Results for “error analysis” — Page 33

DOE OSTI2021

Composition Measurements of the LAW Phase 5 Glasses

This report provides the results from the chemical analyses of glass compositions for the Low-Activity Waste Phase 5 study glasses, a series of simulated nuclear waste glasses fabricated at Pacific Northwest National Laboratory. These data will be used in the development of enhanced property/composition models for waste glass vitrification at Hanford. Chemical analyses were performed on a representative sample of each of the quenched glasses to allow for comparisons with targeted compositions. The relative differences between the targeted and measured concentrations of CaO, Cl - , K 2 O, Li 2 O, and SO 3 for some of the glasses were greater than 10%. Several of these glass samples were rerun to verify original measurements; however, there were no significant changes in the measurements to indicate errors in preparation or analysis of the samples. These results can be used in further characterization of this series of glasses, including the normalization of Product Consistency Test results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Characterization of Recovery Human Action Mechanisms in Nuclear Power Plants

Recovery human action is defined as the action that prevents deviant conditions from producing unwanted effects. Analyzing recovery actions has been a critical part in human reliability analysis (HRA). However, there are a couple of limitations to treat recovery actions only depending on the current HRA methods. Representatively, the existing recovery analysis does not specifically consider recovery actions as are occurred in actual nuclear power plants (NPPs). The overall goal of this study aims to develop a novel recovery analysis method to account for human action recoveries in context of scenarios as well as complement the limitations of existing recovery analysis. In this paper, the recovery analysis in current HRA methods and their challenges are introduced. A strategy to achieve the goal is introduced with a modified recovery definition. Then, how we have researched the approach will be introduced in the paper.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Method for computing self-consistent solution in a gun code

Complex gun code computations can be made to converge more quickly based on a selection of one or more relaxation parameters. An eigenvalue analysis is applied to error residuals to identify two error eigenvalues that are associated with respective error residuals. Relaxation values can be selected based on these eigenvalues so that error residuals associated with each can be alternately reduced in successive iterations. In some examples, relaxation values that would be unstable if used alone can be used.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Weak-lensing magnification as a probe for the dark Universe

This Thesis is devoted to the analysis of weak-lensing magnification on the Dark Energy Survey. Two analysis with different goals each are made on different data-sets: the Science Verification (DES-SV) and the Year 1 (DES-Y1). The DES-SV analysis aims the development of techniques to detect the weak-lensing number count magnification signal and the mitigation of systematic errors. The DES-Y1 analysis employs the methods used with the DES-SV data to measure the convergence profile of the emptiest regions of the Universe –voids and troughs–to use them as a new cosmological probe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗

DOE OSTI2017

Numerical Uncertainties vs. Model Uncertainties in Two-phase Flow Simulations

In recent years, verification, validation and uncertainty quantification (VVUQ) have become a common practice in thermal-hydraulics analysis. In general, these activities deal with propagations of uncertainties in computer code simulations, e.g., through system analysis codes. However, most existing such activities in thermal-hydraulics analysis have been primarily focused on model uncertainties, while numerical uncertainties were largely overlooked. Numerical uncertainties (or numerical errors) can appear in many different forms, e.g., round-off error, statistical sampling error, and discretization error. As pointed out by Oberkampf and Roy, discretization error is usually the largest however the most difficult type of error to estimate reliably. In thermal-hydraulics analysis, especially two-phase flow simulations commonly encountered in reactor safety analysis, the lack of consideration on discretization error is mainly due to the difficulty in estimating them. Accurate estimations on discretization error require continuous mesh refinement and/or implementation of high-order numerical schemes in system analysis code, both of which are unfortunately difficult to achieve for existing codes. At first, it should be emphasized that mesh refinement (or mesh sensitivity study) is a well-understood concept in the thermal-hydraulics field. However, it is not commonly practiced for many reasons, e.g., lack of understanding on numerical errors by code users, consideration on computational cost, deteriorated numerical stability performance with refined meshes, and etc. Secondly, high-order (e.g., second-order) numerical schemes are simply unavailable in almost any existing system analysis code. Implementation of high-order numerical schemes in these codes is difficult, if not impossible, given the complexity of these codes. On the other hand, people tend to argue that numerical uncertainties (or discretization error as we have emphasized) are not as important as the commonly large uncertainties in two-phase flow models, e.g., closure correlations. As a result, nodalization is sometimes played unintentionally, or maybe intentionally, to match experimental data, which is referred as 'user effects' or 'compensating errors' in thermal-hydraulics community. Such a practice is highly criticized by Levy. In this work, we will at first build a computer code that incorporates both first-order and second-order numerical methods to solve the two-phase flow problems. The first-order method resembles the one used in many existing system analysis codes; and the second-order method works as the reference to estimate numerical errors. Numerical verifications on spatial discretization schemes will be presented in the form of mesh convergence study. It will also be demonstrated that, in practical scenarios, discretization errors can be as large as, or even larger than, model uncertainties via case studies.

