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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Spatial frequency spectrum of the x-ray scatter distribution in CBCT projections

Purpose: X-ray scatter is a source of significant image quality loss in cone-beam computed tomography (CBCT). The use of Monte Carlo (MC) simulations separating primary and scattered photons has allowed the structure and nature of the scatter distribution in CBCT to become better elucidated. This work seeks to quantify the structure and determine a suitable basis function for the scatter distribution by examining its spectral components using Fourier analysis.Methods: The scatter distribution projection data were simulated using a CBCT MC model based on the EGSnrc code. CBCT projection data, with separated primary and scatter signal, were generated for a 30.6 cm diameter water cylinder [single angle projection with varying axis-to-detector distance (ADD) and bowtie filters] and two anthropomorphic phantoms (head and pelvis, 360 projections sampled every 1°, with and without a compensator). The Fourier transform of the resulting scatter distributions was computed and analyzed both qualitatively and quantitatively. A novel metric called the scatter frequency width (SFW) is introduced to determine the scatter distribution's frequency content. The frequency content results are used to determine a set basis functions, consisting of low-frequency sine and cosine functions, to fit and denoise the scatter distribution generated from MC simulations using a reduced number of photons and projections. The signal recovery is implemented using Fourier filtering (low-pass Butterworth filter) and interpolation. Estimates of the scatter distribution are used to correct and reconstruct simulated projections.Results: The spatial and angular frequencies are contained within a maximum frequency of 0.1 cm{sup −1} and 7/(2π) rad{sup −1} for the imaging scenarios examined, with these values varying depending on the object and imaging setup (e.g., ADD and compensator). These data indicate spatial and angular sampling every 5 cm and π/7 rad (∼25°) can be used to properly capture the scatter distribution, with reduced sampling possible depending on the imaging scenario. Using a low-pass Butterworth filter, tuned with the SFW values, to denoise the scatter projection data generated from MC simulations using 10{sup 6} photons resulted in an error reduction of greater than 85% for the estimating scatter in single and multiple projections. Analysis showed that the use of a compensator helped reduce the error in estimating the scatter distribution from limited photon simulations by more than 37% when compared to the case without a compensator for the head and pelvis phantoms. Reconstructions of simulated head phantom projections corrected by the filtered and interpolated scatter estimates showed improvements in overall image quality.Conclusions: The spatial frequency content of the scatter distribution in CBCT is found to be contained within the low frequency domain. The frequency content is modulated both by object and imaging parameters (ADD and compensator). The low-frequency nature of the scatter distribution allows for a limited set of sine and cosine basis functions to be used to accurately represent the scatter signal in the presence of noise and reduced data sampling decreasing MC based scatter estimation time. Compensator induced modulation of the scatter distribution reduces the frequency content and improves the fitting results.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-E-T-261: Plan Quality Assurance of VMAT Using Fluence Images Reconstituted From Log-Files

Purpose: A successful VMAT plan delivery includes precise modulations of dose rate, gantry rotational and multi-leaf collimator (MLC) shapes. One of the main problem in the plan quality assurance is dosimetric errors associated with leaf-positional errors are difficult to analyze because they vary with MU delivered and leaf number. In this study, we calculated integrated fluence error image (IFEI) from log-files and evaluated plan quality in the area of all and individual MLC leaves scanned. Methods: The log-file reported the expected and actual position for inner 20 MLC leaves and the dose fraction every 0.25 seconds during prostate VMAT on Elekta Synergy. These data were imported to in-house software that developed to calculate expected and actual fluence images from the difference of opposing leaf trajectories and dose fraction at each time. The IFEI was obtained by adding all of the absolute value of the difference between expected and actual fluence images corresponding. Results: In the area all MLC leaves scanned in the IFEI, the average and root mean square (rms) were 2.5 and 3.6 MU, the area of errors below 10, 5 and 3 MU were 98.5, 86.7 and 68.1 %, the 95 % of area was covered with less than error of 7.1 MU. In the area individual MLC leaves scanned in the IFEI, the average and rms value were 2.1 – 3.0 and 3.1 – 4.0 MU, the area of errors below 10, 5 and 3 MU were 97.6 – 99.5, 81.7 – 89.5 and 51.2 – 72.8 %, the 95 % of area was covered with less than error of 6.6 – 8.2 MU. Conclusion: The analysis of the IFEI reconstituted from log-file was provided detailed information about the delivery in the area of all and individual MLC leaves scanned.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Analysis of DESI × DES using the Lagrangian effective theory of LSS

