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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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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-20: A Correlation Study of 2D and 3D Gamma Passing Rates for Prostate IMRT Plans

Purpose: To investigate the correlation between the two-dimensional gamma passing rate (2D %GP) and three-dimensional gamma passing rate (3D %GP) in prostate IMRT quality assurance. Methods: Eleven prostate IMRT plans were randomly selected from the clinical database and were used to obtain dose distributions in the phantom and patient. Three types of delivery errors (MLC bank sag errors, central MLC errors and monitor unit errors) were intentionally introduced to modify the clinical plans through an in-house Matlab program. This resulted in 187 modified plans. The 2D %GP and 3D %GP were analyzed using different dose-difference and distance-toagreement (1%-1mm, 2%-2mm and 3%-3mm) and 20% dose threshold. The 2D %GP and 3D %GP were then compared not only for the whole region, but also for the PTVs and critical structures using the statistical Pearson’s correlation coefficient (γ). Results: For different delivery errors, the average comparison of 2D %GP and 3D %GP showed different conclusions. The statistical correlation coefficients between 2D %GP and 3D %GP for the whole dose distribution showed that except for 3%/3mm criterion, 2D %GP and 3D %GP of 1%/1mm criterion and 2%/2mm criterion had strong correlations (Pearson’s γ value >0.8). Compared with the whole region, the correlations of 2D %GP and 3D %GP for PTV were better (the γ value for 1%/1mm, 2%/2mm and 3%/3mm criterion was 0.959, 0.931 and 0.855, respectively). However for the rectum, there was no correlation between 2D %GP and 3D %GP. Conclusion: For prostate IMRT, the correlation between 2D %GP and 3D %GP for the PTV is better than that for normal structures. The lower dose-difference and DTA criterion shows less difference between 2D %GP and 3D %GP. Other factors such as the dosimeter characteristics and TPS algorithm bias may also influence the correlation between 2D %GP and 3D %GP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Estimation of sensor measurement errors in reactor coolant systems using multi-sensor fusion

A nuclear power plant is typically instrumented with a variety of sensors to continually monitor its variables, and their sensor’s measurements may be used to assess the plant state and initiate safety actions, if needed. Errors in sensor measurements, due to factors such as calibration drifts, critically affect such state assessments. Here, we address a problem of estimating sensor errors using physics-informed machine learning methods that use measurements collected under known plant conditions. For a given sensor, we propose an information fusion method that uses measurements from other sensors to estimate its output assuming it is error-free and provides its difference from an actual measurement as an error estimate. We present the ensemble of trees and support vector machine fusers, and evaluate their performance using measurements collected over an emulated test loop of a pressurized water reactor. The plant variables are related to each other through the underlying physical laws under inertial constraints that place bounds on their derivatives, which analytically justify the applicability of machine learning methods for computing these fusers. Under twenty scenarios, we assess their sensor error estimates for pressure sensors of the heat exchanger of a reactor’s primary coolant system. Multiple types of errors are captured by both fusers under externally induced calibration drifts, blockages, minor leaks and air gaps in sensing lines, and electromagnetic interference; the root mean square error of the estimation of error is under 2.2% percent of the maximum measurement. We present generalization equations, in the framework of statistical learning theory, for these methods that characterize the confidence probability that the estimation error is bounded by a specified parameter in future test scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1981

Burst statistics of Viterbi decoding

A mathematical model of Viterbi decoder burst error performance is presented. This model allows for computer generation of Viterbi-like error sequences quickly and inexpensively for applications where large amounts of data are required. The model is corroborated through comparisons with actual software decoder simulations.

