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DOE OSTI2015

Physics-based statistical learning approach to mesoscopic model selection

In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. Here, we apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD “training” data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD “test” data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2023

First observation of 28 O

Subjecting a physical system to extreme conditions is one of the means often used to obtain a better understanding and deeper insight into its organization and structure. In the case of the atomic nucleus, one such approach is to investigate isotopes that have very different neutron-to-proton ($N/Z$) ratios than in stable nuclei. Light, neutron-rich isotopes exhibit the most asymmetric $N/Z$ ratios and those lying beyond the limits of binding, which undergo spontaneous neutron emission and exist only as very short-lived resonances (about 10 -21 s), provide the most stringent tests of modern nuclear-structure theories. Here we report on the first observation of 28 O and 27 O through their decay into 24 O and four and three neutrons, respectively. The 28 O nucleus is of particular interest as, with the $Z$ = 8 and $N$ = 20 magic numbers, it is expected in the standard shell-model picture of nuclear structure to be one of a relatively small number of so-called ‘doubly magic’ nuclei. Furthermore, both 27 O and 28 O were found to exist as narrow, low-lying resonances and their decay energies are compared here to the results of sophisticated theoretical modelling, including a large-scale shell-model calculation and a newly developed statistical approach. In both cases, the underlying nuclear interactions were derived from effective field theories of quantum chromodynamics. Finally, it is shown that the cross-section for the production of 28 O from a 29 F beam is consistent with it not exhibiting a closed $N$ = 20 shell structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Relationship between Grain Size and Sample Thickness on the Creep-Rupture Performance of Thin Metallic Sheets of INCONEL Alloy 740H

A study was conducted on INCONEL® alloy 740H® to examine the role of grain size and sheet thickness on the alloy’s creep-rupture behavior. Three different starting sheet thicknesses were utilized and multiple heat-treatment conditions anticipated for compact heat exchanger (CHX) manufacturing were applied to produce a range of grain sizes. Creep-rupture testing was conducted at 750 °C for times up to about 6000 h and the results were compared to wrought databases. The data show that both creep strength and ductility were important factors in the overall creep performance of the sheets. Reductions in performance were observed due either to accelerated creep when grain size was fine or loss of rupture ductility when grain sizes approached the sheet thickness. Some combinations of heat-treatment and thickness were able to produce typical expected wrought creep properties. Historically a ‘rule-of-thumb’ requirement for creep testing suggests 3-5 grains per sample minimum dimension to ensure homogeneous behavior. This research shows that to ensure representative wrought creep performance (i.e. no effect of sample size), the sample minimum cross-section should be 10 times the average ASTM grain size. Statistical analysis of the microstructures suggests the population of larger grains as a controlling feature in creep failure.

14 SOLAR ENERGY↗

DOE OSTI2019

Quantified fracture (joint) clustering in Archean basement, Wyoming: application of the normalized correlation count method

We demonstrate statistically significant self-organized clustering over a length scale range from 10 -2 to 10 1 m for north-striking opening-mode fractures (joints) in Late Archean Mount Owen Quartz Monzonite. Spatial arrangement is a critical fracture network attribute that until recently has only been assessed qualitatively. We use normalized fracture intensity plots and the normalized correlation count (NCC) method of Marrett et al. to discriminate clustered from randomly placed or evenly spaced patterns quantitatively over a wide range of length scales and to test the statistical significance of the resulting patterns. We propose a procedure for interpreting cluster patterns on NCC diagrams generated by the freely available spatial analysis software CorrCount. Results illustrate the efficacy of NCC to measure fracture clustering patterns in texturally homogeneous Archean granitic rock in a setting distant (>2 km) from folds or faults. In their current geological setting, these regional fractures are conduits for water flow and their patterns – and the NCC approach to defining clusters – may be useful guides to the spatial arrangement style and clustering magnitude of conductive fractures in other, less accessible fractured basement rocks.

02 PETROLEUM↗

DOE OSTI2022

Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)

This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.

58 GEOSCIENCES↗

DOE OSTI2024

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Science & Technology - Other Topics↗

DOE OSTI2017

A simple dynamic subgrid-scale model for LES of particle-laden turbulence

In this study, a dynamic model for large-eddy simulations is proposed in order to describe the motion of small inertial particles in turbulent flows. The model is simple, involves no significant computational overhead, contains no adjustable parameters, and is flexible enough to be deployed in any type of flow solvers and grids, including unstructured setups. The approach is based on the use of elliptic differential filters to model the subgrid-scale velocity. The only model parameter, which is related to the nominal filter width, is determined dynamically by imposing consistency constraints on the estimated subgrid energetics. The performance of the model is tested in large-eddy simulations of homogeneous-isotropic turbulence laden with particles, where improved agreement with direct numerical simulation results is observed in the dispersed-phase statistics, including particle acceleration, local carrier-phase velocity, and preferential-concentration metrics.

