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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides]

The ChemCam instrument of Curiosity uses laser-induced breakdown spectroscopy (LIBS). It fires a laser at target and vaporizes rock surfaces, creating a plasma. Three spectrographs divide the plasma light into wavelengths for chemical analysis: ultraviolet, violet, and visible near-infrared. Regression methods (SVR, PCR, CNN) have been employed for calibration (prediction of the elemental composition of samples); however, labeled ChemCam samples are limited. Here, we focus on unsupervised learning and employ generative models from ChemCam analysis. Further, we use labels (supervised) in combination to the generative model to compute uncertainties related to predictions. We report generative modeling can be successfully applied to model real-world data. Normalizing flow models can be efficiently constructed on latent spaces for fast downstream inference. Unsupervised and supervised learning can be combined to form an uncertainty quantification framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1980

Estimation of uncertainty in isotherms deduced from Hugoniots resulting from shockwave generated defects

The problem of how defect concentrations produced in a shock experiment affect the isothermal equation of state is considered. From the few experimental results available that bear directly on this question, it appears the effect could be substantial, i.e., the pressure in material free of defects may be 10-25% lower at a given volume than the isothermal pressure deduced from Hugoniot data on the assumption that defects are negligible in the shocked material. This correction caused by the presence of defects is called the defect pressure.

Ruoff, A. L.↗
NASA NTRS1995

A PC program for estimating measurement uncertainty for aeronautics test instrumentation

A personal computer program was developed which provides aeronautics and operations engineers at Lewis Research Center with a uniform method to quickly provide values for the uncertainty in test measurements and research results. The software package used for performing the calculations is Mathcad 4.0, a Windows version of a program which provides an interactive user interface for entering values directly into equations with immediate display of results. The error contribution from each component of the system is identified individually in terms of the parameter measured. The final result is given in common units, SI units, and percent of full scale range. The program also lists the specifications for all instrumentation and calibration equipment used for the analysis. It provides a presentation-quality printed output which can be used directly for reports and documents.

Blumenthal, Philip Z.↗
NASA NTRS2012

Estimation of Uncertainties for a Supersonic Retro-Propulsion Model Validation Experiment in a Wind Tunnel

A high-quality model validation experiment was performed in the NASA Langley Research Center Unitary Plan Wind Tunnel to assess the predictive accuracy of computational fluid dynamics (CFD) models for a blunt-body supersonic retro-propulsion configuration at Mach numbers from 2.4 to 4.6. Static and fluctuating surface pressure data were acquired on a 5-inch-diameter test article with a forebody composed of a spherically-blunted, 70-degree half-angle cone and a cylindrical aft body. One non-powered configuration with a smooth outer mold line was tested as well as three different powered, forward-firing nozzle configurations: a centerline nozzle, three nozzles equally spaced around the forebody, and a combination with all four nozzles. A key objective of the experiment was the determination of experimental uncertainties from a range of sources such as random measurement error, flowfield non-uniformity, and model/instrumentation asymmetries. This paper discusses the design of the experiment towards capturing these uncertainties for the baseline non-powered configuration, the methodology utilized in quantifying the various sources of uncertainty, and examples of the uncertainties applied to non-powered and powered experimental results. The analysis showed that flowfield nonuniformity was the dominant contributor to the overall uncertainty a finding in agreement with other experiments that have quantified various sources of uncertainty.

Rhode, Matthew N.↗
NASA NTRS2013

Validating an Air Traffic Management Concept of Operation Using Statistical Modeling

Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysis

Statistical emulation↗
NASA NTRS2015

How Certain are We of the Uncertainties in Recent Ozone Profile Trend Assessments of Merged Limbo Ccultation Records? Challenges and Possible Ways Forward

