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DOE OSTI2026

ComStock Measure Documentation: Variable-Speed Pumps

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - variable speed pumps - and briefly introduces key results. The full public data set can be accessed on the ComStock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

ComStock Measure Documentation: High-Efficiency Rooftop Unit

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - high-efficiency rooftop unit (RTU) - and briefly introduces key results. The full public data set can be accessed on the Comstock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps

We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework

Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.

LOSAC↗

DOE OSTI2020

Hydrogeologic Model Based on Reduced Complexity

This data set provides the basic hydrogeologic model used for development of the TOUGH2 reservoir modeling results for the Kimberlina site. The hydrogeologic model is based on the static geologic model provided as a separate data set but has some simplifications regarding the geologic layers. Some geologic layers have been merged for the hydrogeologic model because the hydrogeologic properties were very similar. The data set contains several files. The word file explains the structure of the data files. The pdf contains a table with the hydrogeologic properties. The data set also includes 15 thickness files for 15 geological layers (including 3 Vedder sand layers and 3 Vedder shale layers), three elevations files (with reference elevation at ground surface/model top at 9000 m), and one fault trace file for seven faults. Most users may only need 2-3 faults close to the injection well (also see the figure). The arrangement of these layer thickness and elevations files can be see from the three layer files (for Tecplot). To develop a grid structure, start with the Vedder top elevation (REFVedderTop.dat) and add/subtract the thicknesses of layers above/below.

3D Groundwater Model↗

DOE OSTI2011

Reporting central tendencies of chamber measured surface emission and oxidation

Methane emissions, concentrations, and oxidation were measured on eleven MSW landfills in eleven states spanning from California to Pennsylvania during the three year study. The flux measurements were performed using a static chamber technique. Initial concentration samples were collected immediately after placement of the flux chamber. Oxidation of the emitted methane was evaluated using stable isotope techniques. When reporting overall surface emissions and percent oxidation for a landfill cover, central tendencies are typically used to report 'averages' of the collected data. The objective of this study was to determine the best way to determine and report central tendencies. Results showed that 89% of the data sets of collected surface flux have lognormal distributions, 83% of the surface concentration data sets are also lognormal. Sixty seven percent (67%) of the isotope measured percent oxidation data sets are normally distributed. The distribution of data for all eleven landfills provides insight of the central tendencies of emissions, concentrations, and percent oxidation. When reporting the 'average' measurement for both flux and concentration data collected at the surface of a landfill, statistical analyses provided insight supporting the use of the geometric mean. But the arithmetic mean can accurately represent the percent oxidation, as measured with the stable isotope technique. We examined correlations between surface CH{sub 4} emissions and surface air CH{sub 4} concentrations. Correlation of the concentration and flux values using the geometric mean proved to be a good fit (R{sup 2} = 0.86), indicating that surface scans are a good way of identifying locations of high emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

A STUDY OF CHANGES IN DEFORMATION AND METABOLISM IN LEFT VENTRICLE AS A FUNCTION OF HYPERTROPHY IN SPONTANEOUS HYPERTENSIVE RATS USING MICROPET TECHNOLOGY

Problem: In the case of hypertrophy caused by pressure overload (hypertension) there is an increase in cardiac mass and modification cardiac metabolism. Aim: This study was designed to study the changes in glucose metabolism, ejection fraction, and deformation in the left ventricle with the progression of hypertrophy in spontaneous hypertensive rats (SHR). Methods: Dynamic PET data were acquired using the microPET II at UC Davis. Two rats were imaged at 10-week intervals for 18 months. Each time a dose of approximately 1- 1.5 mCi of F-18-FDG was injected into a normotensive Wistar Kyoto (WKY) rat and the same dose was injected into a SHR rat. Each rat was imaged using a gated dynamic acquisition for 80 minutes acquiring list mode data with cardiac gating of approximately 600-900 million total counts. For the analysis of glucose of metabolism, the list mode data were histogrammed into a dynamic sequence (42 frames over 80 mins). For each time frame, projection data of 1203 140x210 sinograms of 0.582 mm bins were formed by summing the last three gates before and one after the R-wave trigger to correspond to the diastolic phase of the cardiac cycle. Dynamic sequences of 128x128x83 matrices of 0.4x0.4x0.582 mm3 voxels in x, y, and z were reconstructed using an iterative MAP reconstruction which used a prior that penalized the high frequency components of the reconstruction using appropriate weighting between 26 nearest neighboring voxels. Time activity curves were generated from the dynamic reconstructed sequence for the blood and left ventricular tissue regions of interest which were fit to a 2-compartment model to obtain a least squares fit for the kinetic parameters. For the analysis of deformation, the list mode data were histogrammed into 8 gates of the cardiac cycle, each gate was the total sum of the later 60 mins of the 80 min acquisition. Images of 128x128x83 matrices for each gate were reconstructed using the same iterative MAP reconstruction used to reconstruct the dynamic sequence. The in-plane image dimensions were doubled to 256x256x83 in order to increase the resolution for the Warping analyses. These image data sets were then cropped to 128x128x83. The end-systolic image data sets were designated as the template images and the end-diastole image data sets were designated as the target images, thus providing an analysis of the diastolic relaxation and filling phases of the cardiac cycle. The template images were manually segmented to create surface definitions representing the epi- and endocardial surfaces. Finite element models of the left ventricles were created using the segmented surfaces and defining a transversely isotropic material with fiber angles varying from the epicardial surface to the endocardial surface. A Warping analyses was performed to obtain the LV strain tensor and fiber stretch distributions. Results: In one study, the average first principal Green-Lagrange strain, fiber stretch, ejection fraction, and metabolic rate of F-18-FDG was 0.22, 1.08, 80%, 0.1 for the WKY rat and 0.16, 1.06, 50%, 0.25 for the SHR rat, respectively. These same rats studied a year later presented with a metabolic rate of F-18-FDG of 0.11 and 0.25 for the WKY and SHR, respectively. A follow-up study the average strain (n=10) and ejection fraction (n=18) was 0.21, 72.7% for WKY and 0.17, 69.8% for the SHR, respectively. Conclusion: In the case of pressure overload there is an increased reliance on carbohydrate oxidation in an attempt to maintain contractile function.

