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DOE OSTI2012

Atmospheric Dispersion Effects in Weak Lensing Measurements

The wavelength dependence of atmospheric refraction causes elongation of finite-bandwidth images along the elevation vector, which produces spurious signals in weak gravitational lensing shear measurements unless this atmospheric dispersion is calibrated and removed to high precision. Because astrometric solutions and PSF characteristics are typically calibrated from stellar images, differences between the reference stars' spectra and the galaxies' spectra will leave residual errors in both the astrometric positions (dr) and in the second moment (width) of the wavelength-averaged PSF (dv) for galaxies.We estimate the level of dv that will induce spurious weak lensing signals in PSF-corrected galaxy shapes that exceed the statistical errors of the DES and the LSST cosmic-shear experiments. We also estimate the dr signals that will produce unacceptable spurious distortions after stacking of exposures taken at different airmasses and hour angles. We also calculate the errors in the griz bands, and find that dispersion systematics, uncorrected, are up to 6 and 2 times larger in g and r bands,respectively, than the requirements for the DES error budget, but can be safely ignored in i and z bands. For the LSST requirements, the factors are about 30, 10, and 3 in g, r, and i bands,respectively. We find that a simple correction linear in galaxy color is accurate enough to reduce dispersion shear systematics to insignificant levels in the r band for DES and i band for LSST,but still as much as 5 times than the requirements for LSST r-band observations. More complex corrections will likely be able to reduce the systematic cosmic-shear errors below statistical errors for LSST r band. But g-band effects remain large enough that it seems likely that induced systematics will dominate the statistical errors of both surveys, and cosmic-shear measurements should rely on the redder bands.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

Engineering↗

NASA NTRS1989

Lifetime predictions for the Solar Maximum Mission (SMM) and San Marco spacecraft

Lifetime prediction techniques developed by the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) are described. These techniques were developed to predict the Solar Maximum Mission (SMM) spacecraft orbit, which is decaying due to atmospheric drag, with reentry predicted to occur before the end of 1989. Lifetime predictions were also performed for the Long Duration Exposure Facility (LDEF), which was deployed on the 1984 SMM repair mission and is scheduled for retrieval on another Space Transportation System (STS) mission later this year. Concepts used in the lifetime predictions were tested on the San Marco spacecraft, which reentered the Earth's atmosphere on December 6, 1988. Ephemerides predicting the orbit evolution of the San Marco spacecraft until reentry were generated over the final 90 days of the mission when the altitude was less than 380 kilometers. The errors in the predicted ephemerides are due to errors in the prediction of atmospheric density variations over the lifetime of the satellite. To model the time dependence of the atmospheric densities, predictions of the solar flux at the 10.7-centimeter wavelength were used in conjunction with Harris-Priester (HP) atmospheric density tables. Orbital state vectors, together with the spacecraft mass and area, are used as input to the Goddard Trajectory Determination System (GTDS). Propagations proceed in monthly segments, with the nominal atmospheric drag model scaled for each month according to the predicted monthly average value of F10.7. Calibration propagations are performed over a period of known orbital decay to obtain the effective ballistic coefficient. Progagations using plus or minus 2 sigma solar flux predictions are also generated to estimate the despersion in expected reentry dates. Definitive orbits are compared with these predictions as time expases. As updated vectors are received, these are also propagated to reentryto continually update the lifetime predictions.

Smith, E. A.↗

NASA NTRS1996

Multipath analysis diffraction calculations

This report describes extensions of the Kirchhoff diffraction equation to higher edge terms and discusses their suitability to model diffraction multipath effects of a small satellite structure. When receiving signals, at a satellite, from the Global Positioning System (GPS), reflected signals from the satellite structure result in multipath errors in the determination of the satellite position. Multipath error can be caused by diffraction of the reflected signals and a method of calculating this diffraction is required when using a facet model of the satellite. Several aspects of the Kirchhoff equation are discussed and numerical examples, in the near and far fields, are shown. The vector form of the extended Kirchhoff equation, by adding the Larmor-Tedone and Kottler edge terms, is given as a mathematical model in an appendix. The Kirchhoff equation was investigated as being easily implemented and of good accuracy in the basic form, especially in phase determination. The basic Kirchhoff can be extended for higher accuracy if desired. A brief discussion of the method of moments and the geometric theory of diffraction is included, but seems to offer no clear advantage in implementation over the Kirchhoff for facet models.

Statham, Richard B.↗

NASA NTRS2023

Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An Assessment of Amospheric Correction Processors for Landsat 8 and Sentinel-2 Over Land

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals.

