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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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DOE OSTI2014

Low dose dynamic CT myocardial perfusion imaging using a statistical iterative reconstruction method

Purpose: Dynamic CT myocardial perfusion imaging has the potential to provide both functional and anatomical information regarding coronary artery stenosis. However, radiation dose can be potentially high due to repeated scanning of the same region. The purpose of this study is to investigate the use of statistical iterative reconstruction to improve parametric maps of myocardial perfusion derived from a low tube current dynamic CT acquisition. Methods: Four pigs underwent high (500 mA) and low (25 mA) dose dynamic CT myocardial perfusion scans with and without coronary occlusion. To delineate the affected myocardial territory, an N-13 ammonia PET perfusion scan was performed for each animal in each occlusion state. Filtered backprojection (FBP) reconstruction was first applied to all CT data sets. Then, a statistical iterative reconstruction (SIR) method was applied to data sets acquired at low dose. Image voxel noise was matched between the low dose SIR and high dose FBP reconstructions. CT perfusion maps were compared among the low dose FBP, low dose SIR and high dose FBP reconstructions. Numerical simulations of a dynamic CT scan at high and low dose (20:1 ratio) were performed to quantitatively evaluate SIR and FBP performance in terms of flow map accuracy, precision, dose efficiency, and spatial resolution. Results: Forin vivo studies, the 500 mA FBP maps gave −88.4%, −96.0%, −76.7%, and −65.8% flow change in the occluded anterior region compared to the open-coronary scans (four animals). The percent changes in the 25 mA SIR maps were in good agreement, measuring −94.7%, −81.6%, −84.0%, and −72.2%. The 25 mA FBP maps gave unreliable flow measurements due to streaks caused by photon starvation (percent changes of +137.4%, +71.0%, −11.8%, and −3.5%). Agreement between 25 mA SIR and 500 mA FBP global flow was −9.7%, 8.8%, −3.1%, and 26.4%. The average variability of flow measurements in a nonoccluded region was 16.3%, 24.1%, and 937.9% for the 500 mA FBP, 25 mA SIR, and 25 mA FBP, respectively. In numerical simulations, SIR mitigated streak artifacts in the low dose data and yielded flow maps with mean error <7% and standard deviation <9% of mean, for 30×30 pixel ROIs (12.9 × 12.9 mm{sup 2}). In comparison, low dose FBP flow errors were −38% to +258%, and standard deviation was 6%–93%. Additionally, low dose SIR achieved 4.6 times improvement in flow map CNR{sup 2} per unit input dose compared to low dose FBP. Conclusions: SIR reconstruction can reduce image noise and mitigate streaking artifacts caused by photon starvation in dynamic CT myocardial perfusion data sets acquired at low dose (low tube current), and improve perfusion map quality in comparison to FBP reconstruction at the same dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗
DOE OSTI2017

Field evaluation of remote wind sensing technologies: Shore-based and buoy mounted LIDAR systems

