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DOE OSTI2021

Simulated effects of sample size and grain neighborhood on the modeling of extreme value fatigue response

Assessing the size of representative volume elements (RVEs) for fatigue-related applications is challenging. A RVE relevant to random microstructure requires a volume of material that is sufficiently large to capture the grain/phase heterogeneity that captures all statistical moments of the distribution of the driving force for fatigue crack formation at “hot spot” grains. Consequently, the large size of a microstructure RVE required to study fatigue phenomena is largely computationally intractable and difficult to explore. A more realistic objective in this work is to systematically study, as a function of the size of a statistical sample of microstructure, trends towards convergence of the simulated distribution of driving force for fatigue crack formation. Our present work accordingly leverages the recently developed open-source PRISMS-Fatigue framework to examine the trends in convergence of extreme value distributions (EVD) of Fatigue Indicator Parameters (FIPs) in progressively larger polycrystalline microstructure realizations of FCC Al alloy 7075-T6 using crystal plasticity finite element method simulations. The results are compared to the traditional method in which ensembles of statistical volume elements (SVEs) are simulated to build up statistics intended to approximate those associated with a larger volume of material. The convergence of EVDs with increase of size of a SVE of microstructure is closely related to the extent of grain nearest neighbor (NN) interactions. Accordingly, the sensitivity of the local micromechanical response at hot spot grains is quantitatively investigated by systematically varying the orientations of NN grains. Results indicate that SVEs with cubic crystallographic texture tend towards convergence of the EVD of FIPs with tens of thousands of grains while the random and rolled textures require larger volumes. Simple relationships based on microstructure parameters (e.g., Schmid Factor, grain size, NN misorientation) do not completely correlate to fatigue hot spot grains. Finally, the sensitivity of the extreme value fatigue response at hot spot grains extends to the 3rd NN when a single neighborhood grain orientation is altered.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detecting Low-level Radiation Sources Using Border Monitoring Gamma Sensors

We consider a problem of detecting a low-level radiation source using a network of Gamma spectral sensors placed on the periphery of a monitored region. We propose a computationally light-weight, correlation-based method which is primarily intended for systems with limited computing capacity. Sensor measurements are combined at the fusion by first generating decisions at each time step and then taking their majority vote within a time widow. At each time step, decisions are generated using two strategies: (i) SUM method based on a threshold decision on a correlation statistic derived from measurements from all sensors, and (ii) OR method based on logical-OR of threshold decisions based on correlations statistics of individual sensor measurements. We derive analytical performance bounds for false alarm rates of SUM and OR methods, and show that their performance is enhanced by the temporal smoothing of majority vote within a time window. Using measurements from a test campaign, we generate a border monitoring scenario with twelve 2"x2" NaI Gamma sensors deployed on the periphery of 42m x 42m outdoor region. A Cs-137 source is moved in a straight-line across this region, starting several meters outside and finally moving away from it. We illustrate the performance of both correlation-based detection methods, and compare their performances with each other and with a particle filter method. Overall, under small false-alarm conditions, the OR fusion is found to produce better detection performance.

Sen, Satyabrata↗
DOE OSTI2017

Massive Associative K-biclustering (MAK) v1.0

Massive Associative K-biclustering (MAK) is a method to address the key limitations in other biclustering approaches. In a formal evaluation against new simulated data sets with more realistic properties MAK improves recovery of biclusters exhibiting common behavior across conditions, with the highest F1 scores, a measure of a test's accuracy, and lowest false positive rates, the probability of falsely rejecting the null hypothesis, compared to established methods. The developers show how MAK can be applied to a popular data set from Saccharomyces cerevisiae to uncover new properties not captured by other methods. MAK biclusters showed increased statistical enrichments for known regulatory associations and gene functions, including for nearly 30% of the novel MAK biclusters not found by other methods. Compared to other methods, MAK results had at least two-fold higher coverage of differential expression in the overall dataset, an overlapping bicluster network with distinct modules for protein expression and energy metabolism, and biologically relevant overlapping biclusters including cases of combinatorial regulation. MAK is suited for large datasets with many potentially noisy and overlapping patterns of varying sizes.

