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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.

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NASA NTRS2008

Uncertainty and Sensitivity Analyses of a Two-Parameter Impedance Prediction Model

This paper presents comparisons of predicted impedance uncertainty limits derived from Monte-Carlo-type simulations with a Two-Parameter (TP) impedance prediction model and measured impedance uncertainty limits based on multiple tests acquired in NASA Langley test rigs. These predicted and measured impedance uncertainty limits are used to evaluate the effects of simultaneous randomization of each input parameter for the impedance prediction and measurement processes. A sensitivity analysis is then used to further evaluate the TP prediction model by varying its input parameters on an individual basis. The variation imposed on the input parameters is based on measurements conducted with multiple tests in the NASA Langley normal incidence and grazing incidence impedance tubes; thus, the input parameters are assigned uncertainties commensurate with those of the measured data. These same measured data are used with the NASA Langley impedance measurement (eduction) processes to determine the corresponding measured impedance uncertainty limits, such that the predicted and measured impedance uncertainty limits (95% confidence intervals) can be compared. The measured reactance 95% confidence intervals encompass the corresponding predicted reactance confidence intervals over the frequency range of interest. The same is true for the confidence intervals of the measured and predicted resistance at near-resonance frequencies, but the predicted resistance confidence intervals are lower than the measured resistance confidence intervals (no overlap) at frequencies away from resonance. A sensitivity analysis indicates the discharge coefficient uncertainty is the major contributor to uncertainty in the predicted impedances for the perforate-over-honeycomb liner used in this study. This insight regarding the relative importance of each input parameter will be used to guide the design of experiments with test rigs currently being brought on-line at NASA Langley.

Jones, M. G.↗

NASA NTRS1986

Temperature dependence of the reaction NO + NO3 - 2NO2

The flash photolysis-visible absorption technique has been used to measure rate constants for the reaction NO + NO3 2NO2 over the temperature range 224-328 K. The temperature dependence of the rate constant is given by the expression K1(T) = (1.59 + or - 0.32) x 10 to the -11th exp(122/T)cu cm/molecule/s where the stated uncertainties refer to the + or - sigma limits from both random and systematic errors.

Sander, S. P.↗

NASA NTRS1988

Cosmological parameters and evolution of the galaxy luminosity function

The relationship between the observed distribution of discrete sources of a flux limited sample, the luminosity function of these sources, and the cosmological model is discussed. It is stressed that some assumptions about the form and evolution of the luminosity function must be made in order to determine the cosmological parameters from the observed distribution of sources. Presented is a method to test the validity of these assumptions using the observations. It is shown how, using higher moments of the observed distribution, one can determine, independently of the cosmological model, all parameters of the luminosity function except those describing evolution of the density and the luminosity of the luminosity function. These methods are applied to the sample of approximately 1000 galaxies recently used by Loh and Spillar to determine a value of the cosmological density parameter Omega approx = 1. It is shown that the assumptions made by Loh and Spillar about the luminosity function are inconsistent with the data, and that a self-consistent treatment of the data indicates a lower value of Omega approx = 0.2 and a flatter luminosity function. It should be noted, however, that incompleteness in the sample could cause a flattening of the luminosity function and lower the calculated value of Omega and that uncertainty in the values of these parameters due to random fluctuations is large.

Caditz, David↗

NASA NTRS1989

Cosmological parameters and evolution of the galaxy luminosity function

Caditz, David↗

NASA NTRS1990

The simulation of fluid dynamic uncertainties in the SSME turbopump

The aerodynamic uncertainties in the reusable rocket engine turbopumps due to randomness in their cryogenic environment are investigated. The probabilistic simulation of the turbopumps' aerodynamic response was accomplished using the panel method coupled with Fast Probability Integration methods. The results are presented for the probabilistic rotor blades and splitter loading and their sensitivity to specified flow coefficient and rotor preswirl variance.

Hamed, Awatef↗

NASA NTRS1991

Probabilistic evaluation of SSME structural components

The application is described of Composite Load Spectra (CLS) and Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) family of computer codes to the probabilistic structural analysis of four Space Shuttle Main Engine (SSME) space propulsion system components. These components are subjected to environments that are influenced by many random variables. The applications consider a wide breadth of uncertainties encountered in practice, while simultaneously covering a wide area of structural mechanics. This has been done consistent with the primary design requirement for each component. The probabilistic application studies are discussed using finite element models that have been typically used in the past in deterministic analysis studies.

