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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 1,603 records · Page 89
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DOE OSTI2014

Feasibility study of using statistical process control to customized quality assurance in proton therapy

Purpose: To evaluate and improve the reliability of proton quality assurance (QA) processes and, to provide an optimal customized tolerance level using the statistical process control (SPC) methodology. Methods: The authors investigated the consistency check of dose per monitor unit (D/MU) and range in proton beams to see whether it was within the tolerance level of the daily QA process. This study analyzed the difference between the measured and calculated ranges along the central axis to improve the patient-specific QA process in proton beams by using process capability indices. Results: The authors established a customized tolerance level of ±2% for D/MU and ±0.5 mm for beam range in the daily proton QA process. In the authors’ analysis of the process capability indices, the patient-specific range measurements were capable of a specification limit of ±2% in clinical plans. Conclusions: SPC methodology is a useful tool for customizing the optimal QA tolerance levels and improving the quality of proton machine maintenance, treatment delivery, and ultimately patient safety.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-D-201-06: Random Walk Algorithm Seed Localization Parameters in Lung Positron Emission Tomography (PET) Images

Purpose: The objective of this study was to find the best seed localization parameters in random walk algorithm application to lung tumor delineation in Positron Emission Tomography (PET) images. Methods: PET images suffer from statistical noise and therefore tumor delineation in these images is a challenging task. Random walk algorithm, a graph based image segmentation technique, has reliable image noise robustness. Also its fast computation and fast editing characteristics make it powerful for clinical purposes. We implemented the random walk algorithm using MATLAB codes. The validation and verification of the algorithm have been done by 4D-NCAT phantom with spherical lung lesions in different diameters from 20 to 90 mm (with incremental steps of 10 mm) and different tumor to background ratios of 4:1 and 8:1. STIR (Software for Tomographic Image Reconstruction) has been applied to reconstruct the phantom PET images with different pixel sizes of 2×2×2 and 4×4×4 mm{sup 3}. For seed localization, we selected pixels with different maximum Standardized Uptake Value (SUVmax) percentages, at least (70%, 80%, 90% and 100%) SUVmax for foreground seeds and up to (20% to 55%, 5% increment) SUVmax for background seeds. Also, for investigation of algorithm performance on clinical data, 19 patients with lung tumor were studied. The resulted contours from algorithm have been compared with nuclear medicine expert manual contouring as ground truth. Results: Phantom and clinical lesion segmentation have shown that the best segmentation results obtained by selecting the pixels with at least 70% SUVmax as foreground seeds and pixels up to 30% SUVmax as background seeds respectively. The mean Dice Similarity Coefficient of 94% ± 5% (83% ± 6%) and mean Hausdorff Distance of 1 (2) pixels have been obtained for phantom (clinical) study. Conclusion: The accurate results of random walk algorithm in PET image segmentation assure its application for radiation treatment planning and diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-294: The Analysis of Gamma Criteria for Delta4 Dosimetry Using Statistical Process Control

Purpose: To evaluate the sensitivity of gamma criteria for patient-specific volumetric modulated arc therapy(VMAT) quality assurance of the Delta{sup 4} dosimetry program using the statistical process control(SPC) methodology. Methods: The authors selected 20 patient-specific VMAT QA cases which were undertaken MapCHECK and ArcCHECK with gamma pass rate better than 97%. The QAs data were collected Delta4 Phantom+ and Elekta Agility six megavolts without using an angle incrementer. The gamma index(GI) were calculated in 2D planes with normalizing deviation to local dose(local gamma). The sensitivity of the GI methodology using criterion of 3%/3mm, 3%/2mm and 2%/3mm was analyzed with using process acceptability indices. We used local confidence(LC) level, the upper control limit(UCL) and lower control limit(LCL) of I-MR chart for process capability index(Cp) and a process acceptability index (Cpk). Results: The lower local confidence levels of 3%/3mm, 3%/2mm and 2%/3mm were 92.0%, 83.6% and 78.8% respectively. All of the calculated Cp and Cpk values that used LC level were under 1.0 in this study. The calculated LCLs of I-MR charts were 89.5%, 79.0% and 70.5% respectively. These values were higher than 1.0 which means good quality of QA. For the generally used lower limit of 90%, we acquired over 1.3 of Cp value for the gamma index of 3%/3mm and lower than 1.0 in the rest of GI. Conclusion: We applied SPC methodology to evaluate the sensitivity of gamma criteria and could see the lower control limits of VMAT QA for the Delta 4 dosimetry and could see that Delta 4 phantom+ dosimetry more affected by the position error and the I-MR chart derived values are more suitable for establishing lower limits. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2015R1D1A1A01060463)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.

