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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 181 records · Page 10

The National Climate Database (NCDB): An Unbiased 100-Year Dataset for PV Modeling

In this study, we develop a statistical technique to downscale the future projection of solar irradiance for photovoltaics (PV) energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the downscaling method, a summary of the 100-year dataset from 2001-2100, and future extension of this research.

bias correction↗

8760-Based Method for Representing Variable Generation Capacity Value in Capacity Expansion Models

Capacity expansion models (CEMs) are widely used to evaluate the least-cost portfolio of electricity generators, transmission, and storage needed to reliably serve load over many years or decades. CEMs can be computationally complex and are often forced to estimate key parameters using simplified methods to achieve acceptable solve times or for other reasons. In this paper, we discuss one of these parameters -- capacity value (CV). We first provide a high-level motivation for and overview of CV. We next describe existing modeling simplifications and an alternate approach for estimating CV that utilizes hourly '8760' data of load and VG resources. We then apply this 8760 method to an established CEM, the National Renewable Energy Laboratory's (NREL's) Regional Energy Deployment System (ReEDS) model (Eurek et al. 2016). While this alternative approach for CV is not itself novel, it contributes to the broader CEM community by (1) demonstrating how a simplified 8760 hourly method, which can be easily implemented in other power sector models when data is available, more accurately captures CV trends than a statistical method within the ReEDS CEM, and (2) providing a flexible modeling framework from which other 8760-based system elements (e.g., demand response, storage, and transmission) can be added to further capture important dynamic interactions, such as curtailment.

14 SOLAR ENERGY↗

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

97 MATHEMATICS AND COMPUTING↗

SU-F-P-64: The Impact of Plan Complexity Parameters On the Plan Quality and Deliverability of Volumetric Modulated Arc Therapy with Canonical Correlation Analysis

Purpose: To evaluate the impact of complexity indices on the plan quality and deliverability of volumetric modulated arc therapy (VMAT), and to determine the most significant parameters in the generation of an ideal VMAT plan. Methods: A multi-dimensional exploratory statistical method, canonical correlation analysis (CCA) was adopted to study the correlations between VMAT parameters of complexity, quality and deliverability, as well as their contribution weights with 32 two-arc VMAT nasopharyngeal cancer (NPC) patients and 31 one-arc VMAT prostate cancer patients. Results: The MU per arc (MU/Arc) and MU per control point (MU/CP) of NPC were 337.8±25.2 and 3.7±0.3, respectively, which were significantly lower than those of prostate cancer patients (MU/Arc : 506.9±95.4, MU/CP : 5.6±1.1). The plan complexity indices indicated that two-arc VMAT plans were more complex than one-arc VMAT plans. Plan quality comparison confirmed that one-arc VMAT plans had a high quality than two-arc VMAT plans. CCA results implied that plan complexity parameters were highly correlated with plan quality with the first two canonical correlations of 0.96, 0.88 (both p<0.001) and significantly correlated with deliverability with the first canonical correlation of 0.79 (p<0.001), plan quality and deliverability was also correlated with the first canonical correlation of 0.71 (p=0.02). Complexity parameters of MU/CP, segment area (SA) per CP, percent of MU/CP less 3 and planning target volume (PTV) were weighted heavily in correlation with plan quality and deliveability . Similar results obtained from individual NPC and prostate CCA analysis. Conclusion: Relationship between complexity, quality, and deliverability parameters were investigated with CCA. MU, SA related parameters and PTV volume were found to have strong effect on the plan quality and deliverability. The presented correlation among different quantified parameters could be used to improve the plan quality and the efficiency of the radiotherapy process when creating a complex VMAT plan.

60 APPLIED LIFE SCIENCES↗

AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

36 MATERIALS SCIENCE↗

A criterion for establishing life limits

The development of a rigorous statistical method that would utilize hardware-demonstrated reliability to evaluate hardware capability and provide ground rules for safe flight margin is discussed. A statistical-based method using the Weibull/Weibayes cumulative distribution function is described. Its advantages and inadequacies are pointed out. Another, more advanced procedure, Single Flight Reliability (SFR), determines a life limit which ensures that the reliability of any single flight is never less than a stipulated value at a stipulated confidence level. Application of the SFR method is illustrated.

