likely) some combination of all three factors. We define the hiatus interval as 1998-2012, endpoints judiciously chosen to minimize observed warming by including the large 1998 El Nio event and excluding 2014, an exceptionally warm year. Such choices are fundamentally subjective and cannot be considered random, so any probabilistic statements regarding the likelihood of this occurring need to be made carefully. Using this definition, the observed global temperature trend estimates from four datasets fall outside the 5-95 interval predicted by the CMIP5 models. Here we explore some of the plausible explanations for this discrepancy, and show that no unique explanation is likely to fully account for the hiatus.

trends↗

DOE OSTI2021

SSRS (Stochastic Soaring Raptor Simulator)

SSRS (Stochastic Soaring Raptor Simulator) is a generalizable, probabilistic, and predictive tool for wind energy developers, ecologists, wildlife managers and industry consultants to estimate the potential for soaring raptors to interact with operating wind turbines, without the need for site-specific data collection. Rapid expansion of wind energy development across the world has exposed the risk of turbine collisions for birds and bats. The risk to obligate soaring raptors such as golden eagles is of particular concern due to their small population and influence on ecological balance. Golden eagles rely heavily on updrafts to subsidize their flight, putting them in direct conflict with operational wind turbines that utilize the same wind resource. Understanding the behavior of soaring raptors with varying atmospheric conditions is crucial for predicting and mitigating the risk of turbine collision. This software contains a predictive movement model that simulates individual flight paths of golden eagles during updraft-subsidized long-distance flight, including migration. For a given set of atmospheric conditions, the model simulates thousands of eagles at turbine-scale spatial resolution (50m) to produce a relative presence density map. The simulated eagles rely on updrafts to pursue uninterrupted directional flight with minimal energy expenditure, following fluid-flow principles. The simulator includes a stochastic model of eagle behavior and a systematic method of accounting for spatiotemporal variations in atmospheric conditions. This framework only requires publicly available atmospheric data to estimate orographic and thermal updrafts, ensuring general usability.

Sandhu, Rimple↗

DOE OSTI2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.

astronomy image processing↗

NASA NTRS1973

Nonstationary ascent wind analysis.

A statistical approach for computing launch vehicle response to inflight winds is presented. In the response analysis the zonal component of the ascent wind velocity is modeled as a nonstationary random process. Model parameters are computed by processing available measured wind data. Pertinent pitch plane response statistics are computed and their value in constructing fatigue load spectra for a reusable launch vehicle is demonstrated. A method for using the response statistics to estimate structural reliability is also discussed. The accuracy and the cost saving potential of the probabilistic structural dynamics approach are assessed by performing comparisons with digital simulation results.

Howell, L. J.↗

NASA NTRS1981

Semiempirical hot atom theory. I - Initialization and application

A semiempirical approach to the modeling of the kinetics of reaction systems containing both hot and nonhot atoms is proposed. The approach is based on the probabilistic kinetic theory of hot-atom reactions formulated by Wolfgang (1963), with transmission probabilities estimated for a rectangular potential barrier for hot-atom and nonhot-atom reactions. A computational scheme for determining product concentrations following hot and nonhot reactions in a system containing photolytically produced hot atoms is then applied to the DBr + CH4 and HBr + CD4 hot hydrogen atom systems studied by Martin and Willard (1964), and good agreement is obtained between theoretical and experimental results.

Aronowitz, S.↗

NASA NTRS1992

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples, volume 1

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Moore, N. R.↗

NASA NTRS1992

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 2: Software documentation

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes, These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Moore, N. R.↗

NASA NTRS1992

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 3: Structure and listing of programs

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Moore, N. R.↗

NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This presentation describes the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗

DOE OSTI2020

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