Deutsch, L. J.↗
NASA NTRS1982

Statistical tests of ARIES data

Statistical tests are performed on Project ARIES preliminary baseline measurements in the Southern California triangle formed by the Jet Propulsion Laboratory, the Owens Valley Radio Observatory, and the Goldstone tracking complex during 1976-1980. In addition to conventional one-dimensional tests a two-dimensional test which allows for an arbitrary correlation between errors in individual components is formulated using the Hotelling statistic. On two out of three baselines the mean rate of change in baseline vector is statistically significant. Apparent motions on all three baselines are consistent with a pure shear with north-south compression and east-west expansion of 1 x 10 to the -7th/year. The ARIES measurements are consistent with the USGS geodolite networks in Southern California and the SAFE laser satellite ranging experiment. All three experiments are consistent with a 6 cm/year motion between the Pacific and North American Plates and a band of diffuse shear 300 km wide, except that corresponding rotation of the entire triangle is not found.

Musman, S.↗
NASA NTRS2001

Linear and Order Statistics Combiners for Pattern Classification

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Tumer, Kagan↗
NASA NTRS1975

Digital simulation of a communication link for Pioneer Saturn Uranus atmospheric entry probe, part 1

A digital simulation study is presented for a candidate modulator/demodulator design in an atmospheric scintillation environment with Doppler, Doppler rate, and signal attenuation typical of the conditions of an outer planet atmospheric probe. The simulation results indicate that the mean channel error rate with and without scintillation are similar to theoretical characterizations of the link. The simulation gives information for calculating other channel statistics and generates a quantized symbol stream on magnetic tape from which error correction decoding is analyzed. Results from the magnetic tape data analyses are also included. The receiver and bit synchronizer are modeled in the simulation at the level of hardware component parameters rather than at the loop equation level and individual hardware parameters are identified. The atmospheric scintillation amplitude and phase are modeled independently. Normal and log normal amplitude processes are studied. In each case the scintillations are low pass filtered. The receiver performance is given for a range of signal to noise ratios with and without the effects of scintillation. The performance is reviewed for critical reciever parameter variations.

Hinrichs, C. A.↗
NASA NTRS1972

Decision-directed adaptive recursive estimators - Divergence prevention.

A method is proposed for dealing with the divergence phenomenon problem encountered in applications of minimum-variance recursive estimators when the error covariance calculated by the estimator becomes inconsistent with the actual error covariance. The proposed method differs from previous ones in that the state model and statistics are accepted as given. The form of the optimal estimator is used, but a constant check is made on the consistency of the calculated and actual error covariances. The method is independent of the source of error, whether it be inaccuracies in the system model, incorrect values of the a priori and random process statistics, approximations required in the case of nonlinear systems, or computational roundoff. Several simulated examples, in which inconsistencies in the calculated and actual error covariances exist, show a significant improvement in the performance of the estimator when the given procedure is applied.

Nahi, N. E.↗
DOE OSTI2023

Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum

Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processor’s qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors, increasing the overall fidelity and hence the overall quality of circuit induction. Earlier this year, a pioneering work, namely HAMMER, published in ASPLOS-22 demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they intuitively assumed that errors occur in local clusters, and that at higher average Hamming distances this structure falls away. In this work, we show that such a correlation structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the new iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, the fidelity improvement is up to 17.8%. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to integrate and provide more reliable circuit induction results.

Stein, Samuel A.↗
DOE OSTI2025

Comparing Compressed and Full-Modeling analyses with FOLPS: implications for DESI 2024 and beyond

The Dark Energy Spectroscopic Instrument (DESI) will provide unprecedented information about the large-scale structure of our Universe. In this work, we study the robustness of the theoretical modelling of the power spectrum of F OLPS , a novel effective field theory-based package for evaluating the redshift space power spectrum in the presence of massive neutrinos. We perform this validation by fitting the AbacusSummit high-accuracy N -body simulations for Luminous Red Galaxies, Emission Line Galaxies and Quasar tracers, calibrated to describe DESI observations. We quantify the potential systematic error budget of F OLPS finding that the modelling errors are fully sub-dominant for the DESI statistical precision within the studied range of scales. Additionally, we study two complementary approaches to fit and analyse the power spectrum data, one based on direct Full-Modelling fits and the other on the ShapeFit compression variables, both resulting in very good agreement in precision and accuracy. In each of these approaches, we study a set of potential systematic errors induced by several assumptions, such as the choice of template cosmology, the effect of prior choice in the nuisance parameters of the model, or the range of scales used in the analysis. Furthermore, we show how opening up the parameter space beyond the vanilla ΛCDM model affects the DESI observables. These studies include the addition of massive neutrinos, spatial curvature, and dark energy equation of state. We also examine how relaxing the usual Cosmic Microwave Background and Big Bang Nucleosynthesis priors on the primordial spectral index and the baryonic matter abundance, respectively, impacts the inference on the rest of the parameters of interest. This paper pathways towards performing a robust and reliable analysis of the shape of the power spectrum of DESI galaxy and quasar clustering using F OLPS .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Comparative Assessment of Data-driven Process Models in Health Information Technology

