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At least 55 records · Page 3

Precision X-band radio Doppler and ranging navigation: Mars Observer interplanetary cruise scenario

This article describes an error covariance analysis based on a Mars Observer mission scenario; the study was performed to establish the navigation performance that can potentially be achieved in a demonstration of precision two-way X-band (8.4-GHz) Doppler and ranging with the Mars Observer spacecraft planned for next year, and to evaluate the sensitivity of the predicted performance to variations in ground system error modeling assumptions. Orbit determination error statistics computed for a 182-day Doppler and ranging data arc predicted Mars approach orbit determination accuracies of about 0.45 micro-rad in an angular sense, using a conservative ground system error model as a baseline. When less-conservative error model assumptions were employed, it was found that orbit determination accuracies of 0.19 to 0.30 micro-rad could be obtained; the level of accuracy of the assumed Mars ephemeris is about 0.11 micro-rad. In comparison, Doppler-only performance with the baseline error model was predicted to be about 1.30 to 1.51 micro-rad, although it was found that when improved station location accuracies and Global Positioning System-based tropospheric calibration accuracies were assumed, accuracies of 0.44 to 0.52 micro-rad were predicted. In the Doppler plus ranging cases, the results were relatively insensitive to variations in ranging system and station delay calibration uncertainties of a few meters and tropospheric zenith delay calibration uncertainties of a few centimeters.

Estefan, J. A.↗

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗

HST Multicolor (255-1042 nm) Photometry of Saturn's Main Rings: Radial Profiles, Phase and Opening Angle Variations, and Regional Spectra - 1

The main rings of Saturn were observed with the Planetary Camera of the WFPC2 instrument on the Hubble Space Telescope (HST) from September 1996 to August 2000 as the'ring opening angle to Earth and Sun increased from 4 deg to 24 deg, with a spread of phase angles between 0.3 deg and 6 deg at each opening angle. The rings were routinely observed in the five HST wideband UBVRI filters (F336W, F439W, F555W, F675W, and F814W) and occasionally in the F255W, F785LP, and F1042M filters. The emphasis in this series of papers will be on radial color (implying compositional) variations. In this first paper we describe the analysis technique and calibration procedure, note revisions in a previously published Voyager ring color data analysis, and present new results based on over 100 HST images. In the 300-600 nm spectral range where the rings are red, the 555nm/336nm ratio increases by about 14% as the phase angle increases from 0.3 deg to 6 deg. This effect, never reported previously for the rings, is significantly larger than the phase reddening which characterizes other icy objects, primarily because of the redness of the rings. However, there is no discernible tendency for color to vary with ring opening angle at a given phase angle, and there is no phase variation of color where the spectrum is flat. We infer from this combination of facts that multiple intraparticle scattering, either in a regolith or between facets of an unusually rough surface, is important in these geometries, but that multiple interparticle scattering in a vertically extended layer is not. Voyager color ratios at a phase angle of 14 deg are compatible with this trend, but calibration uncertainties prevent their use in quantitative modeling. Overall ring-average spectra are compatible with those of earlier work within calibration uncertainties, but ring spectra vary noticeably with region. We refine and subdivide the regions previously defined by others. The variation seen between radial profiles of ratios between different wavelength suggests the presence of multiple compositional components with different radial distributions. We present new radial profiles of far UV color ratio (F336W/F255W) showing, substantial global variations having a different radial structure than seen between 555 and 336nm. We also find evidence for absorption in the 850nm spectral range (a feature previously only weakly indicated in ring-averaged spectra) primarily through its radial variation; it is located primarily in the C ring, where the particles are known to have lower albedo, and is consistent with "interplanetary pollution" of the rings.