In developing a national energy strategy, the United States has a number of objectives, including increasing economic growth, improving environmental quality, and enhancing national energy security. Wind power contributes to these objectives through the deployment of clean, affordable and reliable domestic energy. To achieve U.S. wind generation objectives, the Wind and Water Power Program within the Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) instituted the U.S. Offshore Wind: Removing Market Barriers Program in FY 2011. Accurate and comprehensive information on offshore wind resource characteristics across a range of spatial and temporal scales is one market barrier that needs to be addressed through advanced research in remote sensing technologies. There is a pressing need for reliable offshore wind-speed measurements to assess the availability of the potential wind energy resource in terms of power production and to identify any frequently occurring spatial variability in the offshore wind resource that may impact the operational reliability and lifetime of wind turbines and their components and to provide a verification program to validate the “bankability” of the output of these alternative technologies for use by finance institutions for the financing of offshore wind farm construction. The application of emerging remote sensing technologies is viewed as a means to cost-effectively meet the data needs of the offshore wind industry. In particular, scanning and buoy mounted LIDAR have been proposed as a means to obtain accurate offshore wind data at multiple locations without the high cost and regulatory hurdles associated with the construction of offshore meteorological towers. However; before these remote sensing technologies can be accepted the validity of the measured data must be evaluated to ensure their accuracy. The proposed research will establish a unique coastal ocean test-bed in the Mid-Atlantic for the evaluation of LIDAR-based wind measurement systems to validate the accuracy of remotely measured wind data in marine applications. Specifically, the test-bed will be utilized to systematically evaluate the capability of emerging scanning LIDAR and buoy mounted vertically profiling LIDAR by: (1) Evaluating a fixed scanning LIDAR against land-based 50 and 60 meter high meteorological masts fitted with research quality cup-vane and/or sonic anemometers; (2) Evaluating a buoy mounted vertically profiling LIDAR fixed on land and floating in a sheltered bay against a co-located 60 meter high meteorological mast fitted with a research quality cup-vane and/or sonic anemometers and the fixed scanning LIDAR; and (3) Offshore field evaluation of both LIDAR platforms through a comparison of the fixed scanning LIDAR data and data obtained by the buoy mounted LIDAR located 10 miles offshore. The proposed research will systematically validate Light Detection and Ranging (LIDAR) based wind measurement systems and assess the temporal and spatial variability of the offshore wind resource in the Mid-Atlantic east of New Jersey. The goal of the proposed project is to address the technical and commercial challenges of the offshore wind energy industry by validating and assessing cost-effective, over ocean wind resource characterization technologies. The objective is to systematically evaluate the capability of both scanning and vertically profiling LIDARs to accurately measure 3D wind fields through comparison with fixed met masts and intercomparison among LIDAR platforms. Once validated, data collected by both buoy mounted vertically profiling LIDARs and shore-based, pulsed horizontally scanning LIDARs can be used to accurately assess offshore wind resources and to quantify the spatial and temporal variability in the offshore wind fields. One of the fundamental research questions to be addressed in phase 1 is the assessment of various measurement and data processing schemes to retrieve accurate wind vectors in the marine environment over large sampling ranges (10 to 12 km) and varying atmospheric aerosol levels. Atmospheric conditions and aerosol content within the coastal ocean region of the Mid-Atlantic seaboard of the US can vary significantly over short time periods in response to frontal passages and extratropical and tropical low pressure system passage offshore of the coast. Since aerosols provide the scattering medium for the determination of LIDAR Doppler shifts in the atmosphere the accuracy and range of LIDAR derived velocity measurements as a function of variation in aerosol content in the marine environment is a key research question to be addressed. In phase 1, it is desired to capture as much variation in atmospheric conditions and aerosol content as possible. To this end, collocated measurements of LIDAR and standard anemometer wind fields will be captured by the project PIs over all four seasons and during specific events (e.g., coastal low pressure system passage) in year 1. Additionally, since the meteorological masts are permanent structures, additional events can be captured over the three year duration of the field research project. All research instruments are owned by Fishermen’s Energy and made available to the PIs though a lease agreement