JOACHIMIAK, MARCIN↗
DOE OSTI2021

Effective $\mathbb{Z}_{3}$ model for finite-density QCD with tensor networks

The tensor renormalization group is a promising numerical method used to study lattice statistical field theories. However, this approach is computationally expensive in 2+1 and 3+1 dimensions. Here we use tensor renormalization group methods to study an effective three-dimensional $\mathbb{Z}_{3}$ model for the heavy-quark, high-temperature, strong-coupling limit of single-flavor 3+1 dimensional quantum chromodynamics. Our results are cross-checked using the worm Monte Carlo algorithm. We present the phase diagram of the model through the measurement of the Polyakov loop, the nearest-neighbor Polyakov loop correlator, and their susceptibilities. The tensor renormalization group results are in good agreement with the literature

Bloch, Jacques↗
DOE OSTI2022

Statistics on magnetic properties of Co compounds: A database-driven method for discovering Co-based ferromagnets

The search for new ferromagnetic compounds is often targeted at known structure families, particularly those containing iron, cobalt, and manganese. Here, we propose a method to expand this search to lesser-known structure types, using a database of experimental Curie and Néel temperatures. This study, in particular, illustrates the case of compounds containing the element cobalt, and we demonstrate how the use of such a database can lead to the discovery of ferromagnetic materials that had previously been overlooked. We report statistics from a literature survey of the magnetic properties of Co-based compounds with more than 33 at. % Co. We classify more than 13 000 compounds by structure type, cobalt content, and magnetic ground state. From these data, compounds TaCo 2 Ga, La 6 Co 13 Bi, and Nd 2 Co 3 were identified as potential ferromagnets, and we confirm their ferromagnetic ordering theoretically via first-principles calculations and experimentally via synthesis and characterization measurements. In addition, the analysis is focused on the collection of data trends and discovery of ferromagnetic materials with easy-axis magnetic anisotropy. Both known ferromagnetic materials with unknown magnetic anisotropy, and unstudied compounds were considered. From the subset of known ferromagnets with unknown anisotropy, the compound Co 2 Mg was synthesized, characterized, and determined to have easy-axis magnetic anisotropy at room temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Photon-number statistics of twin beams: Self-consistent measurement, reconstruction, and properties

A method for the determination of photon-number statistics of twin beams using the joint signal-idler photocount statistics obtained by an iCCD camera is described. It also provides absolute quantum detection efficiency of the camera. Using the measured photocount statistics, quasi-distributions of integrated intensities are obtained. They attain negative values occurring in characteristic strips an a consequence of pairing of photons in twin beams.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Short-Duration Simulations from Measurements.

A method is presented that ascribes proper statistical variability to simulations that are derived from longer-duration measurements. This method is applicable to simulations of either real-value or integer-value data. An example is presented that demonstrates the applicability of this technique to the synthesis of gamma-ray spectra.

Mitchell, Dean J.↗
DOE OSTI2022

Search For Excited Cascade Hypersons (¿*-) Using the CLAS12 Spectrometer at Jefferson Laboratory