42 ENGINEERING↗

DOE OSTI2018

Does size matter? An evaluation of length and proportion of information in environmental impact statements

Highlights: • Empirical analysis of length and proportion of information in EISs is presented. • Current EISs, in Brazil, are significantly longer than the early ones. • Baseline information is the most common element in EISs. • EIS length is affected by many variables, including project sector and size. • While relevant to decision-making, EIS length should be carefully regulated. - Abstract: For decades, authors and institutions have argued that the quality of Environmental Impact Statements (EISs) is somehow affected by the volume of information they contain. Both too little and too much information can be a problem. However, very few academic studies have addressed the issue of EIS length in detail. The objective of this article was to systematically analyze the volume of information presented in EISs, using Brazil as the empirical context. More specifically, this study evaluated the volume and proportion of information disclosed in 49 Brazilian EISs. This study also tried to identify sectorial variations and whether variables such as project size and number of pages in Terms of References are likely determinants of information volume. >146 thousand pages of EIS information were scrutinized in two rounds of content analysis. Data were organized in spreadsheets and then coded and analyzed through various descriptive and inferential statistical techniques. Overall, findings corroborate the fact that EISs are now significantly longer than the early ones, and still heavily loaded with baseline information. The average number of pages in EISs and in Non-technical Summaries was found to be 2993 and 94, respectively. Kruskal-Wallis and linear regression tests indicated that EIS length is likely affected by a combination of variables, including project size, territorial and sectorial characteristics. Such findings suggest that the historical approach of setting page limits to EISs through regulations and Terms of References is no longer appropriate for EIA practice in connection with large enterprises in Brazil, and arguably elsewhere. The article discusses its practical and academic implications, and highlights the need to further investigate the actual impacts of EIS length on decision-making.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

The agile alert system for gamma-ray transients

In recent years, a new generation of space missions has offered great opportunities for discovery in high-energy astrophysics. In this article we focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID) on board the AGILE space mission. AGILE-GRID, sensitive in the energy range of 30 MeV-30 GeV, has detected many γ-ray transients of both galactic and extragalactic origin. This work presents the AGILE innovative approach to fast γ-ray transient detection, which is a challenging task and a crucial part of the AGILE scientific program. The goals are to describe (1) the AGILE Gamma-Ray Alert System, (2) a new algorithm for blind search identification of transients within a short processing time, (3) the AGILE procedure for γ-ray transient alert management, and (4) the likelihood of ratio tests that are necessary to evaluate the post-trial statistical significance of the results. Special algorithms and an optimized sequence of tasks are necessary to reach our goal. Data are automatically analyzed at every orbital downlink by an alert pipeline operating on different timescales. As proper flux thresholds are exceeded, alerts are automatically generated and sent as SMS messages to cellular telephones, via e-mail, and via push notifications from an application for smartphones and tablets. These alerts are crosschecked with the results of two pipelines, and a manual analysis is performed. Being a small scientific-class mission, AGILE is characterized by optimization of both scientific analysis and ground-segment resources. The system is capable of generating alerts within two to three hours of a data downlink, an unprecedented reaction time in γ-ray astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Towards Malware Detection via CPU Power Consumption: Data Collection Design and Analytics

This paper presents an experimental design and data analytics approach aimed at power-based malware detection on general-purpose computers. Leveraging the fact that malware executions must consume power, we explore the postulate that malware can be accurately detected via power data analytics. Our experimental design and implementation allow for programmatic collection of CPU power profiles for fixed tasks during uninfected and infected states using five different rootkits. To characterize the power consumption profiles, we use both simple statistical and novel, sophisticated features. We test a one-class anomaly detection ensemble (that baselines non-infected power profiles) and several kernel-based SVM classifiers (that train on both uninfected and infected profiles) in detecting previously unseen malware and clean profiles. The anomaly detection system exhibits perfect detection when using all features and tasks, with smaller false detection rate than the supervised classifiers. The primary contribution is the proof of concept that baselining power of fixed tasks can provide accurate detection of rootkits. Moreover, our treatment presents engineering hurdles needed for experimentation and allows analysis of each statistical feature individually. This work appears to be the first step towards a viable power-based detection capability for general-purpose computers, and presents next steps toward this goal.

