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DOE OSTI2024

Expert‐in‐the‐loop design of integral nuclear data experiments

Abstract Nuclear data are fundamental inputs to radiation transport codes used for reactor design and criticality safety. The design of experiments to reduce nuclear data uncertainty has been a challenge for many years, but advances in the sensitivity calculations of radiation transport codes within the last two decades have made optimal experimental design possible. The design of integral nuclear experiments poses numerous challenges not emphasized in classical optimal design, in particular, constrained design spaces (in both a statistical and engineering sense), severely under‐determined systems, and optimality uncertainty. We present a design pipeline to optimize critical experiments that uses constrained Bayesian optimization within an iterative expert‐in‐the‐loop framework. We show a successfully completed experiment campaign designed with this framework that involved two critical configurations and multiple measurements that targeted compensating errors in 239 Pu nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1978

The programming language EFL

EFL is a comprehensive language designed to make it easy to write portable, understandable programs. It provides a rich set of data types and structures, a convenient operator set, and good control flow forms. The lexical form is easy to type and to read. Whenever possible, EFL uses the same forms that Ratfor does; in this sense EFL may be viewed as a superset of Ratfor. EFL is a well-defined language; this distinguishes it from most FORTRAN preprocessors which only add simple flow of control constructs to FORTRAN. The EFL compiler generates (possibly tailored) Standard FORTRAN as its output. EFL should catch and diagnose all syntax errors.

Feldman, S. I.↗

NASA NTRS1982

Interpretation of HCMM images: A regional study

Potential users of HCMM data, especially those with only a cursory background in thermal remote sensing are familiarized with the kinds of information contained in the images that can be extracted with some reliability solely from inspection of such standard products as those generated at NASA/GSFC and now achieved in the National Space Science Data Center. Visual analysis of photoimagery is prone to various misimpressions and outright errors brought on by unawareness of the influence of physical factors as well as by sometimes misleading tonal patterns introduced during photoprocessing. The quantitative approach, which relies on computer processing of digital HCMM data, field measurements, and integration of rigorous mathematical models, can usually be used to identify, compensate for, or correct the contributions from at least some of the natural factors and those associated with photoprocessing. Color composite, day-IR, night-IR and visible images of California and Nevada are examined.

Source record↗

NASA NTRS1991

Refinement of ground reference data with segmented image data

One of the ways to determine ground reference data (GRD) for satellite remote sensing data is to photo-interpret low altitude aerial photographs and then digitize the cover types on a digitized tablet and register them to 7.5 minute U.S.G.S. maps (that were themselves digitized). The resulting GRD can be registered to the satellite image or, vice versa. Unfortunately, there are many opportunities for error when using digitizing tablet and the resolution of the edges for the GRD depends on the spacing of the points selected on the digitizing tablet. One of the consequences of this is that when overlaid on the image, errors and missed detail in the GRD become evident. An approach is discussed for correcting these errors and adding detail to the GRD through the use of a highly interactive, visually oriented process. This process involves the use of overlaid visual displays of the satellite image data, the GRD, and a segmentation of the satellite image data. Several prototype programs were implemented which provide means of taking a segmented image and using the edges from the reference data to mask out these segment edges that are beyond a certain distance from the reference data edges. Then using the reference data edges as a guide, those segment edges that remain and that are judged not to be image versions of the reference edges are manually marked and removed. The prototype programs that were developed and the algorithmic refinements that facilitate execution of this task are described.

Robinson, Jon W.↗

NASA NTRS1994

TOPEX/POSEIDON tides estimated using a global inverse model

Altimetric data from the TOPEX/POSEIDON mission will be used for studies of global ocean circulation and marine geophysics. However, it is first necessary to remove the ocean tides, which are aliased in the raw data. The tides are constrained by the two distinct types of information: the hydrodynamic equations which the tidal fields of elevations and velocities must satisfy, and direct observational data from tide gauges and satellite altimetry. Here we develop and apply a generalized inverse method, which allows us to combine rationally all of this information into global tidal fields best fitting both the data and the dynamics, in a least squares sense. The resulting inverse solution is a sum of the direct solution to the astronomically forced Laplace tidal equations and a linear combination of the representers for the data functionals. The representer functions (one for each datum) are determined by the dynamical equations, and by our prior estimates of the statistics or errors in these equations. Our major task is a direct numerical calculation of these representers. This task is computationally intensive, but well suited to massively parallel processing. By calculating the representers we reduce the full (infinite dimensional) problem to a relatively low-dimensional problem at the outset, allowing full control over the conditioning and hence the stability of the inverse solution. With the representers calculated we can easily update our model as additional TOPEX/POSEIDON data become available. As an initial illustration we invert harmonic constants from a set of 80 open-ocean tide gauges. We then present a practical scheme for direct inversion of TOPEX/POSEIDON crossover data. We apply this method to 38 cycles of geophysical data records (GDR) data, computing preliminary global estimates of the four principal tidal constituents, M(sub 2), S(sub 2), K(sub 1) and O(sub 1). The inverse solution yields tidal fields which are simultaneously smoother, and in better agreement with altimetric and ground truth data, than previously proposed tidal models. Relative to the 'default' tidal corrections provided with the TOPEX/POSEIDON GDR, the inverse solution reduces crossover difference variances significantly (approximately 20-30%), even though only a small number of free parameters (approximately equal to 1000) are actually fit to the crossover data.