Skopp, G. H.↗

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless if the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the NASA PRA and present a future dynamic option for software and PRA. Space Launch Vehicle Software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data, respectively. Software uncertainty will also be discussed. We determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Novack, Steven↗

Estimating Software Reliability for Space Launch Vehicles in Probabilistic Risk Assessment (PRA)

It is acutely recognized in the Probabilistic Risk Assessment (PRA) field that software plays a defining role in overall system reliability for all modern systems across a wide variety of industries. Regardless of whether the software is embedded firmware for working components or elements, part of a Human-Machine-Interface, or automated command and control logic, the success of the software to fulfill its function under nominal and off-nominal environments will be a dominant contributor to system reliability. It is also recognized that software reliability prediction and estimation is one of the more challenging and questionable aspects of any PRA or system analyses due to the nature of software and its integration with physics based systems. Irrespective of this dichotomy, any incorporation of software reliability methods requires that the contributions are accountable, quantitative, and tractable. This paper provides a brief overview of software reliability methods, establishes some minimum requirements that the methods should incorporate for completeness, and provides a logic structure for applying software reliability. Model resolution will be discussed that supports current testing plans and trade studies. We will provide initial recommendations for use in the National Aeronautics and Space Administration (NASA) PRA and present a future dynamic option for software and PRA. Space Launch Vehicle software is recognized to be reliable in static conditions, yet relatively vulnerable to a set of failure modes in changing environments/flight phases. Two quantitative methods were chosen to incorporate software reliability into a Space Launch Vehicle PRA accounting for phase adjustments. One method predicts latent software failure using statistical methods, and the second provides estimates of coding errors and software operating system failures based on test and historical data. Software uncertainty will also be discussed. It is determined that recommendations for PRA software reliability should be modeled at the software module level where multiple software components compose a module and combinations of the software architecture can lead to a functional failure.

Steven D. Novack↗

Low Count and Background Radionuclides Analysis - 20488

The US Department of Energy (DOE) is often faced with the need to evaluate radionuclides at low concentrations. When site sample data are likely to be close to threshold activity concentrations of interest, then the means by which the radiochemical analysis is performed and reported is critical. This situation can occur when differentiating from zero (presence/absence) for radionuclides that do not occur naturally, close comparison with environmental background for naturally occurring radionuclides, close comparison with a risk- or dose-based threshold concentrations of interest, or even comparisons across studies. There are several analytical issues that are of concern, but the two that appear to cause incorrect decisions to be made most often involve establishing detection limits and subtracting ambient background conditions in the laboratory. These issues are not critical when radionuclide activity concentrations are large relative to thresholds of concern, but they seem to be poorly understood when it matters. When the comparisons are important and are likely to be close to a threshold of interest, then the general contract with the analytical laboratories needs to be changed so that the right or appropriate data are obtained. The concern is that important decisions are made incorrectly more often as greater scrutiny is placed on DoE's radionuclide cleanup or monitoring decisions by the public and other stakeholders. Examples are presented of problems that have been observed for different projects, both within and outside the realm of DOE and NRC remediation and radioactive waste disposal problems, and solutions are offered that should lead to better data from which important decisions need to be made. The first example is from Los Alamos National Laboratory (LANL) and involves radionuclide concentrations in soil and rock beneath LANL's Material Disposal Area (MDA) G. An initial review of the data led to a conclusion that americium and plutonium are a long way present beneath MDA G. A more thorough review of the data that accounted properly for ambient background and the detection limits that had been established led to the opposite conclusion. Another example is from the Nevada National Security Site where tritium results from one of the wells were unexpectedly high. Proper understanding and analysis of ambient background led to the conclusion that the increased concentrations were not so obvious, and that a different contract with the analytical laboratory was needed to provide more appropriate data to support a better determination. Other examples are used from regulatory review of projects in Nevada, where background levels and secular equilibrium for naturally occurring radionuclides are not established correctly because of analytical issues. The same basic issues have also been found to create difficulties analyzing historical data from the West Valley Demonstration Project. There is evidence in the data that the apparent lack of secular equilibrium where it is expected to exist is related to ambient background subtraction or other analytical issues. A final example is presented for analysis of Tc-99 in samples of depleted uranium. In this case, two different studies that were performed only three months apart provide quite different results. The US Environmental Protection Agency (EPA) established the data quality objectives (DQO) process in the mid-1980's to establish decision performance criteria for data collection. EPA guidance (EPA G-4, for example) clearly distinguishes between DQOs and measurement performance objectives (MQOs) that should be addressed for laboratory analysis of samples. The language of DQOs and MQOs has become confused over time it seems, and the subsequent effects seem to include a lack of attention to decision performance and a routine approach to measurement quality. In order to better address radionuclide sample analysis when the concentrations are close to thresholds of concern, which might be zero for some radionuclides, background for others, and risk-based thresholds for yet others, it is important that routine laboratory analysis methods are adjusted, and that the project team and the laboratory work closely together to ensure that the data meets the MQO requirements of laboratory analysis and reporting of results, and that the MQOs effectively support project-specific DQOs. This basic approach will be applied in Los Alamos in the coming year to the collection of moisture data from underneath MDA T that will be analyzed for americium and neptunium isotopes. Proper understanding of the radiochemistry methods and reporting, and of appropriate statistical methods is critical to the success of such projects, ensuring that the right decisions are made. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