bioinformatics↗
DOE OSTI2012

REPORT FOR COMMERCIAL GRADE NICKEL CHARACTERIZATION AND BENCHMARKING

Oak Ridge Associated Universities (ORAU), under the Oak Ridge Institute for Science and Education (ORISE) contract, has completed the collection, sample analysis, and review of analytical results to benchmark the concentrations of gross alpha-emitting radionuclides, gross beta-emitting radionuclides, and technetium-99 in commercial grade nickel. This report presents methods, change management, observations, and statistical analysis of materials procured from sellers representing nine countries on four continents. The data suggest there is a low probability of detecting alpha- and beta-emitting radionuclides in commercial nickel. Technetium-99 was not detected in any samples, thus suggesting it is not present in commercial nickel.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Solvent Hold Tank Sample Results For MCU-13-814, MCU-13-815, MCU-13-816, MCU-13,817, MCU-13-818 And MCU-13-819: Quarterly Sample From May 2013

Savannah River National Laboratory (SRNL) analyzed solvent samples from Modular Caustic-Side Solvent Extraction Unit (MCU) in support of continuing operations. A quarterly analysis of the solvent is required to maintain solvent composition within specifications. Analytical results of the analyses of Solvent Hold Tank (SHT) samples MCU-13-814, MCU-13-815, MCU-13-816, MCU-13-817, MCU-13-818 and MCU-13-819 received May 28, 2013 are reported. The results show that the solvent at MCU does not require an Isopar® L addition, but it will require addition of trioctylamine despite of the 272 g of TOA that was added to the solvent on June 5, 2013 based on the solvent containing a TOA level of 45% of nominal. A new TOA analysis method (HCl titration) has been used and its output was statistically similar to the results from the SVOA-TOA method. This method provides an independent method for measuring TOA and TiDG in MCU-NG solvent. An impurity containing a tert-butyl group was detected in the solvent and further analytical analysis is needed to identify it. SRNL recommends determining the impact of this impurity on the mass transfer ability of the solvent. SRNL also analyzed the SHT sample for 137 Cs content and determined the measured value is above the results observed from the January 2013.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Statistical and Machine-Learning Classifier Framework to Improve Pulse Shape Discrimination System Design

Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-­realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-­building elements and their functions in a fully-­designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Heavy flavor decay of Zγ at CDF

Diboson production is an important and frequently measured parameter of the Standard Model. This analysis considers the previously neglected p$\bar{p}$ →Z γ→ b$\bar{b}$ channel, as measured at the Collider Detector at Fermilab. Using the entire Tevatron Run II dataset, the measured result is consistent with Standard Model predictions, but the statistical error associated with this method of measurement limits the strength of this correlation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Renewable Energy Zones for the Africa Clean Energy Corridor

Multi-criteria Analysis for Planning Renewable Energy (MapRE) is a study approach developed by the Lawrence Berkeley National Laboratory with the support of the International Renewable Energy Agency (IRENA). The approach combines geospatial, statistical, energy engineering, and economic methods to comprehensively identify and value high-quality wind, solar PV, and solar CSP resources for grid integration based on techno-economic criteria, generation profiles (for wind), and socio-environmental impacts. The Renewable Energy Zones for the Africa Clean Energy Corridor study sought to identify and comprehensively value high-quality wind, solar photovoltaic (PV), and concentrating solar power (CSP) resources in 21 countries in the East and Southern Africa Power Pools to support the prioritization of areas for development through a multi-criteria planning process. These countries include Angola, Botswana, Burundi, Djibouti, Democratic Republic of Congo, Egypt, Ethiopia, Kenya, Lesotho, Libya, Malawi, Mozambique, Namibia, Rwanda, South Africa, Sudan, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe. The study includes the methodology and the key results including renewable energy potential for each region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Whisper Use of Nuclear Data Covariances [Slides]

