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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

EFFECT OF DISCRETIZATION ERROR ON THE QUANTIFICATION OF MODEL UNCERTAINTY IN TWO-PHASE FLOW SIMULATIONS

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 input and model uncertainties, while numerical errors were largely overlooked. Numerical errors can appear in many different forms, e.g., round-off error, statistical sampling error, and discretization error. In thermal-hydraulics analysis, especially two-phase flow simulations commonly encountered in reactor safety analysis, the lack of consideration of discretization error is mainly due to the difficulty in estimating them. Accurate estimations of discretization error require continuous mesh refinement and/or implementation of high-order numerical schemes in system analysis code, both of which are difficult to achieve in existing codes. In this work, we will 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 verification of spatial discretization schemes will be presented in the form of mesh convergence study. It will also be demonstrated via case studies that, in practical scenarios, discretization errors can be as large as, or even larger than, model uncertainties.

42 ENGINEERING↗
DOE OSTI2017

Numerical investigations of potential systematic uncertainties in iron opacity measurements at solar interior temperatures

Iron opacity calculations presently disagree with measurements at an electron temperature of ~180–195 eV and an electron density of (2–4)×10 22 cm –3 , conditions similar to those at the base of the solar convection zone. The measurements use x rays to volumetrically heat a thin iron sample that is tamped with low-Z materials. The opacity is inferred from spectrally resolved x-ray transmission measurements. Plasma self-emission, tamper attenuation, and temporal and spatial gradients can all potentially cause systematic errors in the measured opacity spectra. In this article we quantitatively evaluate these potential errors with numerical investigations. The analysis exploits computer simulations that were previously found to reproduce the experimentally measured plasma conditions. The simulations, combined with a spectral synthesis model, enable evaluations of individual and combined potential errors in order to estimate their potential effects on the opacity measurement. Lastly, the results show that the errors considered here do not account for the previously observed model-data discrepancies.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-417: Experimental Verification of Robustness Optimized Intensity Modulated Proton Therapy Plans for Craniospinal Irradiation

Purpose: The robustness-optimized intensity modulated proton therapy (RO-IMPT) plans takes into account the setup errors to eliminate the hot/cold spots caused by setup errors at the junction for craniospinal (CSI) irradiation fields. The RO-IMPT requires a large area for the junction of two fields. This work is to verify the treatment planning system calculated dose with introduction of setup errors by two-dimensional (2D) dose measurement. Two fields of total lengths of 45 cm with junctions of 21 cm were measured. Methods: 2D dose distributions in the coronal plane were measured by a commercial 2D ion-chamber-array detector in a cubic solid water phantom. A total spine field consisting of an upper and a lower spine field was measured at the depths of 5.4 and 6.9 cm. The active area for the measurement was 24×24 cm{sup 2}2. The junction and the area outside the junction of the spine fields were measured separately to complete the verification for the total field. Six different amounts of set-up errors along the superior-inferior direction up to 1.4 cm were intentionally applied. The measured dose with and without set-up errors were compared with the calculated dose with and without set-up errors using Gamma-index analysis. 3%/3mm and 2%/2mm dose/distance agreement criteria were used. The point dose at the center of the junction was also compared. Results: For all fields with various introduced setup errors, 100 % of pixels passed the 3%/3mm criteria and on average 96% of pixels passed 2%/2mm criteria. Differences in the measured and calculated doses were within 1.1%. Conclusion: The efficacy of the robustness for the CSI RO-IMPT plans were verified by measurement. The measurement method can be used for patient treatment field specific QA for CSI RO-IMPT plans in our institution in the near future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

VISILIENCE: An Interactive Visualization Framework for Resilience Analysis using Control-Flow Graph

Soft errors have become one of the major concerns for the error resilience of HPC applications, as those errors can cause HPC applications to generate serious outcomes such as Silent Data Corruptions (SDCs). A large body of approaches has been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of the analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to conduct the resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of details, which may create hurdles to compare and explore the resilience of HPC program execution. To this end, we present VISILIENCE, an interactive VISual resILIENCE analysis framework to allow programmers to facilitate the resilience analysis of HPC applications. In particular, VISILIENCE leverages an effective visualization approach Control Flow Graph (CFG) to present a function execution. In addition, three widely-used models for resilience analysis (i.e., Y-Branch, IPAS, and TRIDENT) are seamlessly embedded into the framework for resilience analysis and result comparison. Multiple case studies have been conducted to demonstrate the effectiveness of our proposed framework VISILIENCE.

