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Results for “Uncertainty Propagation”

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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Uncertainty propagation in a stratospheric model. I - Development of a concise stratospheric model. II - Monte Carlo analysis of imprecisions due to reaction rates

The paper discusses the development of a concise stratospheric model which uses iteration to obtain coupling between interacting species. The one-dimensional, steady-state, diurnally-averaged model generates diffusion equations with appropriate sources and sinks for species odd oxygen, H2O, H2, CO, N2O, odd nitrogen, CH4, CH3Cl, CCl4, CF2Cl2, CFCl3, and odd chlorine. The model evaluates steady-state perturbations caused by injections of chlorine and NO(x) and may be used to predict ozone depletion. The model is used in a Monte Carlo study of the propagation of reaction-rate imprecisions by calculating an ozone perturbation caused by the addition of chlorine. Since the model is sensitive to only 10 of the more than 50 reaction rates considered, only about 1000 Monte Carlo cases are required to span the space of possible results.

Rundel, R. D.↗
NASA NTRS2018

Combined Error and Uncertainty Estimates for CFD Problems

Given input sources of uncertainty, non-intrusive uncertainty propagation methods quantify the uncertainty in output quantities of interest (QoI) by performing a nite number of CFD (Computational Fluid Dynamics) instance realizations needed in the calculation of output statistics. It is well known that this introduces multiple sources of error. CFD codes often utilize finite-dimensional approximation (grids, basis functions, etc.) thus incurring CFD numerical errors often approximately reinterpreted as a statistical bias. Uncertainty propagation methods calculate uncertainty statistics for output quantities of interest using a numerical method (e.g. deterministic quadrature, sampling, etc.) thus incurring UQ (Uncertainty Quantification) numerical errors. Importance of quantifying these errors in large scale scientific computing: How accurate is an output statistic?; How should additional computational resources be invested to further reduce the error in a statistic?

Posteriori↗
NASA NTRSDate not supplied

TPSAS-NF1676L-12725-DND

Uncertainty analysis and robust design - increase confidence and consistency in aerospace vehicle safety predictions by developing improved methods for quantifying and managing uncertainty. Quantifying: uncertainty modeling (model uncertainty based on experimental data, simulations and/or expert opinion) and uncertainty propagation (given uncertainty models of a system’s inputs, how to propagate them through system models, to efficiently evaluate the corresponding system’s outputs?). Managing: robust design (generate designs that robustly accommodate uncertainty) and uncertainty decomposition (identify uncertainties that contribute the most to performance degradation and determine the parameters that should (not) be modeled as uncertain).

Sean P Kenny↗
NASA NTRS1995

Impact of uncertainty on modeling and testing

A thorough understanding of the uncertainties associated with the modeling and testing of the Space Shuttle Main Engine (SSME) Engine will greatly aid decisions concerning hardware performance and future development efforts. This report will describe the determination of the uncertainties in the modeling and testing of the Space Shuttle Main Engine test program at the Technology Test Bed facility at Marshall Space Flight Center. Section 2 will present a summary of the uncertainty analysis methodology used and discuss the specific applications to the TTB SSME test program. Section 3 will discuss the application of the uncertainty analysis to the test program and the results obtained. Section 4 presents the results of the analysis of the SSME modeling effort from an uncertainty analysis point of view. The appendices at the end of the report contain a significant amount of information relative to the analysis, including discussions of venturi flowmeter data reduction and uncertainty propagation, bias uncertainty documentations, technical papers published, the computer code generated to determine the venturi uncertainties, and the venturi data and results used in the analysis.