In this work we use Lagrangian perturbation theory to analyze the harmonic space galaxy clustering signal of the Bright Galaxy Survey (BGS) and luminous red galaxies (LRGs) targeted by the dark energy spectroscopic instrument (DESI), combined with the galaxy-galaxy lensing signal measured around these galaxies using Dark Energy Survey Year 3 source galaxies. The BGS and LRG galaxies are extremely well characterized by DESI spectroscopy and, as a result, lens galaxy redshift uncertainty and photometric systematics contribute negligibly to the error budget of our “2 × 2-point” analysis. On the modeling side, this work represents the first application of the SPINOSAURUS code, implementing an effective field theory model for galaxy intrinsic alignments, and we additionally introduce a new scheme ( MAIAR ) for marginalizing over the large uncertainties in the redshift evolution of the intrinsic alignment signal. Furthermore, this is the first application of a hybrid effective field theory model for galaxy bias based on the A emulus 𝜈 simulations. Our main result is a measurement of the amplitude of the lensing signal, 𝑆 8 = 𝜎 8 ⁢(Ω 𝑚 /0.3) 0.5 = 0.85⁢0$^{+0.042}_{−0.050}$, consistent with values of this parameter derived from the primary cosmic microwave background. This constraint is artificially improved by a factor of 51% if we assume a more standard, but restrictive parametrization for the redshift evolution and sample dependence of the intrinsic alignment signal, and 63% if we additionally assume the nonlinear alignment model. Furthermore, we show that when fixing the cosmological model to the best-fit values from Planck PR4 there is > 5⁢𝜎 evidence for a deviation of the evolution of the intrinsic alignment signal from the functional form that is usually assumed in cosmic shear and galaxy-galaxy lensing studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

High order interpolation of magnetic fields with vector potential reconstruction for particle simulations

We propose a method for interpolating divergence-free continuous magnetic fields via vector potential reconstruction using Hermite interpolation, which ensures high-order continuity for applications requiring adaptive, high-order ordinary differential equation (ODE) integrators, such as the Dormand-Prince method. The method provides C(m) continuity and achieves high-order accuracy, making it particularly suited for particle trajectory integration and Poincaré section analysis under optimal integration order and timestep adjustments. Through numerical experiments, we demonstrate that the Hermite interpolation method preserves volume and continuity, which are critical for conserving toroidal canonical momentum and magnetic moment in guiding center simulations, especially over long-term trajectory integration. Furthermore, we analyze the impact of insufficient derivative continuity on Runge-Kutta schemes and show how it degrades accuracy at low error tolerances, introducing discontinuity-induced truncation errors. Lastly, we demonstrate performant Poincaré section analysis in two relevant settings of field data collocated from finite element meshes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Testing the dipole modulation model in CMBR

The hemispherical power asymmetry, observed in the CMBR data, has generally been interpreted in terms of the dipole modulation model for the temperature fluctuations. Here we point out that this model leads to several predictions, which can be directly tested in the current data. We suggest tests of the hemispherical power asymmetry both in real and multipole space. We find a significant signal of the dipole modulation model in WMAP and PLANCK data with our tests. The dipole amplitude and direction also agrees, within errors, with earlier results based on hemispherical analysis in multipole space. We also find evidence that the effective dipole modulation amplitude increases with the multipole l in the range l = 2−64.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

Many weather forecasting and climate models simulate a warm surface air temperature (T2m) bias over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to T2m bias using a short-term hindcast approach with observations mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during the period of April to August 2011. The present study examines the contribution of surface energy budget errors to the bias. All participating models simulate higher net shortwave and longwave radiative fluxes at the surface but there is no consistency on signs of biases in latent and sensible heat fluxes over the Central U.S. and ARM SGP. Nevertheless, biases in net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are the main contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF is affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis suggests that radiation errors are always an important source of T2m error for long-term climate runs with EF errors either of equal or lesser importance. However, for the short-term hindcasts, EF errors are more important provided a model has a substantial EF bias.

Ma, H. -Y.↗
DOE OSTI2017

Measured extent of agricultural expansion depends on analysis technique

Concern is rising that ecologically important, carbon-rich natural lands in the United States are losing ground to agriculture. We investigate how quantitative assessments of historical land use change to address this concern differ in their conclusions depending on the data set used. We examined land use change between 2006 and 2014 in 20 counties in the Prairie Pothole Region using the Cropland Data Layer, a modified Cropland Data Layer, data from the National Agricultural Imagery Program, and in-person ground-truthing. The Cropland Data Layer analyses overwhelmingly returned the largest amount of land use change with associated error that limits drawing conclusions from it. Analysis with visual imagery estimated a fraction of this land use change. Clearly, analysis technique drives understanding of the measured extent of land use change; different techniques produce vastly different results that would inform land management policy in strikingly different ways. As a result, best practice guidelines are needed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Optimizing group structures using hierarchical division