Jiang, Hailong↗
DOE OSTI2016

SU-C-BRA-03: An Automated and Quick Contour Errordetection for Auto Segmentation in Online Adaptive Radiotherapy

Purpose: To develop a tool that can quickly and automatically assess contour quality generated from auto segmentation during online adaptive replanning. Methods: Due to the strict time requirement of online replanning and lack of ‘ground truth’ contours in daily images, our method starts with assessing image registration accuracy focusing on the surface of the organ in question. Several metrics tightly related to registration accuracy including Jacobian maps, contours shell deformation, and voxel-based root mean square (RMS) analysis were computed. To identify correct contours, additional metrics and an adaptive decision tree are introduced. To approve in principle, tests were performed with CT sets, planned and daily CTs acquired using a CT-on-rails during routine CT-guided RT delivery for 20 prostate cancer patients. The contours generated on daily CTs using an auto-segmentation tool (ADMIRE, Elekta, MIM) based on deformable image registration of the planning CT and daily CT were tested. Results: The deformed contours of 20 patients with total of 60 structures were manually checked as baselines. The incorrect rate of total contours is 49%. To evaluate the quality of local deformation, the Jacobian determinant (1.047±0.045) on contours has been analyzed. In an analysis of rectum contour shell deformed, the higher rate (0.41) of error contours detection was obtained compared to 0.32 with manual check. All automated detections took less than 5 seconds. Conclusion: The proposed method can effectively detect contour errors in micro and macro scope by evaluating multiple deformable registration metrics in a parallel computing process. Future work will focus on improving practicability and optimizing calculation algorithms and metric selection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Ab initio O(N) elongation-counterpoise method for BSSE-corrected interaction energy analyses in biosystems

An Elongation-counterpoise (ELG-CP) method was developed for performing accurate and efficient interaction energy analysis and correcting the basis set superposition error (BSSE) in biosystems. The method was achieved by combining our developed ab initio O(N) elongation method with the conventional counterpoise method proposed for solving the BSSE problem. As a test, the ELG-CP method was applied to the analysis of the DNAs’ inter-strands interaction energies with respect to the alkylation-induced base pair mismatch phenomenon that causes a transition from G⋯C to A⋯T. It was found that the ELG-CP method showed high efficiency (nearly linear-scaling) and high accuracy with a negligibly small energy error in the total energy calculations (in the order of 10{sup −7}–10{sup −8} hartree/atom) as compared with the conventional method during the counterpoise treatment. Furthermore, the magnitude of the BSSE was found to be ca. −290 kcal/mol for the calculation of a DNA model with 21 base pairs. This emphasizes the importance of BSSE correction when a limited size basis set is used to study the DNA models and compare small energy differences between them. In this work, we quantitatively estimated the inter-strands interaction energy for each possible step in the transition process from G⋯C to A⋯T by the ELG-CP method. It was found that the base pair replacement in the process only affects the interaction energy for a limited area around the mismatch position with a few adjacent base pairs. From the interaction energy point of view, our results showed that a base pair sliding mechanism possibly occurs after the alkylation of guanine to gain the maximum possible number of hydrogen bonds between the bases. In addition, the steps leading to the A⋯T replacement accompanied with replications were found to be unfavorable processes corresponding to ca. 10 kcal/mol loss in stabilization energy. The present study indicated that the ELG-CP method is promising for performing effective interaction energy analyses in biosystems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Infrared spectroscopic method for uranium isotopic analysis

A high performance infrared (HPIR) system was developed and demonstrated for the infrared absorption analysis of {sup 235}U and {sup 238}U isotopes in uranium hexafluoride gas samples. Sweeping the quantum cascade laser light source over the spectral range and sampling via a high-rate analog-to-digital converter provided 0.0005 cm-1 spectral resolution, which allowed for high-precision measurements of the isotopic peak shift. A data analysis method was developed using principal component analysis to predict the isotope weight % content of {sup 235}U. The HPIR precision, accuracy, and error were evaluated for a wide range of isotopic ratio samples (0.287 – 93.7 weight % {sup 235}U), and the results were compared to the International Target Values (ITVS) set forth by the International Atomic Energy Agency (IAEA) for non-destructive and destructive analytical techniques. The method meets or surpasses the IAEA ITVs for non-destructive analysis of samples with isotopic content of depleted to highly enriched. The results also demonstrated the capability of the HPIR system to correctly predict the {sup 235}U weight % content of a mislabeled sample whose isotopic distribution was validated by mass spectroscopic measurements. The HPIR measurement is nondestructive and, thus, allows for confirmatory analyses of the exact sample at a designated IAEA lab if higher-resolution or a certified analysis is needed. (author)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