The number of experimentally observed doubly strange Cascade states to date is far fewer than has been predicted theoretically. The CLAS12 Very Strange physics program (E12- 11-005A) at the Thomas Jefferson National Accelerator Facility aims to study the electroproduction of these states using the newly upgraded CEBAF Large Acceptance Spectrometer for 12 GeV (CLAS12) in experimental Hall B. In this project, the reaction ep ! e0K+K+??? ! e0K+K+K?(?=?0) is studied using CLAS12 Run Group A (RG-A) data sets taken by impinging electron beams of 10:2 and 10:6 GeV energies on an LH2 target. Scattered electrons are detected with either the Forward Detector (FD), covering a polar angle range of 5? to 35? to study electroproduction, or with the Forward Tagger (FT), covering a polar angle range of 2:5? to 4:5?, to study quasi-real photoproduction. The CLAS12 detector with nearly a 4? solid angle coverage is used to detect scattered electrons and charged kaons in the ?final state. ?=?0 hyperons are reconstructed using the missing mass technique to explore intermediate doubly-strange hyperons (???) that decay to K? and ?=?0. No statistically significant ??? states other than the ???(1530) were found in the missing mass spectra based on the currently available statistics in the CLAS12-FD acceptance only. A maximum log-likelihood method is implemented to determine the statistical significance and the upper limits on the ???(1820) electroproduction cross section by constructing 95% confidence level boundaries on the ???(1820) yields. Additional research was conducted to investigate the upper limit electroproduction cross section of the reaction ep ! e0K+K+??? ! e0K+K+K?(?=?0) as a function of the electroproduced ??? mass and the differential cross section ( d?dMM(e0K+K+) ) as a function of missing mass MM(e0K+K+) for electroproduction and quasi-real photoproduction processes.

Khanal, Achyut↗
DOE OSTI2020

The look-elsewhere effect from a unified Bayesian and frequentist perspective

When searching over a large parameter space for anomalies such as events, peaks, objects, or particles, there is a large probability that spurious signals with seemingly high signi ficance will be found. This is known as the look-elsewhere effect and is prevalent throughout cosmology, (astro)particle physics, and beyond. To avoid making false claims of detection, one must account for this effect when assigning the statistical significance of an anomaly. This is typically accomplished by considering the trials factor, which is generally computed numerically via potentially expensive simulations. In this paper we develop a continuous generalization of the Bonferroni and Sidak corrections by applying the Laplace approximation to evaluate the Bayes factor, and in turn relating the trials factor to the prior-to-posterior volume ratio. Here, we use this to define a test statistic whose frequentist properties have a simple interpretation in terms of the global p-value, or statistical significance. We apply this method to various physics-based examples and show it to work well for the full range of p-values, i.e. in both the asymptotic and non-asymptotic regimes. We also show that this method naturally accounts for other model complexities such as additional degrees of freedom, generalizing Wilks' theorem. This provides a fast way to quantify statistical significance in light of the look-elsewhere effect, without resorting to expensive simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Beyond Generalized Eigenvalues in Lattice Quantum Field Theory

Two analysis techniques, the generalized eigenvalue method (GEM) or Prony's (or related) method (PM), are commonly used to analyze statistical estimates of correlation functions produced in lattice quantum field theory calculations. GEM takes full advantage of the matrix structure of correlation functions but only considers individual pairs of time separations when much more data exists. PM can be applied to many time separations and many individual matrix elements simultaneously but does not fully exploit the matrix structure of the correlation function. We combine both these methods into a single framework based on matrix polynomials. As these algebraic methods are well known for producing extensive spectral information about statistically-noisy data, the method should be paired with some information criteria, like the recently proposed Bayesean model averaging.

Fleming, George T.↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRA's of nuclear power plants (NPPs), and, (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware , development of a philosophical basis for defining software failure , and identification of desirable attributes of quantitative software reliability methods . Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. It is widely recognized that software failures are due to the triggering of pre-existing defects by the software's operational environment. Software defects can arise from errors made in user requirements or coding errors introduced during the developmental process. The software's operational environment includes factors such as the time history of digital system inputs, communication interfaces, the internal state of the digital system, and external conditions. Thus, software reliability is a function of both the number of pre-existing defects and the presence of a triggering condition caused by the manner in which the software is used. In this paper, we describe a statistical software-testing approach for quantifying software reliability and applied it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The work involved collaboration between BNL and INL. The objectives of the study include: (1) Development of a statistical testing approach for estimating software failure probability on demand, the results of which are suitable for including in a probabilistic risk assessment (PRA); and, (2) Application of the approach to the LOCS to estimate its failure probability, and obtain insights into the feasibility, practicality, and usefulness of the estimation in models of digital systems for inclusion in nuclear power plants' PRAs. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications

Monte Carlo simulations are at the heart of many high-fidelity simulations and analyses for radiation transport systems. As is the case with any complex computational model, it is important to propagate sources of input uncertainty and characterize how they affect model output. Unfortunately, uncertainty quantification (UQ) is made difficult by the stochastic variability that Monte Carlo transport solvers introduce. The standard method to avoid corrupting the UQ statistics with the transport solver noise is to increase the number of particle histories, resulting in very high computational costs. In this contribution, we propose and analyze a sampling estimator based on the law of total variance to compute UQ variance even in the presence of residual noise from Monte Carlo transport calculations. Here, we rigorously derive the statistical properties of the new variance estimator, compare its performance to that of the standard method, and demonstrate its use on neutral particle transport model problems involving both attenuation and scattering physics. We illustrate, both analytically and numerically, the estimator’s statistical performance as a function of available computational budget and the distribution of that budget between UQ samples and particle histories. We show analytically and corroborate numerically that the new estimator is unbiased, unlike the standard approach, and is more accurate and precise than the standard estimator for the same computational budget.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2009) (V. 2012)

Publications containing historical energy statistics make it possible to estimate fossil fuel CO2 emissions back to 1751. Etemad et al. (1991) published a summary compilation that tabulates coal, brown coal, peat, and crude oil production by nation and year. Footnotes in the Etemad et al.(1991) publication extend the energy statistics time series back to 1751. Summary compilations of fossil fuel trade were published by Mitchell (1983, 1992, 1993, 1995). Mitchell's work tabulates solid and liquid fuel imports and exports by nation and year. These pre-1950 production and trade data were digitized and CO2 emission calculations were made following the procedures discussed in Marland and Rotty (1984) and Boden et al. (1995). Further details on the contents and processing of the historical energy statistics are provided in Andres et al. (1999). The 1950 to present CO2 emission estimates are derived primarily from energy statistics published by the United Nations (2012), using the methods of Marland and Rotty (1984). The energy statistics were compiled primarily from annual questionnaires distributed by the U.N. Statistical Office and supplemented by official national statistical publications. As stated in the introduction of the Statistical Yearbook, "in a few cases, official sources are supplemented by other sources and estimates, where these have been subjected to professional scrutiny and debate and are consistent with other independent sources." Data from the U.S. Department of Interior's Geological Survey (USGS 2011) were used to estimate CO2 emitted during cement production. Values for emissions from gas flaring were derived primarily from U.N. data but were supplemented with data from the U.S. Department of Energy's Energy Information Administration (1994), Rotty (1974), and data provided by G. Marland. Greater details about these methods are provided in Marland and Rotty (1984), Boden et al. (1995), and Andres et al. (1999).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2010) (V. 2013)

Publications containing historical energy statistics make it possible to estimate fossil fuel CO2 emissions back to 1751. Etemad et al. (1991) published a summary compilation that tabulates coal, brown coal, peat, and crude oil production by nation and year. Footnotes in the Etemad et al.(1991) publication extend the energy statistics time series back to 1751. Summary compilations of fossil fuel trade were published by Mitchell (1983, 1992, 1993, 1995). Mitchell's work tabulates solid and liquid fuel imports and exports by nation and year. These pre-1950 production and trade data were digitized and CO2 emission calculations were made following the procedures discussed in Marland and Rotty (1984) and Boden et al. (1995). Further details on the contents and processing of the historical energy statistics are provided in Andres et al. (1999). The 1950 to present CO2 emission estimates are derived primarily from energy statistics published by the United Nations (2013), using the methods of Marland and Rotty (1984). The energy statistics were compiled primarily from annual questionnaires distributed by the U.N. Statistical Office and supplemented by official national statistical publications. As stated in the introduction of the Statistical Yearbook, "in a few cases, official sources are supplemented by other sources and estimates, where these have been subjected to professional scrutiny and debate and are consistent with other independent sources." Data from the U.S. Department of Interior's Geological Survey (USGS 2012) were used to estimate CO2 emitted during cement production. Values for emissions from gas flaring were derived primarily from U.N. data but were supplemented with data from the U.S. Department of Energy's Energy Information Administration (1994), Rotty (1974), and data provided by G. Marland. Greater details about these methods are provided in Marland and Rotty (1984), Boden et al. (1995), and Andres et al. (1999).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2011) (V. 2015)