42 ENGINEERING↗

DOE OSTI2014

Establishing a process of irradiating small animal brain using a CyberKnife and a microCT scanner

Purpose: Establish and validate a process of accurately irradiating small animals using the CyberKnife G4 System (version 8.5) with treatment plans designed to irradiate a hemisphere of a mouse brain based on microCT scanner images. Methods: These experiments consisted of four parts: (1) building a mouse phantom for intensity modulated radiotherapy (IMRT) quality assurance (QA), (2) proving usability of a microCT for treatment planning, (3) fabricating a small animal positioning system for use with the CyberKnife's image guided radiotherapy (IGRT) system, and (4)in vivo verification of targeting accuracy. A set of solid water mouse phantoms was designed and fabricated, with radiochromic films (RCF) positioned in selected planes to measure delivered doses. After down-sampling for treatment planning compatibility, a CT image set of a phantom was imported into the CyberKnife treatment planning system—MultiPlan (ver. 3.5.2). A 0.5 cm diameter sphere was contoured within the phantom to represent a hemispherical section of a mouse brain. A nude mouse was scanned in an alpha cradle using a microCT scanner (cone-beam, 157 × 149 pixels slices, 0.2 mm longitudinal slice thickness). Based on the results of our positional accuracy study, a planning treatment volume (PTV) was created. A stereotactic body mold of the mouse was “printed” using a 3D printer laying UV curable acrylic plastic. Printer instructions were based on exported contours of the mouse's skin. Positional reproducibility in the mold was checked by measuring ten CT scans. To verify accurate dose delivery in vivo, six mice were irradiated in the mold with a 4 mm target contour and a 2 mm PTV margin to 3 Gy and sacrificed within 20 min to avoid DNA repair. The brain was sliced and stained for analysis. Results: For the IMRT QA using a set of phantoms, the planned dose (6 Gy to the calculation point) was compared to the delivered dose measured via film and analyzed using Gamma analysis (3% and 3 mm). A passing rate of 99% was measured in areas of above 40% of the prescription dose. The final inverse treatment plan was comprised of 43 beams ranging from 5 to 12.5 mm in diameter (2.5 mm size increments are available up to 15 mm in diameter collimation). Using the Xsight Spine Tracking module, the CyberKnife system could not reliably identify and track the tiny mouse spine; however, the CyberKnife system could identify and track the fiducial markers on the 3D mold.In vivo positional accuracy analysis using the 3D mold generated a mean error of 1.41 mm ± 0.73 mm when fiducial markers were used for position tracking. Analysis of the dissected brain confirmed the ability to target the correct brain volume. Conclusions: With the use of a stereotactic body mold with fiducial markers, microCT imaging, and resolution down-sampling, the CyberKnife system can successfully perform small-animal radiotherapy studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

Erratum: “On virial analysis at low aspect ratio” [Phys. Plasmas 23, 072508 (2016)]

This article corrects an error in M.W. Bongard et al., 'On Virial Analysis at Low Aspect Ratio,' Phys. Plasmas 23, 072508 (2016) pertaining to a typographical error in its equation 21. Furthermore, all numerical calculations reported in this paper utilized the correct formulation described here. Accordingly, no changes to the conclusions of the original article are required.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2013

Status and characterization of the lithium beam diagnostic on DIII-D

The 30 keV lithium beam diagnostic on DIII-D is suitable to measure both the radial electron density and poloidal magnetic field profiles in the pedestal. The refurbished system features a new setup to measure the Doppler shift allowing accurate alignment of the spectral filters. The injector has been optimized to generate a stable lithium neutral beam with a current of I= 15−20 mA and a diameter of 1.9 ± 0.1 cm measured by beam imaging. The typical temporal resolution is Δt= 1−10 ms and the radial resolution of ΔR= 5 mm is given by the optical setup. A new analysis technique based on fast Fourier transform avoids systematic error contributions from the digital lock-in analysis and accounts intrinsically for background light correction. Latest upgrades and a detailed characterization of the system are presented. Proof-of-principle measurements of the poloidal magnetic field with a statistical error of typically 2% show a fair agreement with the predictions modeled with the Grad-Shafranov equilibrium solver EFIT within 4%.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Uncertainty of Rotating Shadowband Irradiometers and Si-Pyranometers Including the Spectral Irradiance Error

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible and color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2% for global horizontal irradiance (GHI), and 2.9% for DNI (for GHI and DNI over 300 W/m2) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.