diBELLA: Distributed Long Read to Long Read Alignment

© 2019 ACM. We present a parallel algorithm and scalable implementation for genome analysis, specifically the problem of finding overlaps and alignments for data from "third generation" long read sequencers [29]. While long sequences of DNA offer enormous advantages for biological analysis and insight, current long read sequencing instruments have high error rates and therefore require different approaches to analysis than their short read counterparts. Our work focuses on an efficient distributed-memory parallelization of an accurate single-node algorithm for overlapping and aligning long reads. We achieve scalability of this irregular algorithm by addressing the competing issues of increasing parallelism, minimizing communication, constraining the memory footprint, and ensuring good load balance. The resulting application, diBELLA, is the first distributed memory overlapper and aligner specifically designed for long reads and parallel scalability. We describe and present analyses for high level design trade-offs and conduct an extensive empirical analysis that compares performance characteristics across state-of-the-art HPC systems as well as a commercial cloud architectures, highlighting the advantages of state-of-the-art network technologies.

Ellis, Marquita↗
DOE OSTI2020

Distributed Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper (DiBELLA) v1.0.0

We present a parallel algorithm and scalable implementation for genome analysis, specifically the problem of finding overlaps and alignments for data from "third generation" long read sequencers. While long sequences of DNA offer enormous advantages for biological analysis and insight, current long read sequencing instruments have high error rates and therefore require different approaches to analysis than their short read counterparts. Our work focuses on an efficient distributed-memory parallelization of an accurate single-node algorithm for overlapping and aligning long reads. We achieve scalability of this irregular algorithm by addressing the competing issues of increasing parallelism, minimizing communication, constraining the memory footprint, and ensuring good load balance. The resulting application, DiBELLA, is the first distributed memory overlapper and aligner specifically designed for long reads and parallel scalability.

Ellis, Marquita↗
DOE OSTI2023

Applications of the Dulmage–Mendelsohn decomposition for debugging nonlinear optimization problems

Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering practitioners, but programming an optimization problem based on a complex electrical, mechanical, or chemical process is a time-consuming and error-prone activity. Therefore, there is a need for model analysis and debugging tools that can detect and diagnose modeling errors. One such tool is the Dulmage–Mendelsohn decomposition, which identifies structurally under- and over-determined subsets in systems of equations and variables by partitioning the bipartite graph of the system. This work provides the necessary background to understand the Dulmage–Mendelsohn decomposition and its application to the analysis of nonlinear optimization problems, demonstrates its use in diagnosing a variety of modeling errors, and introduces software implementations for analyzing nonlinear optimization problems in the Pyomo and JuMP algebraic modeling languages.

42 ENGINEERING↗
DOE OSTI2016

Controlling excited-state contamination in nucleon matrix elements

We present a detailed analysis of methods to reduce statistical errors and excited-state contamination in the calculation of matrix elements of quark bilinear operators in nucleon states. All the calculations were done on a 2+1-flavor ensemble with lattices of size 32 3 × 64 generated using the rational hybrid Monte Carlo algorithm at 𝑎 = 0.081 fm and with 𝑀 𝜋 = 312 MeV. The statistical precision of the data is improved using the all-mode-averaging method. We compare two methods for reducing excited-state contamination: a variational analysis and a 2-state fit to data at multiple values of the source-sink separation 𝑡 sep . We show that both methods can be tuned to significantly reduce excited-state contamination and discuss their relative advantages and cost effectiveness. Here a detailed analysis of the size of source smearing used in the calculation of quark propagators and the range of values of 𝑡 sep needed to demonstrate convergence of the isovector charges of the nucleon to the 𝑡 sep → ∞ estimates is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Spectral characteristics of background error covariance and multiscale data assimilation

The spatial resolutions of numerical atmospheric and oceanic circulation models have steadily increased over the past decades. Horizontal grid spacing down to the order of 1 km is now often used to resolve cloud systems in the atmosphere and sub-mesoscale circulation systems in the ocean. These fine resolution models encompass a wide range of temporal and spatial scales, across which dynamical and statistical properties vary. In particular, dynamic flow systems for small scales can become spatially localized and temporarily intermittent. An analysis shows that the background correlation length scale is larger than 75 km for streamfunctions, even for a 2-km resolution model, and larger than 25 km for water vapor mixing ratios. The theoretical analyses suggest that such correlation length scales prevent the currently used data assimilation schemes from constraining spatial scales smaller than 150 km for streamfunctions and 50 km for water vapor mixing ratios. These results highlight the necessity of fundamentally modifying the currently used data assimilation algorithm for assimilating high-resolution observations into the aforementioned fine resolution models. A multiscale methodology based on scale decomposition is suggested, and challenges are discussed.