Atmospheric correction↗

DOE OSTI2014

Improved Modeling Tools Development for High Penetration Solar

One of the significant objectives of the High Penetration solar research is to help the DOE understand, anticipate, and minimize grid operation impacts as more solar resources are added to the electric power system. For Task 2.2, an effective, reliable approach to predicting solar energy availability for energy generation forecasts using the University of California, San Diego (UCSD) Sky Imager technology has been demonstrated. Granular cloud and ramp forecasts for the next 5 to 20 minutes over an area of 10 square miles were developed. Sky images taken every 30 seconds are processed to determine cloud locations and cloud motion vectors yielding future cloud shadow locations respective to distributed generation or utility solar power plants in the area. The performance of the method depends on cloud characteristics. On days with more advective cloud conditions, the developed method outperforms persistence forecasts by up to 30% (based on mean absolute error). On days with dynamic conditions, the method performs worse than persistence. Sky Imagers hold promise for ramp forecasting and ramp mitigation in conjunction with inverter controls and energy storage. The pre-commercial Sky Imager solar forecasting algorithm was documented with licensing information and was a Sunshot website highlight.

14 SOLAR ENERGY↗

DOE OSTI2018

Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.

17 WIND ENERGY↗

NASA NTRS1971

Inertial navigation system using three TDF gyroscopic sensors not jointly mounted on a stable platform

An inertial navigation system is described and analyzed based on two two-degree-of-freedom Schuler-gyropendulums and one two-degree-of-freedom azimuth gyro. The three sensors, each base motion isolated about its two input axes, are mounted on a common base, strapped down to the vehicle. The up and down pointing spin vectors of the two properly tuned gyropendulums track the vertical and indicate physically their velocity with respect to inertial space. The spin vector of the azimuth gyro is pointing northerly parallel to the earth axis. The system can be made self-aligning on a stationary base. If external measurements for the north direction and the vertical are available, initial disturbance torques can be measured and easily biased out. The error analysis shows that the system is practicable with today's technology.

Stieler, B.↗

NASA NTRS1975

Regions of constrained maximum likelihood parameter identifiability

This paper considers the parameter identification problem of general discrete-time, nonlinear, multiple-input/multiple-output dynamic systems with Gaussian-white distributed measurement errors. Knowledge of the system parameterization is assumed to be known. Regions of constrained maximum likelihood (CML) parameter identifiability are established. A computation procedure employing interval arithmetic is proposed for finding explicit regions of parameter identifiability for the case of linear systems. It is shown that if the vector of true parameters is locally CML identifiable, then with probability one, the vector of true parameters is a unique maximal point of the maximum likelihood function in the region of parameter identifiability and the CML estimation sequence will converge to the true parameters.

Lee, C.-H.↗

NASA NTRS2019

The LUVOIR Extreme Coronagraph for Living Planetary Systems (ECLIPS) II. Performance Evaluation, Aberration Sensitivity Analysis and Exoplanet Detection Simulations

Future space missions such as the Large UV-Optical-Infrared Surveyor (LUVOIR) and the Habitable Exoplanet Observatory (HabEx) require coronagraphs with active wavefront control to suppress starlight to discover and characterize habitable exoplanets. The Extreme Coronagraph for Living Planetary Systems (ECLIPS) is the coronagraph instrument on the LUVOIR Surveyor mission concept, an 8–15m segmented telescope. ECLIPS is split into three channels: UV (200 to 400 nm), optical (400 nm to 850 nm), and NIR (850 nm to 2.0 microns), with each channel equipped with two deformable mirrors for wavefront control, a suite of coronagraph masks, a low-order/out-of-band wavefront sensor, and separate science imagers and spectrographs. The Apodized Pupil Lyot Coronagraph (APLC) and the Vector Vortex Coronagraph (VVC) are the baselined mask technologies for ECLIPS to enable the required 10 -10 contrast for observations in the habitable zones of nearby stars. Their performance depends on active wavefront sensing and control, as well as metrology subsystems to compensate for aberrations induced by segment errors (piston and tip/tilt, among others), secondary mirror misalignment, and global low-order wavefront errors. Here we present the latest results of the simulation of these effects for the two technologies and discuss the achieved contrast for exoplanet detection and characterization after closed-loop wavefront estimation and control algorithms have been applied. Finally, we show simulated observations using high-fidelity spatial and spectral input models of complete planetary systems generated with the Haystacks code framework.

Roser Juanola-Parramon↗

DOE OSTI2024

Light Detecting and Ranging (LIDAR) for in-situ heliostat optical error assessment

A system and method for optical assessment of a heliostat includes obtaining a point cloud data representing an image of the heliostat; isolating the data; filtering and fitting the filtered heliostat data to a bounding box; translating the heliostat data to a plane to aid in segmentation; segmenting a plurality of facets of the heliostat fitting each of the segmented facets to a respective plane; generating normal vectors characterizing each of the plurality of facets; and calculating a canting angle associated with each respective facet of the plurality of facets. A heliostat with mirrored facets and a scanner are provided. The scanner captures point cloud data representing the heliostat, which is segmented for each facet. Normal vectors characterize the facets and a canting angle is calculated for the respective facet.