as part of the DOE grant. Energy Fishermen’s Energy will be responsible for the operation and maintenance of the scanning LIDAR and met mast anemometers. On a daily basis, environmental data and systems performance indicators will be transmitted from each measurement station to the Fishermen’s project team consisting of both in-house personnel and equipment manufacturer engineers. Data sets include compiled LIDAR files as well as data sets from ancillary sensors. Diagnostic parameters to be monitored include standard deviations of measured values, battery levels and charging systems output, and the operational status. Once data have been confirmed as complete and reliable, files will be transferred to the Garrad Hassan (a subcontractor to Fishermen’s Energy) for incorporation in to the validation database, which is accessible to other scientific team members. Data collection times and durations will be determined by the PI and Co-PIs in consultation with instrument engineers to ensure the capture of data representative of the expected range of mid-Atlantic atmospheric conditions (e.g., temperature, moisture, coastal low pressure systems, tropical systems, rain, snow, fog). The collection and processing of the data is a function of site specific measurement requirements (Kelley et.al. 2007; Hannon et.al. 2008). To determine the optimal profiles of wind speed and direction from the LIDAR radial velocities as a function of azimuth angle, rigorous estimates of the bias and random error of each radial velocity estimate are required. Lockheed Martin Coherent Technologies, Inc., under contract with Fishermen’s Energy, will provide analyses of raw and processed data using various scan patterns to determine optimal performance settings for the pulsed scanning LIDAR. Once optimized, appropriate processing and analyses techniques will be evaluated by Garrad Hassan for use in validating the accuracy of the LIDAR wind field measurements against the standard anemometer measurements from the meteorological masts. The most attractive capability of the scanning LIDAR is the ability to provide high spatial resolution observations in a three-dimensional volume which provides superior statistical accuracy due to the large number of samples obtained. Each radial scan provides measurements in 100 range gates over a distance of 10 to 12 km at an update rate of 5 to 10 Hz and rotation of 2.5° per second. Each rotation at a fixed azimuth requires 2.4 minutes. Depending on the number of azimuths desired a complete scan can take up to 10 minutes or longer to complete. Once collected the radial velocities are processed to produce vector wind velocity estimates based on a set of data distributed in angle and range around points of interest, typically a standard grid within the radial wind map. To calculate wind vectors over a limited spatial area of interest for the comparison of data with other measurement platforms a localized least-squares approach has been applied by Hannon et.al. (2008) and a Variation Assimilation (VAR) processing technique has been applied by Chan and al Assimilation (VAR) processing technique has been applied by Chan and Shao (2006). Additionally, Kelley et.al. (2007) applied a “stare” technique that fixed a scanning LIDAR in both azimuth and elevation angles to measure over collocated sampling volumes of the LIDAR and a 3D sonic anemometer mounted to a fixed mast. Although the stare technique is limited to wind directions aligned with the sampling radial of the LIDAR, it does provide a direct comparison of sampling volumes. Each of the processing techniques described above (and possibly others) will be evaluated to determine the validity of the LIDAR derived wind fields in the marine environment. Numerical methods such as linear regression and comparison of probability density functions of wind fields measured by each instrument platform will be used to assess the processing techniques. Linear regression has the advantage of directly evaluating corresponding pairs of wind data measured by each instrument and can lend insight into deviations and bias between instruments as a function of wind speed. Assessment of the coherency between the probability density function of the wind measured by each instrument provides insight into processes that may not be accurately resolved by each instrument at specific frequencies. Once processed and assessed the most appropriate technique will be utilized to provide valid wind measurements from the pulsed scanning LIDAR. A detailed analysis of the measurement data from the LIDAR and the three meteorological towers and comparison of the coastal wind characteristics from the different systems will be performed by the CO_PI at NREL. This analysis will evaluate how the WT LIDAR performance and measurement of the wind characteristics vary with distance from the LIDAR and by atmospheric conditions; using the tower measurements at different distances (4.8, 9.6, and 19 km) from the LIDAR as a reference. The comparative analysis will include, to the extent possible, evaluation of parameters such as wind speed and direction distributions, wind shear, turbulence intensity and their variations by atmospheric conditions, month or season, and time of day.