Whisper is statistical analysis code using sensitivity/uncertainty-based methods to determine baseline upper subcritical limit (USL) for nuclear criticality safety. Features of Whisper 1.1 include: GLLS method implemented to compute adjusted covariance based on current benchmark suite (1,100+ ICSBEP models), BLO “low-fidelity” covariance data used (44 energy groups), adjusted covariance is pre-computed and saved, and adjusted cross sections are NOT computed. Potential future efforts include: an extension to include angular distributions in benchmark selection and in GLLS adjustment, a move toward more modern covariance data (ENDF/B-VIII.0) and different group structure, and compute and store adjusted cross sections (trivial).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Nuclear Material Control & Accounting for Pebble Bed Reactors (FY 2023 Summary Report)

This report discusses the work done under the US Department of Energy NE-5 Advanced Reactor Safeguards and Security Program during FY 2023. It provides a summary of material control and accounting (MC&A) for pebble bed reactors (PBRs) and addresses some of the main challenges with current PBR MC&A approaches that will inform safeguards and security by design efforts. The efforts to date have focused on tristructural isotropic (TRISO) pebble fuel material accounting and control including working with partners in industry, loss and production of nuclear material as part of reactor operations, burnup modeling and measurements, uncertainty quantifications for such modeling and measurements, statistical approaches needed, and measurement methods. The unique fuel management and utilization in a PBR, where the fuel in spherical form is introduced and circulates through the reactor, poses special challenges for MC&A. This contrasts with traditional water-cooled reactors in which the fuel is contained in large assemblies and can be easily identified and counted. Even online fueled reactors, such as the CANDU reactors (none of which operate in the United States), are significantly different because the fuel is still contained in relatively large assemblies, is uniquely identified, and the number of assemblies that pass through the core on an annual basis is much fewer than the hundreds of thousands that circulate in a PBR, none of which are uniquely identified. Additionally, the nature of the TRISO fuel results in very low heavy metal loading with each pebble containing less than 10 g of uranium and on the order of less than 1 g of fissile material. This low fuel density and the robustness of the TRISO particles are major features of the TRISO fuel from a safety basis as each TRISO particle and pebble acts as a containment for the nuclear material and fission products during normal and accident conditions. This also results in very low plutonium loading per pebble during normal operations, which is on the order of 0.1 g at full burnup. A major feature of PBRs is that they will allow for significantly higher burnup, on the order of 160 GWd/THM compared to the burnup of traditional LWRs, which is on the order of 45 GWd/THM. This is achieved by monitoring the pebbles as they circulate through the reactor and allowing them to be reintroduced into the core until the desired burnup is achieved and they are removed from the reactor and enter the spent fuel storage areas.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Near Infrared Spectroscopy Calibration for Wood Chemistry: Which Chemometric Technique Is Best for Prediction and Interpretation?

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

09 BIOMASS FUELS↗
DOE OSTI2016

INFERRING THE GALACTIC POTENTIAL WITH GAIA AND FRIENDS: SYNERGIES WITH OTHER SURVEYS

In the coming decade, the Gaia satellite will precisely measure the positions and velocities of millions of stars in the Galactic halo, including stars in many tidal streams. These streams, the products of hierarchical accretion of satellite galaxies by the Milky Way (MW), can be used to infer the Galactic gravitational potential thanks to their initial compactness in phase space. Plans for observations to extend Gaia’s radial velocity (RV) measurements to faint stars, and to determine precise distances to RR Lyrae in streams, would further extend the power of Gaia’s kinematic catalog to characterize the MW’s potential at large Galactocentric distances. In this work I explore the impact of these extra data on the ability to fit the potential using the method of action clustering, which statistically maximizes the information content (clumpiness) of the action space of tidal streams, eliminating the need to determine stream membership for individual stars. Using a mock halo in a toy spherical potential, updated post-launch error models for Gaia, and estimates for RV and distance errors for the tracers to be followed up, I show that combining either form of additional information with the Gaia catalog greatly reduces the bias in determining the scale radius and total mass of the Galaxy compared to the use of Gaia data alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Rapid Analysis of Carbohydrates in Bioprocess Samples: An Evaluation of the CarboPac SA10 for HPAE-PAD Analysis by Interlaboratory Comparison

A technique for carbohydrate analysis for bioprocess samples has been developed, providing reduced analysis time compared to current practice in the biofuels R&D community. The Thermofisher CarboPac SA10 anion-exchange column enables isocratic separation of monosaccharides, sucrose and cellobiose in approximately 7 minutes. Additionally, use of a low-volume (0.2 mL) injection valve in combination with a high-volume detection cell minimizes the extent of sample dilution required to bring sugar concentrations into the linear range of the pulsed amperometric detector (PAD). Three laboratories, representing academia, industry, and government, participated in an interlaboratory study which analyzed twenty-one opportunistic samples representing biomass pretreatment, enzymatic saccharification, and fermentation samples. The technique's robustness, linearity, and interlaboratory reproducibility were evaluated and showed excellent-to-acceptable characteristics. Additionally, quantitation by the CarboPac SA10/PAD was compared with the current practice method utilizing a HPX-87P/RID. While these two methods showed good agreement a statistical comparison found significant quantitation difference between them, highlighting the difference between selective and universal detection modes.