Small, Daniel E.↗

DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗

NASA NTRS1982

MAGSAT and aeromagnetic data of the continental US

The MAGSAT data were used to produce a scalar anomaly map of the U.S. In order to remove the east-west striping anomalies, which are believed to be caused by the low-order polynomials used to reduce the orbital bias errors, a 2-D spectral filtering using the Fourier transform method was applied. The resultant low-pass filtered map, with the east-west stripings removed, resembles much closer the surface aeromagnetic map based on the U.S. MAGNET data. The spectral filtering was also applied to the MAGSAT vector data. Department of Defense gravity data was processed to produce a filtered U.S. gravity map whose spectral contents are comparable to those of the MAGSAT magnetic map.

Won, I. J.↗

NASA NTRS1991

Ada Namelist Package

Ada Namelist Package, developed for Ada programming language, enables calling program to read and write FORTRAN-style namelist files. Features are: handling of any combination of types defined by user; ability to read vectors, matrices, and slices of vectors and matrices; handling of mismatches between variables in namelist file and those in programmed list of namelist variables; and ability to avoid searching entire input file for each variable. Principle benefits derived by user: ability to read and write namelist-readable files, ability to detect most file errors in initialization phase, and organization keeping number of instantiated units to few packages rather than to many subprograms.

Klumpp, Allan R.↗

NASA NTRS2006

Study of Synthetic Vision Systems (SVS) and Velocity-vector Based Command Augmentation System (V-CAS) on Pilot Performance

This study investigated the effects of synthetic vision system (SVS) concepts and advanced flight controls on single pilot performance (SPP). Specifically, we evaluated the benefits and interactions of two levels of terrain portrayal, guidance symbology, and control-system response type on SPP in the context of lower-landing minima (LLM) approaches. Performance measures consisted of flight technical error (FTE) and pilot perceived workload. In this study, pilot rating, control type, and guidance symbology were not found to significantly affect FTE or workload. It is likely that transfer from prior experience, limited scope of the evaluation task, specific implementation limitations, and limited sample size were major factors in obtaining these results.

Liu, Dahai↗

NASA NTRS2006

Study of Synthetic Vision Systems (SVS) and Velocity-vector Based Command Augmentation System (V-CAS) on Pilot Performance

Liu, Dahai↗

NASA NTRS2013

Improving Photometry and Stellar Signal Preservation with Pixel-Level Systematic Error Correction

The Kepler Mission has demonstrated that excellent stellar photometric performance can be achieved using apertures constructed from optimally selected CCD pixels. The clever methods used to correct for systematic errors, while very successful, still have some limitations in their ability to extract long-term trends in stellar flux. They also leave poorly correlated bias sources, such as drifting moiré pattern, uncorrected. We will illustrate several approaches where applying systematic error correction algorithms to the pixel time series, rather than the co-added raw flux time series, provide significant advantages. Examples include, spatially localized determination of time varying moiré pattern biases, greater sensitivity to radiation-induced pixel sensitivity drops (SPSDs), improved precision of co-trending basis vectors (CBV), and a means of distinguishing the stellar variability from co-trending terms even when they are correlated. For the last item, the approach enables physical interpretation of appropriately scaled coefficients derived in the fit of pixel time series to the CBV as linear combinations of various spatial derivatives of the pixel response function (PRF). We demonstrate that the residuals of a fit of soderived pixel coefficients to various PRF-related components can be deterministically interpreted in terms of physically meaningful quantities, such as the component of the stellar flux time series which is correlated with the CBV, as well as, relative pixel gain, proper motion and parallax. The approach also enables us to parameterize and assess the limiting factors in the uncertainties in these quantities.

Kolodzijczak, Jeffrey J.↗

NASA NTRS2017

Fast and Reliable Approximations for Interplanetary Low-Thrust Transfers

A three-step process bridges the gap between lower-fidelity solutions that ignore optimal dynamics and fully optimized solutions that are computationally expensive to generate. First, analytic solutions for transfers with free time and angle characterize the evolution of the shape and orientation of the orbit. Next, optimal control theory supplies the thrust vector with variable specific impulse while satisfying flight time and transfer angle constraints. Transfers with the additional constraint of constant specific impulse then provide a more realistic thruster model for preliminary trade studies. These approximations deliver a hundredfold improvement in run time at the expense of a few percent error in mass.

Landau, Damon↗