Process mining for conformance analysis consists of comparing a reference process model against a data-driven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman’s correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Texture analysis on the fluence map to evaluate the degree of modulation for volumetric modulated arc therapy

Purpose: Texture analysis on fluence maps was performed to evaluate the degree of modulation for volumetric modulated arc therapy (VMAT) plans. Methods: A total of six textural features including angular second moment, inverse difference moment, contrast, variance, correlation, and entropy were calculated for fluence maps generated from 20 prostate and 20 head and neck VMAT plans. For each of the textural features, particular displacement distances (d) of 1, 5, and 10 were adopted. To investigate the deliverability of each VMAT plan, gamma passing rates of pretreatment quality assurance, and differences in modulating parameters such as multileaf collimator (MLC) positions, gantry angles, and monitor units at each control point between VMAT plans and dynamic log files registered by the Linac control system during delivery were acquired. Furthermore, differences between the original VMAT plan and the plan reconstructed from the dynamic log files were also investigated. To test the performance of the textural features as indicators for the modulation degree of VMAT plans, Spearman’s rank correlation coefficients (r{sub s}) with the plan deliverability were calculated. For comparison purposes, conventional modulation indices for VMAT including the modulation complexity score for VMAT, leaf travel modulation complexity score, and modulation index supporting station parameter optimized radiation therapy (MI{sub SPORT}) were calculated, and their correlations were analyzed in the same way. Results: There was no particular textural feature which always showed superior correlations with every type of plan deliverability. Considering the results comprehensively, contrast (d = 1) and variance (d = 1) generally showed considerable correlations with every type of plan deliverability. These textural features always showed higher correlations to the plan deliverability than did the conventional modulation indices, except in the case of modulating parameter differences. The r{sub s} values of contrast to the global gamma passing rates with criteria of 2%/2 mm, 2%/1 mm, and 1%/2 mm were 0.536, 0.473, and 0.718, respectively. The respective values for variance were 0.551, 0.481, and 0.688. In the case of local gamma passing rates, the r{sub s} values of contrast were 0.547, 0.578, and 0.620, respectively, and those of variance were 0.519, 0.527, and 0.569. All of the r{sub s} values in those cases were statistically significant (p < 0.003). In the cases of global and local gamma passing rates, MI{sub SPORT} showed the highest correlations among the conventional modulation indices. For global passing rates, r{sub s} values of MI{sub SPORT} were −0.420, −0.330, and −0.632, respectively, and those for local passing rates were −0.455, −0.490 and −0.502. The values of r{sub s} of contrast, variance, and MI{sub SPORT} with the MLC errors were −0.863, −0.828, and 0.795, respectively, all with statistical significances (p < 0.001). The correlations with statistical significances between variance and dose-volumetric differences were observed more frequently than the others. Conclusions: The contrast (d = 1) and variance (d = 1) calculated from fluence maps of VMAT plans showed considerable correlations with the plan deliverability, indicating their potential use as indicators for assessing the degree of modulation of VMAT plans. Both contrast and variance consistently showed better performance than the conventional modulation indices for VMAT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Robust verification of stochastic simulation codes