Creating group structures with few groups that give low errors is a difficult problem in reactor analysis. In recent years, automated optimization techniques have been applied to this task. We continue this trend by applying the hierarchical division algorithm to generate optimized group structures that minimize a cost function. At each stage, the algorithm adds a single group boundary to an existing group structure, dividing one group into two to increase the resolution of the group structure. The location of the added boundary is the one that gives the lowest error over all possible new boundary locations. Our implementation requires a beginning group structure, a set of candidate new boundary locations, and a set of reference reaction rates. As a proof of concept, we used WIMS-69 as the initial group structure, XMAS-172 as the ending group structures, and a 344-group reference group structure. Testing on two simple, homogenized reactor problems, we found that hierarchical division was able to reduce the error by a factor of around 5 with an increase of only 15% in the number of groups. Because hierarchical division can get stuck in local minima, it often reaches a plateau in its error reduction capability as many groups are added. Nevertheless, we find hierarchical division has strong potential to make good group structures into great group structures at a modest increase in computational cost. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Microsegregation Measurement: Methods and Applications

Microsegregation is chemical variation at the microstructural level in a solidified ingot, which can cause undesirable effects on material properties. Its formation is driven by different levels of solute elemental solubility between solid and liquid phases, resulting in solute buildup or depletion in the liquid phase during solidification. Characterization of microsegregation can be essential for understanding its formation and removing it through heat treatment. Measurement is frequently carried out via scanning electron microscope (SEM), though metallographic analysis, mechanical testing and optical radiography have been used. SEM analysis can consist of point measurements on single dendrites, line scans across or along dendrites, elemental maps, and extended line-based or grid-based analyses extending over many dendrites, which can generate large amounts of data. Line-based or grid-based data can be analyzed using scatterplots of element concentrations to visualize segregation behavior and sorting/ranking of data by a single element or a combination of elements, to give compositional profiles as a function of extent of solidification. For both qualitative and quantitative analyses, effects of phase transformations and secondary phases need to be considered as well as sorting of measurement errors when generating compositional profiles. Applications of analysis include determination of partition ratios, examination of solute–element interactions, determination of effects of cooling rate and back diffusion, applications to freckling studies, validations of modeling, and development of heat-treatment profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2013

HUBBLE PARAMETER MEASUREMENT CONSTRAINTS ON DARK ENERGY

We use 21 Hubble parameter versus redshift data points from Simon et al., Gaztanaga et al., Stern et al., and Moresco et al. to place constraints on model parameters of constant and time-evolving dark energy cosmologies. The inclusion of the eight new measurements results in H(z) constraints more restrictive than those derived by Chen and Ratra. These constraints are now almost as restrictive as those that follow from current Type Ia supernova (SNIa) apparent magnitude versus redshift data, which now more carefully account for systematic uncertainties. This is a remarkable result. We emphasize, however, that SNIa data have been studied for a longer time than the H(z) data, possibly resulting in a better estimate of potential systematic errors in the SNIa case. A joint analysis of the H(z), baryon acoustic oscillation peak length scale, and SNIa data favors a spatially flat cosmological model currently dominated by a time-independent cosmological constant but does not exclude slowly evolving dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗
DOE OSTI2019

Subtlety of TiO 2 phase stability: Reliability of the density functional theory predictions and persistence of the self-interaction error

TiO 2 is an important material with broad applications that can exist in different phases with dramatically different properties. Theoretical prediction of their polymorph energetics is therefore critical for the material design and for identifying thermodynamically accessible structures. Determining TiO 2 relative phase stabilities remains challenging for first-principles methods, and density functional theory is the only approach available for studying phase stabilities at finite temperatures with acceptable computational efficiency. Here, we show that density functional theory using the recently developed efficient strongly constrained and appropriately normed (SCAN) [Sun et al., Phys. Rev. Lett. 115, 036402 (2015)] exchange-correlation functional for the first time predicts the phase stability in qualitative agreement with the experimental results at realistic conditions. Further analysis shows that the self-interaction error intrinsic in the density functional persists in the stability prediction. By correcting the self-interaction error through an empirical approach, SCAN predicts the relative stability as well as defect properties in excellent agreement with the experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products

Approximate computing techniques, which trade off the computation accuracy of an algorithm for better performance and energy efficiency, have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop \bf qdot---an error-bounded approximate dot product kernel. Following the introduction of qdot, here we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks---the conjugate gradient (CG) and power methods. In some instances, using qdot for the dot products in CG can result in many components being quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Emulating Baryonic Effects in Weak Lensing Measurements