Egbert, Gary D.↗

NASA NTRS2013

Mars Science Laboratory Frame Manager for Centralized Frame Tree Database and Target Pointing

The FM (Frame Manager) flight software module is responsible for maintaining the frame tree database containing coordinate transforms between frames. The frame tree is a proper tree structure of directed links, consisting of surface and rover subtrees. Actual frame transforms are updated by their owner. FM updates site and saved frames for the surface tree. As the rover drives to a new area, a new site frame with an incremented site index can be created. Several clients including ARM and RSM (Remote Sensing Mast) update their related rover frames that they own. Through the onboard centralized FM frame tree database, client modules can query transforms between any two frames. Important applications include target image pointing for RSM-mounted cameras and frame-referenced arm moves. The use of frame tree eliminates cumbersome, error-prone calculations of coordinate entries for commands and thus simplifies flight operations significantly.

quaternion↗

NASA NTRS2015

Airborne Lidar-Based Estimates of Tropical Forest Structure in Complex Terrain: Opportunities and Trade-Offs for REDD+

Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. Results: We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (approx. 20 returns/sq m) data was highly accurate (mean signed error of 0.19 +/-0.97 m), while those derived from reduced-density datasets (8/sq m, 4/sq m, 2/sq m and 1/sq m) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4/sq m, the bias in height estimates translated into errors of 80-125 Mg/ha in predicted aboveground biomass. Conclusions: Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Airborne lidar↗

DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.

17 WIND ENERGY↗

NASA NTRS2005

Spaceborne Doppler Radar Measurements of Rainfall: Correction of Errors Induced by Pointing Uncertainties

In this paper a sea surface radar echo spectral analysis technique to correct for the rainfall velocity error caused by radar-pointing uncertainty is presented. The correction procedure is quite straightforward when the radar is observing a homogeneous rainfall field. When nonuniform beam filling (NUBF) occurs and attenuating frequencies are used, however, additional steps are necessary in order to correctly estimate the antenna-pointing direction. This new technique relies on the application of the combined frequency-time (CFT) algorithm to correct for uneven attenuation effects on the observed sea surface Doppler spectrum. The performance of this correction technique was evaluated by a Monte Carlo simulation of the Doppler precipitation radar backscatter from high-resolution 3D rain fields (either generated by a cloud resolving numerical model or retrieved from airborne radar measurements). The results show that the antenna-pointing-induced error can, indeed, be reduced by the proposed technique in order to achieve 1 m s(exp -1) accuracy on rainfall vertical velocity estimates.

remote sensing↗

NASA NTRS2016

Applications of Displacement Transfer Functions to Deformed Shape Predictions of the GIII Swept-Wing Structure

The displacement transfer functions (DTFs) were applied to the GIII swept wing for the deformed shape prediction. The calculated deformed shapes are very close to the correlated finite element results as well as the measured data. The convergence study showed that using 17 strain stations, the wing-tip displacement prediction error was 1.6 percent, and that there is no need to use a large number of strain stations for G-III wing shape predictions.

on board string sensing system↗

NASA NTRS2017

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Arctic↗

DOE OSTI2019

LLOCUS: Learning-based Localization Using crowdSourcing

We propose a novel learning-based system, LLOCUS, enabled by crowdsourcing on mobile devices, for localizing mobile transmitters with unknown transmit power. Our approach carefully tackles several challenges in learning and localizing using a crowdsourced system with both ends’ mobility, the ‘ends’ being the transmitters and the receivers. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the transmit power of an unknown transmitter, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the transmit power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using four experiments, in different indoor and outdoor environments, with the size of the areas ranging from 225 to 5500 square meters. We find that LLOCUS reduces the localization error by 17-47%, over a number of non-learning methods. LLOCUS also estimates the transmit power of an unknown transmitter with average error in the range of 3-5 dB, across our experiments.

42 ENGINEERING↗

DOE OSTI2020

Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. Here, satellite and ground-based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground-based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground-based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems

In this paper, a novel control algorithm is presented to enhance the performance of tracking property for a class of non-linear dynamic stochastic systems with unmeasurable variables. To minimize the entropy of tracking errors without changing the existing closed loop with PI controller, the enhanced performance loop is constructed based on the state estimation by extended Kalman Filter and the new controller is designed by full state feedback following this presented control algorithm. Besides, the conditions are obtained for the stability analysis in the mean square sense. In the end, the comparative simulation results are given to illustrate the effectiveness of proposed control algorithm.