We introduce a robust verification tool for computational codes, which we call Stochastic Robust Extrapolation based Error Quantification (StREEQ). Unlike the prevalent Grid Convergence Index (GCI) [1] method, our approach is suitable for both stochastic and deterministic computational codes and is generalizable to any number of discretization variables. Building on ideas introduced in the Robust Verification [2] approach, we estimate the converged solution and orders of convergence with uncertainty using multiple fits of a discretization error model. In contrast to Robust Verification, we perform these fits to many bootstrap samples yielding a larger set of predictions with smoother statistics. Here, bootstrap resampling is performed on the lack-of-fit errors for deterministic code responses, and directly on the noisy data set for stochastic responses. This approach lends a degree of robustness to the overall results, capable of yielding precise verification results for sufficiently resolved data sets, and appropriately expanding the uncertainty when the data set does not support a precise result. For stochastic responses, a credibility assessment is also performed to give the analyst an indication of the trustworthiness of the results. Furthermore, this approach is suitable for both code and solution verification, and is particularly useful for solution verification of high-consequence simulations..

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2022

Evaluation of remote sensing-based evapotranspiration products at low-latitude eddy covariance sites

Remote sensing-based evapotranspiration (ET) products have been evaluated primarily using data from northern middle latitudes; therefore, little is known about their performance at low latitudes. To address this bias, an evaluation dataset was compiled using eddy covariance data from 40 sites between latitudes 30° S and 30° N. The flux data were obtained from the emerging network in Mexico (MexFlux) and from openly available databases of FLUXNET, AsiaFlux, and OzFlux. This unique reference dataset was then used to evaluate remote sensing-based ET products in environments that have been underrepresented in earlier studies. The evaluated products were: MODIS ET (MOD16, both the discontinued collection 5 (C5) and the latest collection (C6)), Global Land Evaporation Amsterdam Model (GLEAM) ET, and Atmosphere-Land Exchange Inverse (ALEXI) ET. Products were compared with unadjusted fluxes (ETorig) and with fluxes corrected for the lack of energy balance closure (ETebc). Three common statistical metrics were used: coefficient of determination (R2), root mean square error (RMSE), and percent bias (PBIAS). The effect of a vegetation mismatch between pixel and site on product evaluation results was investigated by examining the relationship between the statistical metrics and product-specific vegetation match indexes. Evaluation results of this study and those published in the literature were used to examine the performance of the products across latitudes. Differences between the MOD16 collection 5 and 6 datasets were generally smaller than differences with the other products. Performance and ranking of the evaluated products depended on whether ETorig or ETebc was used. When using ETorig, GLEAM generally had the highest R2, smallest PBIAS, and best RMSE values across the studied land cover types and climate zones. Neither MOD16 nor ALEXI performed consistently better than the other. When using ETebc, none of the products stood out in terms of both low bias and strong correlations. The use of ETebc instead of ETorig affected the biases more than the correlations. The product evaluation results showed no significant relationship with the degree of match between the vegetation at the pixel and site scale. The latitudinal comparison showed tendencies of lower R2 (all products) but better PBIAS and normalized RMSE values (MOD16 and GLEAM) for forests at low latitudes than for forests at northern middle latitudes. For non-forest vegetation, the products showed no clear latitudinal differences in performance.

Diego Salazar-Martínez↗
NASA NTRS1982

The effects of transmitter/receiver clock time-base instability on coherent communication system performance

The purpose of this paper is to present a model and associated analysis of the deleterious effects which both transmitter and receiver data clock time-base instability have on bit-error probability. The types of time-base instability modeled and analyzed include what is commonly referred to as bit jitter (epoch jitter), bit-jitter rate (time interval jitter), and data asymmetry. The bit-error probability is determined as a function of the statistical characteristics of the time-base instability, the data-transition density, the signal-to-noise ratio in the bit-rate bandwidth and the mechanizations of both the data-bit detector and bit synchronizer. The latter is manifested through the bit synchronizer's natural frequency and loop-damping parameters. Numerical results which indicate the sensitivity of the system bit-error rate to various parameters of the model are provided.