Learning process mapping heuristics under stochastic sampling overheads

A statistical method was developed previously for improving process mapping heuristics. The method systematically explores the space of possible heuristics under a specified time constraint. Its goal is to get the best possible heuristics while trading between the solution quality of the process mapping heuristics and their execution time. The statistical selection method is extended to take into consideration the variations in the amount of time used to evaluate heuristics on a problem instance. The improvement in performance is presented using the more realistic assumption along with some methods that alleviate the additional complexity.

Ieumwananonthachai, Arthur↗

Feature Selection and Inferential Procedures for Video Data [Slides]

With the rise of electronic and high-dimensional data, new and innovative feature detection and statistical methods are required to perform accurate and meaningful statistical analysis of these datasets that provide unique statistical challenges. In the area of feature detection, much of the recent feature detection research in the computer vision community has focused on deep learning methods, which require large amounts of labeled training data. However, in many application areas, training data is very limited and often difficult to obtain. We develop methods for fast, unsupervised, precise feature detection for video data based on optical flows, edge detection, and clustering methods. We also use pretrained neural networks and interpretable linear models to extract features using very limited training data. In the area of statistics, while high-dimensional data analysis has been a main focus of recent statistical methodological research, much focus has been on populations of high-dimensional vectors, rather than populations of high-dimensional tensors, which are three-dimensional arrays that can be used to model dependent images, such as images taken of the same person or ripped video frames. Our feature detection method is a non-model-based method that fusses information from dense optical flow, raw image pixels, and frame differences to generate detections. Our hypothesis testing methods are based on the assumption that dependent images are concatenated into a tensor that follows a tensor normal distribution, and from this assumption, we derive likelihood-ratio, score, and regression-based tests for one- and multiple-sample testing problems. Our methods will be illustrated on simulated and real datasets. We conclude this report with comments on the relationship between feature detection and hypothesis testing methods.