Cuzzi, Jeffrey N.↗

Massively Parallel Bayesian Model Calibration and Uncertainty Quantification with Applications to Nuclear Fuels and Materials

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗

BEYONDPLANCK X. Planck Low Frequency Instrument frequency maps with sample-based error propagation

We present Planck Low Frequency Instrument (LFI) frequency sky maps derived within the BEYONDPLANCK framework. This framework draws samples from a global posterior distribution that includes instrumental, astrophysical, and cosmological parameters, and the main product is an entire ensemble of frequency sky map samples, each of which corresponds to one possible realization of the various modeled instrumental systematic corrections, including correlated noise, time-variable gain, as well as far sidelobe and bandpass corrections. This ensemble allows for computationally convenient end-to-end propagation of low-level instrumental uncertainties into higher-level science products, including astrophysical component maps, angular power spectra, and cosmological parameters. We show that the two dominant sources of LFI instrumental systematic uncertainties are correlated noise and gain fluctuations, and the products presented here support – for the first time – full Bayesian error propagation for these effects at full angular resolution. We compared our posterior mean maps with traditional frequency maps delivered by the Planck Collaboration, and find generally good agreement. The most important quality improvement is due to significantly lower calibration uncertainties in the new processing, as we find a fractional absolute calibration uncertainty at 70 GHz of Δg 0 /g 0 = 5 x 10 -5 , which is nominally 40 times smaller than that reported by Planck 2018. However, we also note that the original Planck 2018 estimate has a nontrivial statistical interpretation, and this further illustrates the advantage of the new framework in terms of producing self-consistent and well-defined error estimates of all involved quantities without the need of ad hoc uncertainty contributions. We describe how low-resolution data products, including dense pixel-pixel covariance matrices, may be produced from the posterior samples directly, without the need for computationally expensive analytic calculations or simulations. We conclude that posterior-based frequency map sampling provides unique capabilities in terms of low-level systematics modeling and error propagation, and may play an important role for future Cosmic Microwave Background (CMB) B-mode experiments aiming at nanokelvin precision.

79 ASTRONOMY AND ASTROPHYSICS↗

Sensitivity of satellite-derived net shortwave irradiance at the Earth's surface to radiometric calibration

The effect of radiometric calibration uncertainties on satellite-derived net shortwave irradiance at the Earth's surface was examined. Net shortwave irradiance sensitivity to calibration is expressed as a function of two basic components that depend on surface and cloud albedo sensitivities, respectively. The analysis of these sensitivities for a wide range of atmospheric and surface conditions, as well as radiation geometries, shows that a 10 percent uncertainty in the calibration induces up to 40 W/sqm errors in instantaneous net shortwave irradiance (negative when the calibration uncertainty is positive). The maximum relative errors are obtained in overcast conditions when cloud albedos are high. On a monthly time scale, the induced error becomes typically 13 W/sqm in the tropics and 16 W/sqm in higher latitude regions during summer. The error almost vanishes at high latitudes during winter. A 10 percent positive uncertainty in the calibration gives a net shortwave irradiance error similar to that induced by the 3 hr sampling of the ISCCP Project.

Gautier, C.↗

Frequency-Selectable Laser Source (FLS) Calibrator for CMB Bandpass Characterization

One of the biggest challenges for Cosmic Microwave Background (CMB) experiments comes from our detector bandpass calibration. Uncertainties in bandpass can severely limit our measurements by limiting foreground removal and spectral fitting, which is particularly important for high-$\ell$ observations like cluster science using the Sunyaev-Zeldovich (SZ) effect. Currently, CMB experiments typically use a Fourier Transform Spectrometer (FTS) to measure the detector bandpasses. However, the resolution of the FTS is dependent on the length of the interferometer arms, leading to a need for increasingly large FTS instruments as CMB experiments require tighter constraints on detector bandpasses. Additionally, systematic effects like shifts in bandpass shape from uneven illumination from the FTS further limit the calibration uncertainties. As a complement to the FTS, we have developed a Frequency-selectable Laser Source (FLS) calibrator, which uses a laser with adjustable frequency housed in a calibrator that allows for varying degrees of laser power attenuation. We present several tests used to characterize the first prototype design of the FLS calibrator, as well as the improvements to the calibrator design currently underway.