Rajagopal, K. R.↗

NASA NTRS1993

Resolved stars in nearby galaxies: Ground-based photometry of M81

Using the Canada-France-Hawaii Telescope (CFHT) we have obtained three closely spaced epochs of calibrated Blue Violet Red Infrared (BVRI) CCD imaging of two fields in M81, each known to contain a thirty-day Cepheid. Calibrated BVRI photometry of the brightest stars in these fields is presented. The slope of the luminosity function from the brightest 3-4 mag of the main-sequence blue plume is consistent with similar determinations of the apparent luminosity function in other resolved galaxies, thereby removing the one potential deviation from universality noted by Freedman in a photographic study of luminosity functions in nearby resolved galaxies. Under the assumption that the two Cepheids are representative, a reddening-law fit to the multiwavelength BVRI period-luminosity moduli give a true distance modulus of (m-M)sub 0 = 27.79 mag for M81, corresponding to a linear distance of 3.6 Mpc. An error analysis shows that the derived true distance modulus has a random error of +/- 0.28 mag (due to the photometric uncertainties in the BVRI data), with a systematic uncertainty of +/- 0.10 mag (accounting for the combined effects of unknown phasing of the data points, and the unknown positioning of these particular stars within the Cepheid instabiliy strip).

Madore, Barry F.↗

NASA NTRS2008

Sensitivity of Polar Stratospheric Ozone Loss to Uncertainties in Chemical Reaction Kinetics

Several recent observational and laboratory studies of processes involved in polar stratospheric ozone loss have prompted a reexamination of aspect of out understanding for this key indicator of global change. To a large extent, our confidence in understanding and projecting changes in polar and global ozone is based on our ability to to simulate these process in numerical models of chemistry and transport. These models depend on laboratory-measured kinetic reaction rates and photlysis cross section to simulate molecular interactions. In this study we use a simple box-model scenario for Antarctic ozone to estimate the uncertainty in loss attributable to known reaction kinetic uncertainties. Following the method of earlier work, rates and uncertainties from the latest laboratory evaluation are applied in random combinations. We determine the key reaction and rates contributing the largest potential errors and compare the results to observations to evaluate which combinations are consistent with atmospheric data. Implications for our theoretical and practical understanding of polar ozone loss will be assessed.

Kawa, S. Randolph↗

NASA NTRS2008

Sensitivity of Polar Stratospheric Ozone Loss to Uncertainties in Chemical Reaction Kinetics

Several recent observational and laboratory studies of processes involved in polar stratospheric ozone loss have prompted a reexamination of aspects of our understanding for this key indicator of global change. To a large extent, our confidence in understanding and projecting changes in polar and global ozone is based on our ability to simulate these processes in numerical models of chemistry and transport. The fidelity of the models is assessed in comparison with a wide range of observations. These models depend on laboratory-measured kinetic reaction rates and photolysis cross sections to simulate molecular interactions. A typical stratospheric chemistry mechanism has on the order of 50- 100 species undergoing over a hundred intermolecular reactions and several tens of photolysis reactions. The rates of all of these reactions are subject to uncertainty, some substantial. Given the complexity of the models, however, it is difficult to quantify uncertainties in many aspects of system. In this study we use a simple box-model scenario for Antarctic ozone to estimate the uncertainty in loss attributable to known reaction kinetic uncertainties. Following the method of earlier work, rates and uncertainties from the latest laboratory evaluations are applied in random combinations. We determine the key reactions and rates contributing the largest potential errors and compare the results to observations to evaluate which combinations are consistent with atmospheric data. Implications for our theoretical and practical understanding of polar ozone loss will be assessed.

Kawa, S. Randolph↗

NASA NTRS2022

How Well do Multisatellite Products Capture the Space-Time Dynamics of Precipitation? Part II: Building an Error Model Through Spectral System Identification

Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatio-temporal scale. We propose a spectral error model which accounts for the neighboring space-time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency-wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space-time linear filtering term. The random errors are represented as a non-stationary additive noise. The spectral error model is applied to the IMERG multi satellite precipitation product and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge-radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass. While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from totally different sources shall not be expected to automatically have statistically independent errors.