97 MATHEMATICS AND COMPUTING↗

Decoding the age–chemical structure of the Milky Way disc: an application of copulas and elicitable maps

In the Milky Way, the distribution of stars in the [α/Fe] versus [Fe/H] and [Fe/H] versus age planes holds essential information about the history of star formation, accretion, and dynamical evolution of the Galactic disc. We investigate these planes by applying novel statistical methods called copulas and elicitable maps to the ages and abundances of red giants in the Apache Point Observatory Galactic Evolution Experiment survey. We find that the high- and low-α disc stars have a clean separation in copula space and use this to provide an automated separation of the α sequences using a purely statistical approach. This separation reveals that the high-α disc ends at the same [α/Fe] and age at high [Fe/H] as the low-[Fe/H] start of the low-α disc, thus supporting a sequential formation scenario for the high- and low-α discs. We then combine copulas with elicitable maps to precisely obtain the correlation between stellar age τ and metallicity [Fe/H] conditional on Galactocentric radius R and height z in the range 0 < R < 20 kpc and |z| < 2 kpc. The resulting trends in the age–metallicity correlation with radius, height, and [α/Fe] demonstrate a ≈0 correlation wherever kinematically cold orbits dominate, while the naively expected negative correlation is present where kinematically hot orbits dominate. This is consistent with the effects of spiral-driven radial migration, which must be strong enough to completely flatten the age–metallicity structure of the low-α disc.

79 ASTRONOMY AND ASTROPHYSICS↗

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

In this work, we investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Best practices in NMR metabolomics: Current state

A literature survey was conducted to identify current practices used by NMR metabolomics investigators when conducting and reporting their metabolomics studies. A total of 463 papers from 2020 and 80 papers from 2010 were selected from PubMed and were manually analyzed by a team of investigators to assess the extent and completeness of the experimental procedures and protocols reported. A significant number of the papers did not report on essential experimental details, incompletely stated which statistical methods were used, improperly applied supervised multivariate statistical analyses, or lacked validation of statistical models. A large diversity of protocols and software were identified, which suggests a lack of consensus and a relatively limited use of commonly agreed upon standards for conducting and reporting NMR metabolomics studies. In conclusion, the overall intent of the survey is to inform and encourage the NMR metabolomics community to develop and adopt best-practices for the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Identification and interpretation of patterns in rocket engine data

A prototype software system was constructed to detect anomalous Space Shuttle Main Engine (SSME) behavior in the early stages of fault development significantly earlier than the indication provided by either redline detection mechanism or human expert analysis. The major task of the research project is to analyze ground test data, to identify patterns associated with the anomalous engine behavior, and to develop a pattern identification and detection system on the basis of this analysis. A prototype expert system which was developed on both PC and Symbolics 3670 lisp machine for detecting anomalies in turbopump vibration data was checked with data from ground tests 902-473, 902-501, 902-519, and 904-097 of the Space Shuttle Main Engine. The neural networks method was also applied to supplement the statistical method utilized in the prototype system to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. In most cases the anomalies detected by the expert system agree with those reported by NASA. On the neural networks approach, the results are given the successful detection rate higher than 95 percent to identify either normal or abnormal running condition based on the experimental data as well as numerical simulation.

Lo, C. F.↗

Interrelationships Between Receiver/Relative Operating Characteristics Display, Binomial, Logit, and Bayes' Rule Probability of Detection Methodologies

Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.

Generazio, Edward R.↗

Quantitative validation of carbon-fiber laminate low velocity impact simulations

Simulations of low velocity impact with a flat cylindrical indenter upon a carbon fiber fabric reinforced polymer laminate are rigorously validated. Comparison of the impact energy absorption between the model and experiment is used as the validation metric. Additionally, non-destructive evaluation, including ultrasonic scans and three-dimensional computed tomography, provide qualitative validation of the models. The simulations include delamination, matrix cracks and fiber breaks. An orthotropic damage and failure constitutive model, capable of predicting progressive damage and failure, is developed in conjunction and described. An ensemble of simulations incorporating model parameter uncertainties is used to predict a response distribution which is then compared to experimental output using appropriate statistical methods. Lastly, the model form errors are exposed and corrected for use in an additional blind validation analysis. The result is a quantifiable confidence in material characterization and model physics when simulating low velocity impact in structures of interest.

36 MATERIALS SCIENCE↗