Li, Zhijin↗
DOE OSTI2023

BeyondPlanck: XII. Cosmological parameter constraints with end-to-end error propagation

We present cosmological parameter constraints estimated using the Bayesian BEYONDPLANCK analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data onto final cosmological parameters. As a first demonstration of the method, we analyzed time-ordered Planck LFI observations, combined with selected external data (WMAP 33–61 GHz, Planck HFI DR4 353 and 857 GHz, and Haslam 408 MHz) in the form of pixelized maps that are used to break critical astrophysical degeneracies. Overall, all the results are generally in good agreement with previously reported values from Planck 2018 and WMAP, with the largest relative difference for any parameter amounting about 1σ when considering only temperature multipoles between 30 ≤ ℓ ≤ 600. In cases where there are differences, we note that the BEYONDPLANCK results are generally slightly closer to the high-ℓ HFI-dominated Planck 2018 results than previous analyses, suggesting slightly less tension between low and high multipoles. Using low-ℓ polarization information from LFI and WMAP, we find a best-fit value of τ = 0.066 ± 0.013, which is higher than the low value of τ = 0.052 ± 0.008 derived from Planck 2018 and slightly lower than the value of 0.069 ± 0.011 derived from the joint analysis of official LFI and WMAP products. Most importantly, however, we find that the uncertainty derived in the BEYONDPLANCK processing is about 30 % greater than when analyzing the official products, after taking into account the different sky coverage. We argue that this uncertainty is due to a marginalization over a more complete model of instrumental and astrophysical parameters, which results in more reliable and more rigorously defined uncertainties. We find that about 2000 Monte Carlo samples are required to achieve a robust convergence for a low-resolution cosmic microwave background (CMB) covariance matrix with 225 independent modes, and producing these samples takes about eight weeks on a modest computing cluster with 256 cores.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.

17 WIND ENERGY↗
DOE OSTI2014

Low-Cost Spectral Sensor Development Description

Solar spectral data for all parts of the US is limited due in part to the high cost of commercial spectrometers. Solar spectral information is necessary for accurate photovoltaic (PV) performance forecasting, especially for large utility-scale PV installations. A low-cost solar spectral sensor would address the obstacles and needs. In this report, a novel low-cost, discrete-band sensor device, comprised of five narrow-band sensors, is described. The hardware is comprised of commercial-off-the-shelf components to keep the cost low. Data processing algorithms were developed and are being refined for robustness. PV module short-circuit current ($I_{sc}$) prediction methods were developed based on interaction-terms regression methodology and spectrum reconstruction methodology for computing $I_{sc}$. The results suggest the computed spectrum using the reconstruction method agreed well with the measured spectrum from the wide-band spectrometer (RMS error of 38.2 W/m 2 -nm). Further analysis of computed $I_{sc}$ found a close correspondence of 0.05 A RMS error. The goal is for ubiquitous adoption of the low-cost spectral sensor in solar PV and other applications such as weather forecasting.

Armijo, Kenneth Miguel↗
DOE OSTI2019

Sub-divertor fuel isotopic content detection limit for JET and its impact on ICRF core heating and DTE2 operation

The ability to detect and control fuel isotopic content down to a 1% concentration level is greatly important for the upcoming JET DTE2 campaign, as well as its associated TT and DD phases. A reduction of H minority concentration even from 2% down to 1% is shown in this paper to have significant impact on the effectiveness of ion cyclotron range of frequencies core heating, while the ability to maintain T or D concentration at or below 1% is critical to limiting fusion neutron generation in the DD and TT phases, correspondingly. The sub-divertor measurement of (global) isotopic concentration, based on Penning-activated optical spectroscopy, can deliver minimally this 1% detection for DTE2 as long as light collection from the Penning emission can be optimized and gradual window transmission deterioration can be minimized. Here, this is simulated with a statistical analysis developed to understand the uncertainty sources in the JET DTE1 data, as well as to guide the optimization of an upgraded, fuel-isotopic content (and helium-ash concentration) gas analysis system for the JET divertor in preparation for DTE2. While this random error can be reduced to allow measurement substantially below 1% concentration, analysis also shows a systematic error of up to 1% understood to be due to plasma–surface interactions in the Penning excitation, suggesting that 1% may still be the low-end limit for the sub-divertor measurement, unless a Penning-source conditioning approach is also developed.

fusion fuel isotopic content↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