Precipitation↗

NASA NTRS2025

Simultaneous Localization and Mapping for Spacecraft Rendezvous and Proximity Operations Using Random Finite Sets

Future space missions require spacecraft to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current navigation filters used for RPO can have difficulty when optical sensors are used, due to complications with data association, high measurement uncertainty, and clutter. This paper provides an initial demonstration of the feasibility of using random finite set (RFS) filters for spacecraft relative navigation and pose estimation. Spacecraft relative navigation is formulated as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. Simulated flash LIDAR measurements are processed using a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer over time. Results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario.

David E Gaylor↗

NASA NTRS2014

Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

CFD↗

NASA NTRS1991

Probabilistic fracture finite elements

The Probabilistic Fracture Mechanics (PFM) is a promising method for estimating the fatigue life and inspection cycles for mechanical and structural components. The Probability Finite Element Method (PFEM), which is based on second moment analysis, has proved to be a promising, practical approach to handle problems with uncertainties. As the PFEM provides a powerful computational tool to determine first and second moment of random parameters, the second moment reliability method can be easily combined with PFEM to obtain measures of the reliability of the structural system. The method is also being applied to fatigue crack growth. Uncertainties in the material properties of advanced materials such as polycrystalline alloys, ceramics, and composites are commonly observed from experimental tests. This is mainly attributed to intrinsic microcracks, which are randomly distributed as a result of the applied load and the residual stress.

Liu, W. K.↗

NASA NTRS1972

Path selection system simulation and evaluation for a Martian roving vehicle

The simulation and evaluation of proposed path selection systems for an autonomous Martian roving vehicle was developed. The package incorporates a number of realistic features, such as the simulation of random effects due to vehicle bounce and sensor-reading uncertainty, to increase the reliability of the results. Qualitative and quantitative evaluation criteria were established. The performance of three different path selection systems was evaluated to determine the effectiveness of the simulation package, and to form some preliminary conclusions regarding the tradeoffs involved in a path selection system design.

Boheim, S. L.↗

NASA NTRS1973

Precision selenodesy via differential interferometry.

Review of the first results obtained from the development and use of a new type of differential receiver designed to determine the relative locations on the lunar surface of two Apollo-deposited transmitters. This receiver has made it possible to reduce random and systematic instrumental errors to nearly negligible levels (displacement uncertainties equivalent to a few centimeters).

Counselman, C. C., III↗

NASA NTRS1982

A technique for measuring magnetic fields on solar-type stars

A method is developed and tested by which magnetic-field strengths and area filling-factors of magnetic regions on late-type stars may be inferred from high-resolution, absorption-line profiles that have been broadened by the Zeeman effect. The technique involves fitting such profiles with a triplet of components, the shape of which is derived from profiles of lines of low Zeeman sensitivity. Tests of the systematic and random errors indicate that such magnetic flux measurements have an uncertainty of 20% for stars with field strengths of 2000 gauss if at least 10% of the stellar surface contains magnetic regions.

Marcy, G. W.↗

NASA NTRS2010

NASA's New Orbital Debris Engineering Model, ORDEM2010

This paper describes the functionality and use of ORDEM2010, which replaces ORDEM2000, as the NASA Orbital Debris Program Office (ODPO) debris engineering model. Like its predecessor, ORDEM2010 serves the ODPO mission of providing spacecraft designers/operators and debris observers with a publicly available model to calculate orbital debris flux by current-state-of-knowledge methods. The key advance in ORDEM2010 is the input file structure of the yearly debris populations from 1995-2035 of sizes 10 micron - 1 m. These files include debris from low-Earth orbits (LEO) through geosynchronous orbits (GEO). Stable orbital elements (i.e., those that do not randomize on a sub-year timescale) are included in the files as are debris size, debris number, material density, random error and population error. Material density is implemented from ground-test data into the NASA breakup model and assigned to debris fragments accordingly. The random and population errors are due to machine error and uncertainties in debris sizes. These high-fidelity population files call for a much higher-level model analysis than what was possible with the populations of ORDEM2000. Population analysis in the ORDEM2010 model consists of mapping matrices that convert the debris population elements to debris fluxes. One output mode results in a spacecraft encompassing 3-D igloo of debris flux, compartmentalized by debris size, velocity, pitch, and yaw with respect to spacecraft ram direction. The second output mode provides debris flux through an Earth-based telescope/radar beam from LEO through GEO. This paper compares the new ORDEM2010 with ORDEM2000 in terms of processes and results with examples of specific orbits.

Krisko, Paula H.↗

NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