Publications containing historical energy statistics make it possible to estimate fossil fuel CO2 emissions back to 1751. Etemad et al. (1991) published a summary compilation that tabulates coal, brown coal, peat, and crude oil production by nation and year. Footnotes in the Etemad et al.(1991) publication extend the energy statistics time series back to 1751. Summary compilations of fossil fuel trade were published by Mitchell (1983, 1992, 1993, 1995). Mitchell's work tabulates solid and liquid fuel imports and exports by nation and year. These pre-1950 production and trade data were digitized and CO2 emission calculations were made following the procedures discussed in Marland and Rotty (1984) and Boden et al. (1995). Further details on the contents and processing of the historical energy statistics are provided in Andres et al. (1999). The 1950 to present CO2 emission estimates are derived primarily from energy statistics published by the United Nations (2014), using the methods of Marland and Rotty (1984). The energy statistics were compiled primarily from annual questionnaires distributed by the U.N. Statistical Office and supplemented by official national statistical publications. As stated in the introduction of the Statistical Yearbook, "in a few cases, official sources are supplemented by other sources and estimates, where these have been subjected to professional scrutiny and debate and are consistent with other independent sources." Data from the U.S. Department of Interior's Geological Survey (USGS 2014) were used to estimate CO2 emitted during cement production. Values for emissions from gas flaring were derived primarily from U.N. data but were supplemented with data from the U.S. Department of Energy's Energy Information Administration (1994), Rotty (1974), and data provided by G. Marland. Greater details about these methods are provided in Marland and Rotty (1984), Boden et al. (1995), and Andres et al. (1999).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2013) (V. 2016)

Publications containing historical energy statistics make it possible to estimate fossil fuel CO2 emissions back to 1751. Etemad et al. (1991) published a summary compilation that tabulates coal, brown coal, peat, and crude oil production by nation and year. Footnotes in the Etemad et al.(1991) publication extend the energy statistics time series back to 1751. Summary compilations of fossil fuel trade were published by Mitchell (1983, 1992, 1993, 1995). Mitchell's work tabulates solid and liquid fuel imports and exports by nation and year. These pre-1950 production and trade data were digitized and CO2 emission calculations were made following the procedures discussed in Marland and Rotty (1984) and Boden et al. (1995). Further details on the contents and processing of the historical energy statistics are provided in Andres et al. (1999). The 1950 to present CO2 emission estimates are derived primarily from energy statistics published by the United Nations (2016), using the methods of Marland and Rotty (1984). The energy statistics were compiled primarily from annual questionnaires distributed by the U.N. Statistical Office and supplemented by official national statistical publications. As stated in the introduction of the Statistical Yearbook, "in a few cases, official sources are supplemented by other sources and estimates, where these have been subjected to professional scrutiny and debate and are consistent with other independent sources." Data from the U.S. Department of Interior's Geological Survey (USGS 2016) were used to estimate CO2 emitted during cement production. Values for emissions from gas flaring were derived primarily from U.N. data but were supplemented with data from the U.S. Department of Energy's Energy Information Administration (1994), Rotty (1974), and data provided by G. Marland. Greater details about these methods are provided in Marland and Rotty (1984), Boden et al. (1995), and Andres et al. (1999).

54 ENVIRONMENTAL SCIENCES↗