Coleman, Hugh W.↗
NASA NTRS2018

Realistic Covariance Generation for the GPM Spacecraft

A covariance realism process for NASA's Global Precipitation Measurement (GPM) spacecraft is detailed. The GPM spacecraft is in a low earth orbit, and performs collision avoidance maneuvers few times a year. Currently GPM is below the International Space Station (ISS). So, in addition to cataloged debris objects, GPM must contend with smallsat/cubesat objects that are deployed from the ISS. Both operational scenarios require complete knowledge of the expected GPM prediction errors as a function of time. In this study, we present a method for generating realistic predicted covariance that uses linear propagation of the covariance with the addition of process noise. Further analyses are presented for the process noise ''tuning'' that generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Different tuning strategies are considered and compared via a Goodness-of-Fit testing for the Gaussian properties of the scaled covariance. SpaceNav's realistic covariance generation approach takes into account the contribution of predicted maneuver errors in the increased propagation uncertainty. Corresponding maneuver uncertainty is injected into the state uncertainty, and is used within the collision avoidance process to determine the collision risk for close approach events that follow a maneuver. This is a critical step in the maneuver planning process that provides the satellite operator with an accurate quantification of the collision probability for planned maneuvers. Using this information, an informed decision can be made to proceed with a maneuver if the collision risk is acceptable. This approach is validated by Monte-Carlo simulations and results are presented.

spacecraft uncertainty propagation↗
NASA NTRS1972

Derivation of mission fluences from flux models: Propagation of uncertainties

The calculation of fluences from the flux models for spacecraft designers, and the propagation of uncertainties in the flux models to larger uncertainties in the final answer are discussed. Ionization and displacement damage in silicon, fluence, and a fluence of an equivalent energy are considered. Data in graph form are presented on stopping power and range curves for electrons and protons in silicon, relative electron and proton damage in silicon, and electron fluences expressed as 3 MeV equivalent and proton fluences expressed as 20 MeV equivalent for flybys in Jupiter's magnetic equatorial plane. It is concluded that the problem includes the uncertainty in peak flux, which has an immediate linear effect; uncertainty in the spatial distribution of the flux which gives great divergency between the upper-limit model and the nominal model and in the case of protons, may preclude any mission planning in order to make the situation better; and the uncertainty in the energy spectrum both in shape and energy location of the peak, if any, and which has little importance for mission planning if the spectrum is fairly constant.

Barengoltz, J.↗
NASA NTRS1997

Doppler Global Velocimeter Development for the Large Wind Tunnels at Ames Research Center

Development of an optical, laser-based flow-field measurement technique for large wind tunnels is described. The technique uses laser sheet illumination and charged coupled device detectors to rapidly measure flow-field velocity distributions over large planar regions of the flow. Sample measurements are presented that illustrate the capability of the technique. An analysis of measurement uncertainty, which focuses on the random component of uncertainty, shows that precision uncertainty is not dependent on the measured velocity magnitude. For a single-image measurement, the analysis predicts a precision uncertainty of +/-5 m/s. When multiple images are averaged, this uncertainty is shown to decrease. For an average of 100 images, for example, the analysis shows that a precision uncertainty of +/-0.5 m/s can be expected. Sample applications show that vectors aligned with an orthogonal coordinate system are difficult to measure directly. An algebraic transformation is presented which converts measured vectors to the desired orthogonal components. Uncertainty propagation is then used to show how the uncertainty propagates from the direct measurements to the orthogonal components. For a typical forward-scatter viewing geometry, the propagation analysis predicts precision uncertainties of +/-4, +/-7, and +/-6 m/s, respectively, for the U, V, and W components at 68% confidence.

Reinath, Michael S.↗
NASA NTRS2022

MODIS TEB Electronic Crosstalk Correction Update and Impact on L1B Product Uncertainty

The MODIS instruments onboard the Terra and Aqua satellites have been in operation for over22 and 20 years, respectively. The instruments’ calibration accuracy has been maintained, even with instrument degradation. Electronic crosstalk in the thermal emissive bands (TEB) is a known issue with an increasing impact on the calibration and product. The Terra MODIS photovoltaic (PV) longwave infrared (LWIR) bands crosstalk corrections have been applied in Collection 6.1 (C6.1). However, the electronic crosstalk contamination for some detectors in the mid-wave infrared (MWIR) bands and the Aqua PVLWIR bands affect the Level-1B (L1B) product’s measurement accuracy and image quality. In Collection 7 (C7), crosstalk corrections for select detectors in the Terra and Aqua MWIR and Aqua PV LWIR bands are applied. The entire mission crosstalk coefficients for the select detectors and bands are derived from scheduled lunar observations and populated in the form of look-up tables (LUTs). The Aqua PV-LWIR bands exhibit similar downward crosstalk trends as the Terra PV-LWIR bands, especially in recent years. The crosstalk coefficients and their trends provide a guideline for the correction application. Earth measurement analyses before and after the correction provide contamination and correction assessments. It has been shown that the product quality is enhanced with the crosstalk correction applied in C7. For C7, the crosstalk coefficient uncertainty is derived from the fit residuals between the measured values and a linear fit over a three-year sliding window. The uncertainty propagation is modeled and applied in the total uncertainty calculation intheL1B product. The TEB electronic crosstalk LUTs have been processed over the entire Terra and Aqua MODIS missions. This paper presents the C7 crosstalk correction, as well as its assessment and uncertainty propagation algorithm to the TEB uncertainty.