Bridges, Robert↗

DOE OSTI2019

Practical applications of the sigma factor alarm method within alpha CAMs utilizing either ROI, tail-fitting, or peak-fitting algorithms

Some test must be applied to the low-level count data from alpha continuous air monitors (CAMs) to determine if the count is statistically significant (i.e., significantly above background). The test should also automatically account for different levels of background (i.e., different ambient radon progeny concentrations). In other words, the method should be as sensitive as possible, automatically desensitizing when required, yet in such a manner as to not exceed some previously-chosen acceptable false-alarm rate. The test that has been historically utilized within many alpha CAMs to accomplish these goals is the so-called sigma factor approach.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2025

STAT7 v2.0 User Guide

The STAT7 software was developed to perform steady-state, single-phase thermal hydraulics analysis of plate-fueled reactors based on statistical propagation of uncertainties. Application of the software includes non-power research and test reactor analysis, and it has been used for the conversion to low- enriched uranium fuel of U.S. High Performance Research Reactors such as the Massachusetts Institute of Technology Research Reactor. Since it can be necessary to repeat reactor safety analysis, such as during fuel reloading, STAT7 accommodates flexibility in analyzing many practical aspects of reactor fuel management. STAT7 uses a Monte Carlo approach to model uncertainty in common fuel fabrication parameters and other key reactor operating parameters required for reactor thermal hydraulics analysis. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux, or onset of flow instability. STAT7 supports water properties based on the IAPWS-IF97 functions (The International Association for the Properties of Water and Steam Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam) in addition to fitted functions. STAT7 predicts axial profiles of fuel, cladding, and coolant temperature along a lateral stripe that runs the full length of the fuel plate from the bottom to the top. STAT7 can simultaneously analyze every axial node in each lateral stripe of all fuel plates and coolant channels in every fuel element of an entire reactor core. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. In a single execution, STAT7 can be used to perform full core analysis by analyzing the margin to onset of nucleate boiling and onset of flow instability for each axial node of each stripe of each plate of each fuel element in the core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

Background: Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze commonand rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods. Results: The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk. Conclusions: A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Physics-based, Bayesian sequential detection method and system for radioactive contraband

A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Performance of a GM tube based environmental dose rate monitor operating in the Time-To-Count mode

The events at the Fukushima Daiichi power plant in the aftermath of a natural disaster underline the importance of a large array of networked environmental monitors to cover areas around nuclear power plants. These monitors should meet a few basic criteria: have a uniform response over a wide range of gamma energies, have a uniform response over a wide range of incident angles, and have a large dynamic range. Many of these criteria are met if the probe is qualified to the international standard IEC 60532 (Radiation protection instrumentation - Installed dose rate meters, warning assemblies and monitors - X and gamma radiation of energy between 50 keV and 7 MeV), which specifically deals with energy response, angle of incidence, dynamic range, response time, and a number of environmental characteristics. EcoGamma is a dual GM tube environmental gamma radiation monitor designed specifically to meet the requirements of IEC 60532 and operate in the most extreme conditions. EcoGamma utilizes two energy compensated GM tubes operating with a Time-To-Count (TTC) collection algorithm. The TTC algorithm extends the lifetime and range of a GM tube significantly and allows the dual GM tube probe to achieve linearity over approximately 10 decades of gamma dose rate (from the Sv/hr range to 100 Sv/hr). In the TTC mode of operation, the GM tube is not maintained in a biased condition continuously. This is different from a traditional counting system where the GM tube is held at a constant bias continuously and the total number of strikes that the tube registers are counted. The traditional approach allows for good sensitivity, but does not lend itself to a long lifetime of the tube and is susceptible to linearity issues at high count rates. TTC on the other hand only biases the tube for short periods of time and in effect measures the time between events, which is statistically representative of the total strike rate. Since the tube is not continually biased, the life of the tube is extended and the linearity is greatly improved. Testing has been performed at Pacific Northwest National Laboratory (PNNL) in the USA and confirms compliance to IEC 60532 as well as linearity (± 10%) up to 100 Sv/hr. Furthermore, a network of EcoGamma probes may be linked through available supervisory software to provide a dose rate map of an area. This allows for real time monitoring of dose rates from one (or multiple) remote locations. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Frontiers in computing for artificial intelligence

An emerging diversity of computational platforms offers many different approaches to adopting the paradigm of artificial intelligence to the study of electron-ion collisions. Here we review several leading candidates in this computational frontier and their workflows for experimental applications of artificial intelligence that may impact the future Electron-Ion Collider. We discuss the motivation for exploring novel methods to solve artificial intelligence and machine learning problems including with customized devices, quantum simulation, and heterogeneous computing systems. Furthermore, these technologies offer promising approaches to address some of the leading concerns of future computing that may impact the Electron-Ion Collider but they will require further development and testing in order to support future planning efforts.

detector design and construction technologies and ↗

DOE OSTI2016

Statistical Approach for Determining the Sandia Array Performance Model Coefficients that Considers String-Level Mismatch

Commonly used performance models, such as PVsyst, Sandia Array Performance Model (SAPM), and PV LIB, treat the PV array as being constructed of identical modules. Each of the models attempts to account for mismatch losses by applying a simple percent reduction factor to the overall estimated power. The present work attempted to reduce uncertainty of mismatch losses by determining a representative set of performance coefficients for the SAPM that were developed from a characterization of a sample of modules. This approach was compared with current practice, where only a single module’s thermal and electrical properties are testing. However, the results indicate that minimal to no improvements in model predictions were achieved.

47 OTHER INSTRUMENTATION↗