Complex industrial process↗

NASA NTRS1981

Test progress on the electrostatic membrane reflector

NASA is currently developing a low mass antenna which derives its reflector surface quality from the application of electrostatic forces to form a thin membrane into the desired concave reflector surface. The shuttle-deployed antenna would have a diameter of 100 m and an RMS surface smoothness of 10 to 1 mm for operation at 1 to 10 GHz. Surface quality measurements have been made on a highly deformable elastic membrane, pressurized by electrostatic forces. Included are the effects of the perimeter boundary, splicing of the membrane, the long-scale smoothness of commercial membranes, and the spatial controllability of the membrane using voltage adjustments to alter the electrostatic forces. The electrostatic membrane was found to operate well in an open-loop sense, showing a high degree of position stability and negligible power consumption in dry air. Visco-electric creep was not evident, but the polymer membrane did expand and contract considerably due to its hygroscopic expansion coefficient. A residual roughness of about 0.75 mm existed with the polymer used in these tests; this error is attributed to seams and membrane anisotropy where the material is stiffer in one direction.

Goslee, J. W.↗

NASA NTRS1992

Short-arc orbit determination using coherent X-band ranging data

The use of X-band frequencies in ground-spacecraft and spacecraft-ground telecommunication links for current and future robotic interplanetary missions makes it possible to perform ranging measurements of greater accuracy than previously obtained. It is shown that ranging data of sufficient accuracy, when acquired from multiple stations, can sense the geocentric angular position of a distant spacecraft. The application of high-accuracy S/X-band and X-band ranging to orbit determination with relatively short data arcs is investigated in planetary approach and encounter scenarios. Actual trajectory solutions for the Ulysses spacecraft constructed from S/X-band ranging and Doppler data are presented; error covariance calculations are used to predict the performance of X-band ranging and Doppler data. The Ulysses trajectory solutions indicate that the aim point for the spacecraft's February 1992 Jupiter encounter was predicted to a geocentric accuracy of 0.20 to 0.23/microrad. Explicit modeling of range bias parameters for each station pass is shown to largely remove systematic ground system calibration errors and transmission media effects from the Ulysses range measurements, which would otherwise corrupt the angle finding capabilities of the data. The Ulysses solutions were found to be reasonably consistent with the theoretical results, which suggest that angular accuracies of 0.08 to 0.1/microrad are achievable with X-band ranging.

Thurman, S. W.↗

NASA NTRS1993

Atmospheric moisture fields derived by satellite observations over the tropical Pacific Ocean

Values of precipitable water are retrieved over the tropical and subtropical Pacific Ocean from TOVS infrared and microwave channel brightness temperature and OLR observations by means of stepwise linear regression. The most useful temperature and moisture sensing channels are pre-selected from sensitivity tests of a radiative transfer model. Numerous models are developed and tested against collocated radiosonde observations and Nimbus-7 SMMR precipitable water estimates. For RAOB comparisons, the best estimator used 15 TOVS predictors and captured 71.1 deg percent of the variance (+0.62 g/sq cm standard error) for column precipitable water; for precipitable water of 700-500 mb bulk layer, these values were 71.7 percent and +/- 0.17 g/sq cm. Little skill of estimated precipitable water was obtained for moisture above 500 mb. Regressions were less skillful against SMMR, unless collocation parameters were tightly controlled; SMMR was less acceptable than RAOB's because of observational drift and errors. Generally, the most skillful predictors were boundary layer brightness temperatures of TOVS channels and satellite estimated stability indices. 'Moisture channels' were hardly useful except for estimating middle and upper tropospheric moisture. Additional regression models were constructed testing the sensitivity to different observational and meteorological characteristics. Models which used some in situ observations surface observations or stabilities calculated from RAOB, were the most successful. The best of these explained 87.5 percent of the variance but the regression selected almost no TOVS channels, relying instead on conventional RAOB and surface observations. A set of four regression models were developed, stratifying atmospheric characteristics on the basis of collocated OLR values. These models improved the variance explained by 5.0 percent; the model associated with the highest OLR values (275 W/sq m less than or equal to OLR; that is, no cloud) showed only marginal skill. Precipitable water fields were generated from the best TOVS-only model for seven days in January 1983 and compared with SMMR-estimated fields. OLR fields and ECMWF precipitable water analysis. The TOVS regression model compared favorably to the SMMR analysis in amplitudes and features. It revealed more evolving synoptic signal than the ECMWF analysis. In synoptically active regions, it differed with respect to the OLR analysis, primarily because of actual differences in the vertical distribution of water vapor.

Chung, Hyosang↗

NASA NTRS2008

Reconfigurable Processing for Satellite On-Board Automatic Cloud Cover Assessment (ACCA)

Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations.

El-Araby, Esam↗