Saunders, Lauren [Fermilab]↗

The OLI Radiometric Scale Realization Round Robin Measurement Campaign

A round robin radiometric scale realization was performed at the Ball Aerospace Radiometric Calibration Laboratory in January/February 2011 in support of the Operational Land Imager (OLI) Program. Participants included Ball Aerospace, NIST, NASA Goddard Space Flight Center, and the University of Arizona. The eight day campaign included multiple observations of three integrating sphere sources by nine radiometers. The objective of the campaign was to validate the radiance calibration uncertainty ascribed to the integrating sphere used to calibrate the OLI instrument. The instrument level calibration source uncertainty was validated by quatnifying: (1) the long term stability of the NIST calibrated radiance artifact, (2) the responsivity scale of the Ball Aerospace transfer radiometer and (3) the operational characteristics of the large integrating sphere.

Cutlip, Hansford↗

Effect of Correlated Precision Errors on Uncertainty of a Subsonic Venturi Calibration

An uncertainty analysis performed in conjunction with the calibration of a subsonic venturi for use in a turbine test facility produced some unanticipated results that may have a significant impact in a variety of test situations. Precision uncertainty estimates using the preferred propagation techniques in the applicable American National Standards Institute/American Society of Mechanical Engineers standards were an order of magnitude larger than precision uncertainty estimates calculated directly from a sample of results (discharge coefficient) obtained at the same experimental set point. The differences were attributable to the effect of correlated precision errors, which previously have been considered negligible. An analysis explaining this phenomenon is presented. The article is not meant to document the venturi calibration, but rather to give a real example of results where correlated precision terms are important. The significance of the correlated precision terms could apply to many test situations.

Hudson, S. T.↗

Emulation With Uncertainty Quantification of Regional Sea‐Level Change Caused by the Antarctic Ice Sheet

Abstract Projecting regional sea‐level change under various climate‐change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice‐mass change, which requires substantial computational cost if applied to probabilistic frameworks requiring thousands to millions of samples. Here we build emulators of regional sea‐level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are evaluated against a numerical sea‐level model applied to an ensemble of ice‐sheet model simulations of the Antarctic Ice Sheet through 2100. We build a physics‐based emulator using a recent sensitivity kernel approach and compare it to machine learning based emulators (neural network and conditional variational autoencoder methods). In order to quantify uncertainty, we derive well‐calibrated prediction intervals for regional sea‐level change via split‐conformal inference and linear regression, and show that Monte Carlo dropout does not yield well‐calibrated uncertainties in this instance. We also demonstrate substantial gains in computational efficiency using both the physics‐based emulator and neural networks in comparison to the numerical model for the complete regional sea‐level solution. Overall, we find the physics‐based emulator modestly outperforms the machine learning emulators for this problem.

58 GEOSCIENCES↗

Ultraviolet albedo of Comet West /1976 VI/

The nature of the dust scattered radiation shown in the ultraviolet spectrum of Comet West is analyzed. The continuum of solar radiation scattered by cometary dust is detected to wavelengths as short as 2100 A and is found to follow closely the solar spectrum. The flux from the entire visible coma is included in the spectrometer slit. A comparison of the ultraviolet with the visible solar flux gives the ratio of the albedo at 2700 A to that of 5500 A as about 0.3. Even with all the uncertainties included (the absolute calibration uncertainty is plus or minus 15%), the evidence for strong reddening between the visible and the ultraviolet is clear. A further decrease in albedo is found near 2200 A. The value of the visible albedo of 0.2 is used to derive a value of 0.6 for the cometary albedo at 2700 A. This value is similar to that found for the moon and lunar dust in this spectral region.

Feldman, P. D.↗

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits: Validation - Part 2

Consistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target-modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2 percent and 3 percent for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions. The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1 percent for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1 percent and 2 percent for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.

Doelling, David R.↗

Evaluating Statistical Process Control (SPC) techniques and computing the uncertainty of force calibrations

In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.

Navard, Sharon E.↗