Chie, C. M.↗
NASA NTRS2004

The Impact of Subsampling on MODIS Level-3 Statistics of Cloud Optical Thickness and Effective Radius

The MODIS Level-3 optical thickness and effective radius cloud product is a gridded l deg. x 1 deg. dataset that is derived from aggregation and subsampling at 5 km of 1 km, resolution Level-2 orbital swath data (Level-2 granules). This study examines the impact of the 5 km subsampling on the mean, standard deviation and inhomogeneity parameter statistics of optical thickness and effective radius. The methodology is simple and consists of estimating mean errors for a large collection of Terra and Aqua Level-2 granules by taking the difference of the statistics at the original and subsampled resolutions. It is shown that the Level-3 sampling does not affect the various quantities investigated to the same degree, with second order moments suffering greater subsampling errors, as expected. Mean errors drop dramatically when averages over a sufficient number of regions (e.g., monthly and/or latitudinal averages) are taken, pointing to a dominance of errors that are of random nature. When histograms built from subsampled data with the same binning rules as in the Level-3 dataset are used to reconstruct the quantities of interest, the mean errors do not deteriorate significantly. The results in this paper provide guidance to users of MODIS Level-3 optical thickness and effective radius cloud products on the range of errors due to subsampling they should expect and perhaps account for, in scientific work with this dataset. In general, subsampling errors should not be a serious concern when moderate temporal and/or spatial averaging is performed.

Oreopoulos, Lazaros↗
DOE OSTI2017

Representative elementary volume (REV) of cementitious materials from three-dimensional pore structure analysis

The representative elementary volume (REV) is a fundamental property of a material, but no direct measurements exist for cementitious materials. In this paper, the REV of cement pastes with supplementary cementitious materials (GGBS, PFA, SF) was determined by analysing the three-dimensional pore structure (> 0.2 μm) using laser scanning confocal microscopy (LSCM). The effect of axial distortion inherent to LSCM on 3D pore structure was also investigated. A range of 3D pore parameters was measured using skeletonisation, maximal ball and random walker algorithms. Results show that axial distortion has insignificant effects on most parameters except Euler connectivity, average pore and throat volumes and directional diffusion tortuosities. Most pore parameters become independent of sampling volume at ≈ 60{sup 3} μm{sup 3} except diffusion tortuosities and formation factor. The REV for porosity calculated based on a statistical approach at eight realisations and 5% relative error was found to be ≈ 100{sup 3} μm{sup 3}.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Benefit Cost Analysis of Improved Forecasting for Day-Ahead Hourly Regulation Requirements

A benefit cost analysis (BCA) of the Day-Ahead Regulation Prediction (DARP) tool found substantial savings to providing an improved forecast of the California Independent System Operators (CAISO) reserve requirements for regulation services with increasing amounts of solar generation. DARP predicts day-ahead reserve requirements for Regulation Up (Reg Up) and Regulation Down (Reg Down) using the balancing authority’s Area Control Error (ACE) and actual frequency. The BCA approach statistically estimated the price-quantity relationships for Reg Up and Reg Down. The estimated changes in Regulation reserve prices along with the estimated changes in day-ahead reserve requirements, forecast by DARP, provide a cost savings estimate. The results indicate that more savings were found for reducing Reg Up than reducing Reg Down purchases. A similar approach may be used to estimate savings from improved forecasting of reserves for other ancillary services, or to estimate benefits from other forecasting tools and methodologies.

Ancillary Services, Benefit Cost Analysis, Frequen↗
DOE OSTI2023

An Online Joint Optimization–Estimation Architecture for Distribution Networks

Here in this article, we propose an optimal joint optimization-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the OPF decisions and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization and estimation usually operate at distinct layers and different time scales. Hence, it enables a computationally affordable, efficient, and robust online operational framework for distribution networks under time-varying settings.

24 POWER TRANSMISSION AND DISTRIBUTION↗