Tiejun Chang↗
NASA NTRS2022

Modis TEB Electronic Crosstalk Correction Update and Impact on L1B Product Uncertainty

The MODIS instruments on board the Terra and Aqua satellites have been in operation for over 22 and 20 years, respectively. The instruments’ calibration accuracy has been maintained, even with instrument degradation. Electronic crosstalk in the thermal emissive bands (TEB) is a known issue with an increasing impact on the calibration and product. The Terra MODIS photovoltaic (PV) longwave infrared (LWIR) bands crosstalk corrections have been applied in Collection 6.1 (C6.1). However, the electronic crosstalk contamination for some detectors in the mid-wave infrared(MWIR) bands and the Aqua PVLWIR bands affect the Level-1B(L1B) product’s measurement accuracy and image quality. In Collection 7 (C7), crosstalk corrections for select detectors in the Terra and Aqua MWIR and Aqua PV LWIR bands are applied. The entire mission crosstalk coefficients for the select detectors and bands are derived from scheduled lunar observations and populated in the form of look-up tables (LUTs). The Aqua PV-LWIR bands exhibit similar downward crosstalk trends as the Terra PV-LWIR bands, especially in recent years. The crosstalk coefficients and their trends provide a guideline for the correction application. Earth measurement analyses before and after the correction provide contamination and correction assessments. It has been shown that the product quality is enhanced with the crosstalk correction applied in C7. For C7, the crosstalk coefficient uncertainty is derived from the fit residuals between the measured values and a linear fit over a three-year sliding window. The uncertainty propagation is modeled and applied in the total uncertainty calculation intheL1B product. The TEB electronic cross talk LUT shave been processed over the entire Terra and Aqua MODIS missions. This paper presents the C7 crosstalk correction, as well as its assessment and uncertainty propagation algorithm to the TEB uncertainty.

Tiejun Chang↗
NASA NTRS2010

A Framework for Propagation of Uncertainties in the Kepler Data Analysis Pipeline

The Kepler space telescope is designed to detect Earth-like planets around Sun-like stars using transit photometry by simultaneously observing 100,000 stellar targets nearly continuously over a three and a half year period. The 96-megapixel focal plane consists of 42 charge-coupled devices (CCD) each containing two 1024 x 1100 pixel arrays. Cross-correlations between calibrated pixels are introduced by common calibrations performed on each CCD requiring downstream data products access to the calibrated pixel covariance matrix in order to properly estimate uncertainties. The prohibitively large covariance matrices corresponding to the ~75,000 calibrated pixels per CCD preclude calculating and storing the covariance in standard lock-step fashion. We present a novel framework used to implement standard propagation of uncertainties (POU) in the Kepler Science Operations Center (SOC) data processing pipeline. The POU framework captures the variance of the raw pixel data and the kernel of each subsequent calibration transformation allowing the full covariance matrix of any subset of calibrated pixels to be recalled on-the-fly at any step in the calibration process. Singular value decomposition (SVD) is used to compress and low-pass filter the raw uncertainty data as well as any data dependent kernels. The combination of POU framework and SVD compression provide downstream consumers of the calibrated pixel data access to the full covariance matrix of any subset of the calibrated pixels traceable to pixel level measurement uncertainties without having to store, retrieve and operate on prohibitively large covariance matrices. We describe the POU Framework and SVD compression scheme and its implementation in the Kepler SOC pipeline.

Clarke, Bruce D.↗