Sitek, Arkadiusz↗

DOE OSTI2019

Third-Order Møller–Plesset Perturbation Theory Made Useful? Choice of Orbitals and Scaling Greatly Improves Accuracy for Thermochemistry, Kinetics, and Intermolecular Interactions

In this study, we develop and test methods that include second- and third-order perturbation theory (MP3) using orbitals obtained from regularized orbital-optimized second-order perturbation theory, κ-OOMP2, denoted as MP3:κ-OOMP2. Testing MP3:κ-OOMP2 shows RMS errors that are 1.7-5 times smaller than those of MP3 across 7 data sets. To do still better, empirical training of the scaling factors for the second- and third-order correlation energies and the regularization parameter on one of those data sets led to an unregularized scaled (c 2 = 1.0; c 3 = 0.8) denoted as MP2.8:κ-OOMP2. MP2.8:κ-OOMP2 yields significant additional improvement over MP3:κ-OOMP2 in 4 of 6 test data sets on thermochemistry, kinetics, and noncovalent interactions. Remarkably, these two methods outperform coupled cluster with singles and doubles in 5 of the 7 data sets considered, at greatly reduced cost (no O(N 6 ) iterations).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

25 ENERGY STORAGE↗

DOE OSTI2023

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Steingart, Daniel Artemis↗

DOE OSTI2021

Identifying Light-Duty Vehicle Travel from Large-Scale Multimodal Wearable GPS Data with Novelty Detection Algorithms

Identifying travel mode within travel survey data sets, especially light-duty vehicle (LDV) travel, is foundational, though nontrivial, to travel behavior analysis and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. They are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data requirements. This paper proposes an LDV travel mode detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique-one-class support vector machines (OCSVMs)-and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal prediction.

47 OTHER INSTRUMENTATION↗

DOE OSTI2014

Meteorological Services: Annual Data Report for 2013

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2013. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to a limited degree outside researchers based on the level of time and resources required. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

GPU-based relative fuzzy connectedness image segmentation

Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

The Demand-Side Grid (dsgrid) Model

This presentation gives a high-level overview of the recently published demand-side grid (dsgrid) model documentation. The general modeling philosophy and technical approach of dsgrid is described, as is an initial data set covering 2012 contiguous United States (CONUS) electricity demand. The initial data set describes about 80% of electrical load at an hourly level by county, subsector and end use. Coarser models and data sets are used to describe the remainder. Model residuals are calculated at different levels of aggregation by leveraging historical electricity sector data and a model of 2012 distributed generation. Overall, the dsgrid model has been developed to support analysis of future load scenarios, including interactive effects between energy efficiency, electrification, demand response, renewable energy, battery energy (or other forms of) storage, and future grid conditions broadly construed. Planned future work includes the development of future load scenarios and physically realistic estimates of the flexible load resource.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

The Hubble Constant from Strongly Lensed Supernovae with Standardizable Magnifications

The dominant uncertainty in the current measurement of the Hubble constant (H 0 ) with strong gravitational lensing time delays is attributed to uncertainties in the mass profiles of the main deflector galaxies. Strongly lensed supernovae (glSNe) can provide, in addition to measurable time delays, lensing magnification constraints when knowledge about the unlensed apparent brightness of the explosion is imposed. We present a hierarchical Bayesian framework to combine a data set of SNe that are not strongly lensed and a data set of strongly lensed SNe with measured time delays. We jointly constrain (i) H 0 using the time delays as an absolute distance indicator, (ii) the lens model profiles using the magnification ratio of lensed and unlensed fluxes on the population level, and (iii) the unlensed apparent magnitude distribution of the SN population and the redshift–luminosity relation of the relative expansion history of the universe. We apply our joint inference framework on a future expected data set of glSNe and forecast that a sample of 144 glSNe of Type Ia with well-measured time series and imaging data will measure H 0 to 1.5%. We discuss strategies to mitigate systematics associated with using absolute flux measurements of glSNe to constrain the mass density profiles. Using the magnification of SN images is a promising and complementary alternative to using stellar kinematics. Future surveys, such as the Rubin and Roman observatories, will be able to discover the necessary number of glSNe, and with additional follow-up observations, this methodology will provide precise constraints on mass profiles and H 0 .

79 ASTRONOMY AND ASTROPHYSICS↗