Baryonic effects are one of the most serious systematics to the tomographic analysis of weak lensing statistical measurements. We are working on a novel method for correcting cosmological observations for baryonic effects - a term used to label the fact that we observe galaxies, while theoretical modeling at present is only accurate enough for the dynamics of dark matter. Although modern cosmological simulations of dark matter (the “dark matter only", or DMO simulations) consistently reach the required 1% level of precision, the effect of baryonic physics on cosmological scales is expected to be a source of a much more severe systematic error in weak lensing measurements. In this analysis we apply a general framework to consistently include systematic effects into a likelihood analysis for cosmological surveys, such as the Dark Energy Survey (DES), and future Large Synoptic Survey Telescope survey (LSST).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Analysis of over-magnetization of elemental transition metal solids from the SCAN density functional

Current investigations have revealed that the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation exchange-correlation functional significantly over-magnetizes elemental Fe, Co, and Ni solids. For the paradigmatic case, bcc Fe, the error relative to experiment is ≳ 20 % . A comparative analysis of magnetization results from SCAN and its deorbitalized counterpart, SCAN-L, leads to identification of the source of the discrepancy. It is not from the difference between Kohn-Sham (SCAN-L) and generalized Kohn-Sham (SCAN) procedures. The key is the iso-orbital indicator α (the ratio of the local Pauli and Thomas-Fermi kinetic energy densities). Its deorbitalized counterpart α L has more dispersion in both spin channels with respect to magnetization in an approximate region between 0.6 and 1.2 bohrs around an Fe nucleus. The overall effect is that the SCAN switching function evaluated with α L reduces the energetic disadvantage of the down channel with respect to up compared to the original α , which in turn reduces the magnetization. This identifies the origin of the SCAN magnetization error as insensitivity of the SCAN switching function to α values in the approximate range 0.5 ≲ α ≲ 0.8 and oversensitivity for α ≳ 0.8 .

36 MATERIALS SCIENCE↗
DOE OSTI2017

Comparative analysis of image binarization methods for crack identification in concrete structures

Surface cracks in concrete structures are critical indicators of structural damage and durability. Manual visual inspection, the most commonly used method in practice, is inefficient from cost, time, accuracy, and safety perspectives. A promising alternative is computer vision-based methods that can automatically extract crack information from images. Image binarization, developed for text detection, is appropriate for crack identification, as texts and cracks are similar, consisting of distinguishable lines and curves. However, standardizing crack identification using image binarization is challenging, because binarization depends on the method and associated parameters. We investigate image binarization for crack identification, focusing on optimal parameter determination and comparative performance evaluation for five common binarization methods. Crack images are prepared to obtain optimal parameters by minimizing errors in estimated crack widths. Subsequently, comparative analysis is conducted using crack images with different conditions based on three performance evaluation criteria: crack width and length measurement accuracy and computation time.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: BAO and RSD measurements from the anisotropic power spectrum of the quasar sample between redshift 0.8 and 2.2

ABSTRACT We measure the clustering of quasars of the final data release (DR16) of eBOSS. The sample contains $343\, 708$ quasars between redshifts 0.8 ≤ z ≤ 2.2 over $4699\, \mathrm{deg}^2$. We calculate the Legendre multipoles (0,2,4) of the anisotropic power spectrum and perform a BAO and a Full-Shape (FS) analysis at the effective redshift zeff = 1.480. The errors include systematic errors that amount to 1/3 of the statistical error. The systematic errors comprise a modelling part studied using a blind N-body mock challenge and observational effects studied with approximate mocks to account for various types of redshift smearing and fibre collisions. For the BAO analysis, we measure the transverse comoving distance DM(zeff)/rdrag = 30.60 ± 0.90 and the Hubble distance DH(zeff)/rdrag = 13.34 ± 0.60. This agrees with the configuration space analysis, and the consensus yields: DM(zeff)/rdrag = 30.69 ± 0.80 and DH(zeff)/rdrag = 13.26 ± 0.55. In the FS analysis, we fit the power spectrum using a model based on Regularised Perturbation Theory, which includes redshift space distortions and the Alcock–Paczynski effect. The results are DM(zeff)/rdrag = 30.68 ± 0.90 and DH(zeff)/rdrag = 13.52 ± 0.51 and we constrain the linear growth rate of structure f(zeff)σ8(zeff) = 0.476 ± 0.047. Our results agree with the configuration space analysis. The consensus analysis of the eBOSS quasar sample yields: DM(zeff)/rdrag = 30.21 ± 0.79, DH(zeff)/rdrag = 3.23 ± 0.47, and f(zeff)σ8(zeff) = 0.462 ± 0.045 and is consistent with a flat ΛCDM cosmological model using Planck results.

Neveux, Richard↗