Most recent assessments of long-term changes in the vertical distribution of ozone (by e.g. WMO and SI2N) rely on data sets that integrate observations by multiple instruments. Several merged satellite ozone profile records have been developed over the past few years; each considers a particular set of instruments and adopts a particular merging strategy. Their intercomparison by Tummon et al. revealed that the current merging schemes are not sufficiently refined to correct for all major differences between the limb/occultation records. This shortcoming introduces uncertainties that need to be known to obtain a sound interpretation of the different satellite-based trend studies. In practice however, producing realistic uncertainty estimates is an intricate task which depends on a sufficiently detailed understanding of the characteristics of each contributing data record and on the subsequent interplay and propagation of these through the merging scheme. Our presentation discusses these challenges in the context of limb/occultation ozone profile records, but they are equally relevant for other instruments and atmospheric measurements. We start by showing how the NDACC and GAW-affiliated ground-based networks of ozonesonde and lidar instruments allowed us to characterize fourteen limb/occultation ozone profile records, together providing a global view over the last three decades. Our prime focus will be on techniques to estimate long-term drift since our results suggest this is the main driver of the major trend differences between the merged data sets. The single-instrument drift estimates are then used for a tentative estimate of the systematic uncertainty in the profile trends from merged data records. We conclude by reflecting on possible further steps needed to improve the merging algorithms and to obtain a better characterization of the uncertainties involved.

Merging Algorithms↗
NASA NTRS2018

Homogenizing and Estimating the Uncertainty in NOAA's Long-Term Vertical Ozone Profile Records Measured with the Electrochemical Concentration Cell Ozonesonde

NOAA's program of long-term monitoring of the vertical distribution of ozone with electrochemical concentration cell (ECC) ozonesondes has undergone a number of changes over the 50-year record. In order to produce a homogenous data set, these changes must be documented and, where necessary, appropriate corrections applied. This is the first comprehensive and consistent reprocessing of NOAA's ozonesonde data records that corrects for these changes using the rawest form of the data (cell current and pump temperature) in native resolution as well as a point-by-point uncertainty calculation that is unique to each sounding. The reprocessing is carried out uniformly at all eight ozonesonde sites in NOAA's network with differences in sensing solution and ozonesonde types accounted for in the same way at all sites. The corrections used to homogenize the NOAA ozonesonde data records greatly improve the ozonesonde measurements with an average one sigma uncertainty of +/- 4-6% in the stratosphere and +/- 5-20% in the troposphere. A comparison of the integrated column ozone from the ozonesonde profile with co-located Dobson spectrophotometers total column ozone measurements shows agreement within +/- 5% for >70% of the profiles. Very good agreement is also found in the stratosphere between ozonesonde profiles and profiles retrieved from the Solar Backscatter Ultraviolet (SBUV) instruments.

ozonesonde↗
NASA NTRS2019

Estimating Residual Uncertainties for Lunar Irradiance Measurements Due to Imaging Acquisition Parameters

Lunar calibration is a commonly used method to track a climate satellite sensor's long-term radiometric stability. We present a modeling approach to examine the satellite sensor lunar observation uncertainties due to several important aspects related to the lunar image acquisition by the satellite sensor: lunar pixel shift, point spread function (PSF), lunar orientation, pitch, and oversampling rates. Our analyses can be summarized as follows: (1) The sensor observed lunar irradiance can vary due to small lunar pixel shift if the PSF is less than ideal; (2) During lunar calibration, an unstable oversampling rate due to spacecraft control will result in errors in observed lunar irradiance. A drift in oversampling rate would result in a bias in observed lunar irradiance and a random variation in oversampling rate would cause random error in lunar irradiance. Increasing the overall oversampling rates can reduce random error in observed lunar irradiance but would not change the biases in the observation; (3) Furthermore, the biases can vary when the Moon is observed at different orientations. Our results show impacts on observed lunar irradiance are on the order of 0.1 percent, which is a significant part of the overall uncertainty for a lunar irradiance measurement of a climate satellite sensor.

Oversampling↗
NASA NTRS2021

Nonlinear Unsteady Aerodynamic Modeling Using Empirical Orthogonal Functions

Empirical orthogonal function modeling is explained and applied to identify compact discrete-time nonlinear unsteady aerodynamic models from data generated by an unsteady three-dimensional compressible Navier-Stokes flow solver for an airfoil undergoing various pitching motions. Model structures, model parameter estimates, and model parameter uncertainty estimates for nondimensional lift, drag, and pitching moment coefficient models were determined autonomously and directly from the data. Prediction tests using data that were not used in the modeling process showed that the identified models exhibited excellent prediction capability, which is a strong indicator of an accurate model.

empirical↗