14 SOLAR ENERGY↗

DOE OSTI2018

The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: theoretical systematics and Baryon Acoustic Oscillations in the galaxy correlation function

We investigate the potential sources of theoretical systematics in the anisotropic Baryon Acoustic Oscillation (BAO) distance scale measurements from the clustering of galaxies in configuration space using the final Data Release (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS). We perform a detailed study of the impact on BAO measurements from choices in the methodology such as fiducial cosmology, clustering estimators, random catalogues, fitting templates, and covariance matrices. The theoretical systematic uncertainties in BAO parameters are found to be 0.002 in the isotropic dilation α and 0.003 in the quadrupolar dilation ε. The leading source of systematic uncertainty is related to the reconstruction techniques. Theoretical uncertainties are sub-dominant compared with the statistical uncertainties for BOSS survey, accounting 0.2σ stat for α and 0.25σ stat for ε (σ α , stat ~ 0.010 and σ ε , stat ~ 0.012, respectively). We also present BAO-only distance scale constraints from the anisotropic analysis of the correlation function. Our constraints on the angular diameter distance D A (z) and the Hubble parameter H(z), including both statistical and theoretical systematic uncertainties, are 1.5 percent and 2.8 percent at z eff = 0.38, 1.4 percent and 2.4 percent at z eff = 0.51, and 1.7 percent and 2.6 percent at z eff = 0.61. This paper is part of a set that analyses the final galaxy clustering data set from BOSS. The measurements and likelihoods presented here are cross-checked with other BAO analysis in Alam et al. The systematic error budget concerning the methodology on post-reconstruction BAO analysis presented here is used in Alam et al. to produce the final cosmological constraints from BOSS.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Discrete models of fluids: spatial averaging, closure and model reduction

We consider semidiscrete ODE models of single-phase fluids and two-fluid mixtures. In the presence of multiple fine-scale heterogeneities, the size of these ODE systems can be very large. Spatial averaging is then a useful tool for reducing computational complexity of the problem. The averages satisfy exact balance equations of mass, momentum, and energy. These equations do not form a satisfactory continuum model because evaluation of stress and heat flux requires solving the underlying ODEs. To produce continuum equations that can be simulated without resolving microscale dynamics, we recently proposed a closure method based on the use of regularized deconvolution. Here we continue the investigation of deconvolution closure with the long term objective of developing consistent computational upscaling for multiphase particle methods. The structure of the fine-scale particle solvers is reminiscent of molecular dynamics. For this reason we use nonlinear averaging introduced for atomistic systems by Noll, Hardy, and Murdoch-Bedeaux. We also consider a simpler linear averaging originally developed in large eddy simulation of turbulence. We present several simple but representative examples of spatially averaged ODEs, where the closure error can be analyzed. Based on this analysis we suggest a general strategy for reducing the relative error of approximate closure. For problems with periodic highly oscillatory material parameters we propose a spectral boosting technique that augments the standard deconvolution and helps to correctly account for dispersion effects. We also conduct several numerical experiments, one of which is a complete mesoscale simulation of a stratified two-fluid flow in a channel. In this simulation, the operation count per coarse time step scales sublinearly with the number of particles.

Multiscale modeling, discrete models, fluid flow↗

DOE OSTI2022

Analyzing rotational bands in odd-mass nuclei using effective field theory and Bayesian methods

We recently developed an Effective Field Theory (EFT) for rotational bands in odd-mass nuclei. Here we use EFT expressions to perform a Bayesian analysis of data on the rotational energy levels of 99 Tc, 155,157 Gd, 159 Dy, 167,169 Er, 167,169 Tm, 183 W, 235 U and 239 Pu. The error model in our Bayesian analysis includes both experimental and EFT truncation uncertainties. It also accounts for the fact that low-energy constants (LECs) at even and odd orders are expected to have different sizes. We use Markov Chain Monte Carlo (MCMC) sampling to explore the joint posterior of the EFT and error-model parameters and show both the LECs and the breakdown scale can be reliably determined. We extract the LECs up to fourth order in the EFT and find that, provided we correctly account for EFT truncation errors in our likelihood, results for lower-order LECs are stable as we go to higher orders. LEC results are also stable with respect to the addition of higher-energy data. We extract the expansion parameter for all the nuclei listed above and find a clear correlation between the extracted and the expected value of the inverse breakdown scale, W, based on the single-particle and vibrational energy scales. However, the W that actually determines the convergence of the EFT expansion is markedly smaller than would be naively expected based on those scales.

EFT↗

DOE OSTI2021

Antiferromagnetic character of the quantum phase transition in the Hubbard model on the honeycomb lattice

In this work we provide a unified, comprehensive treatment of all operators that contribute to the antiferromagnetic, ferromagnetic, and charge-density-wave structure factors and order parameters of the hexagonal Hubbard Model. We use the Hybrid Monte Carlo algorithm to perform a systematic, carefully controlled analysis in the temporal Trotter error and of the thermodynamic limit. We expect our findings to improve the consistency of Monte Carlo determinations of critical exponents. We perform a data collapse analysis and determine the critical exponent β = 0.898 (37) for the semimetal-Mott insulator transition in the hexagonal Hubbard Model. Our methods are applicable to a wide range of lattice theories of strongly correlated electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

TH-B-BRC-00: How to Identify and Resolve Potential Clinical Errors Before They Impact Patients Treatment: Lessons Learned

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-B-BRC-01: How to Identify and Resolve Potential Clinical Errors

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-B-BRC-02: Troubleshooting Issues with Linear Accelerator

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-B-BRC-03: Measurement and Calibration Discrepancy: Advanced Warning Guidance

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.

60 APPLIED LIFE SCIENCES↗