09 BIOMASS FUELS↗
DOE OSTI2012

Onsite Gaseous Centrifuge Enrichment Plant UF6 Cylinder Destructive Analysis

The IAEA safeguards approach for gaseous centrifuge enrichment plants (GCEPs) includes measurements of gross, partial, and bias defects in a statistical sampling plan. These safeguard methods consist principally of mass and enrichment nondestructive assay (NDA) verification. Destructive assay (DA) samples are collected from a limited number of cylinders for high precision offsite mass spectrometer analysis. DA is typically used to quantify bias defects in the GCEP material balance. Under current safeguards measures, the operator collects a DA sample from a sample tap following homogenization. The sample is collected in a small UF6 sample bottle, then sealed and shipped under IAEA chain of custody to an offsite analytical laboratory. Current practice is expensive and resource intensive. We propose a new and novel approach for performing onsite gaseous UF6 DA analysis that provides rapid and accurate assessment of enrichment bias defects. DA samples are collected using a custom sampling device attached to a conventional sample tap. A few micrograms of gaseous UF6 is chemically adsorbed onto a sampling coupon in a matter of minutes. The collected DA sample is then analyzed onsite using Laser Ablation Absorption Ratio Spectrometry-Destructive Assay (LAARS-DA). DA results are determined in a matter of minutes at sufficient accuracy to support reliable bias defect conclusions, while greatly reducing DA sample volume, analysis time, and cost.

Uranium Enrichment Laser Safeguards↗
DOE OSTI2023

Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact Evaluation

This chapter presents results from the Big Data analysis framework to improve power system situational awareness and system reliability. For this purpose, a dataset with real-world phasor measurement unit data and historical transmission system outage data has been created and used to carry out the analysis. Several statistical analysis and machine learning methods have been developed and implemented for event and anomaly detection and modeling. Detection and analysis results for actual examples of power system events are presented. Finally, data-driven characterization and risk assessment methods for weather-related extremes in power systems are developed and demonstrated on the Bonneville Power Administration system. These applications demonstrate the capability of Machine Learning (ML) methods to monitor system abnormalities, to predict system events, and to characterize the impact of extreme events on power grid

data mining, power grid, machine learning, anomaly↗
DOE OSTI2012

Mixture-process variable approach to optimize a microemulsion electrokinetic chromatography method for the quality control of a nutraceutical based on coenzyme Q10

In recent years, multivariate optimization has played an increasing role in analytical method development. ICH guidelines recommend using statistical design of experiments to identify the design space, in which multivariate combinations of composition variables and process variables have been demonstrated to provide quality results. Considering a microemulsion electrokinetic chromatography method (MEEKC), the performance of the electrophoretic run depends on the proportions of mixture components (MCs) of the microemulsion and on the values of process variables (PVs). In the present work, for the first time in the literature, a mixture-process variable (MPV) approach was applied to optimize a MEEKC method for the analysis of coenzyme Q10 (Q10), ascorbic acid (AA), and folic acid (FA) contained in nutraceuticals. The MCs (buffer, surfactant-cosurfactant, oil) and the PVs (voltage, buffer concentration, buffer pH) were simultaneously changed according to a MPV experimental design. A 62-run MPV design was generated using the I-optimality criterion, assuming a 46-term MPV model allowing for special-cubic blending of the MCs, quadratic effects of the PVs, and some MC-PV interactions. The obtained data were used to develop MPV models that express the performance of an electrophoretic run (measured as peak efficiencies of Q10, AA, and FA) in terms of the MCs and PVs. Contour and perturbation plots were drawn for each of the responses. Finally, the MPV models and criteria for the peak efficiencies were used to develop the design space and an optimal subregion (i.e., the settings of the mixture MCs and PVs that satisfy the respective criteria), as well as a unique optimal combination of MCs and PVs.

Coenzyme Q10↗