17 WIND ENERGY↗
DOE OSTI2014

Seismic Modeling Workshop - Joint inversion [Slides]

Why joint inversion? It enables spanning spatial and data scales! Multiple benefits: Different data sets have different spatial coverage and resolution. “Standard” geophysical models are developed only to fit one type of data. Different data types have different strengths. Multiple challenges: Deal with different data bandwidths. Design responsive misfit norms; relative weighting of data sets. Make assumptions to model the different data; relationships between independent data sets.

58 GEOSCIENCES↗
DOE OSTI2015

The LaueUtil toolkit for Laue photocrystallography. I. Rapid orientation matrix determination for intermediate-size-unit-cell Laue data

A new method for determination of the orientation matrix of Laue X-ray data is presented. The method is based on matching of the experimental patterns of central reciprocal lattice rows projected on a unit sphere centered on the origin of the reciprocal lattice with the corresponding pattern of a monochromatic data set on the same material. This technique is applied to the complete data set and thus eliminates problems often encountered when single frames with a limited number of peaks are to be used for orientation matrix determination. Application of the method to a series of Laue data sets on organometallic crystals is described. The corresponding program is available under a Mozilla Public License-like open-source license.

Kalinowski, Jaroslaw A.↗
DOE OSTI2022

Cosmological Constraints from Gas Mass Fractions of Massive, Relaxed Galaxy Clusters

We present updated cosmological constraints from measurements of the gas mass fractions (f(gas)) of massive, dynamically relaxed galaxy clusters. Our new data set has greater leverage on models of dark energy, thanks to the addition of the Perseus cluster at low redshifts, two new clusters at redshifts z greater than or similar to 1, and significantly longer observations of four clusters at 0.6 < z < 0.9. Our low-redshift (z < 0.16) f(gas) data, combined with the cosmic baryon fraction measured from the cosmic microwave background (CMB), imply a Hubble constant of h = 0.722 +/- 0.067. Combining the full f(gas) data set with priors on the cosmic baryon density and the Hubble constant, we constrain the dark energy density to be Omega(Lambda) = 0.865 +/- 0.119 in non-flat Lambda cold dark matter (cosmological constant) models, and its equation of state to be in flat, constant-w models, respectively 41percent and 29percent tighter than our previous work, and comparable to the best constraints available from other probes. Combining f(gas), CMB, supernova, and baryon acoustic oscillation data, we also constrain models with global curvature and evolving dark energy. For the massive, relaxed clusters employed here, we find the scaling of f(gas) with mass to be consistent with a constant, with an intrinsic scatter that corresponds to just similar to 3percent in distance.

clusters: general – X-rays:↗
DOE OSTI2023

A Reproducible Validation of Algorithms for Estimating Array Tilt and Azimuth from Photovoltaic Power Time Series

In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real- world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5- based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.

azimuth↗
DOE OSTI2013

Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data

Purpose: For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated. Methods: Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space. Results: The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of Almost-Equal-To 0.047 {+-} 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of Almost-Equal-To 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary. Conclusions: In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Reproducible Validation of Algorithms for Estimating Array Tilt and Azimuth from Photovoltaic Power Time Series

In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real-world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5-based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.

algorithm validation↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

The Demand-Side Grid (dsgrid) Model Documentation

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supply-demand balance that must be maintained on electrical grids at all times and for all timescales, load is a major source of variability and uncertainty in grid operations. Despite this, utility load forecasting is typically done in a top-down manner that lacks the granularity in time, geography, end use, and technology that is needed to explore the potential impact of technological shifts; and many studies of future grid systems employ relatively simple scaling of historical loads, placing more emphasis on supply-side resources such as generation and transmission that are fewer in number and are better understood in terms of cost and performance. The primary purpose of the demand-side grid model (dsgrid) is to fill these gaps by creating comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current patterns and future projections of end-use load. Furthermore, the dsgrid platform uses a bottom-up methodology that allows highly resolved analysis of 'what-if' scenarios. dsgrid leverages detailed sectoral energy models to provide hourly time series of load by subsector, end use, and county covering a full year. Although dsgrid currently emphasizes electricity load data, its component sector models for residential buildings, commercial buildings, and industry provide information on other fuel use, including natural gas. The data sets can thus be leveraged to support analysis of numerous demand-side technology-driven changes, such as energy efficiency, electrification, and operational flexibility (i.e., demand response). The electricity use data are time-synchronized with solar and wind data sets so as to be suitable for use in power systems analysis. This report documents dsgrid and its initial data set, which covers electricity demand in the contiguous United States for the historical year of 2012. We start with a historical year to enable model calibration and validation, as well as the development of a mathematical description of our model residuals that can be applied to future-year 'what-if' scenarios to remove known errors between our bottom-up modeling and historical load shapes. This work is part of the Electrification Futures Study (EFS), for which the dsgrid team will be developing future load snapshots that describe projected year 2050 under baseline and a range of electrification assumptions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Ancient Rapid Radiation Explains Most Conflicts Among Gene Trees and Well-Supported Phylogenomic Trees of Nostocalean Cyanobacteria

Prokaryotic genomes are often considered to be mosaics of genes that do not necessarily share the same evolutionary history due to widespread horizontal gene transfers (HGTs). Consequently, representing evolutionary relationships of prokaryotes as bifurcating trees has long been controversial. However, studies reporting conflicts among gene trees derived from phylogenomic data sets have shown that these conflicts can be the result of artifacts or evolutionary processes other than HGT, such as incomplete lineage sorting, low phylogenetic signal, and systematic errors due to substitution model misspecification. Here, we present the results of an extensive exploration of phylogenetic conflicts in the cyanobacterial order Nostocales, for which previous studies have inferred strongly supported conflicting relationships when using different concatenated phylogenomic data sets. We found that most of these conflicts are concentrated in deep clusters of short internodes of the Nostocales phylogeny, where the great majority of individual genes have low resolving power. We then inferred phylogenetic networks to detect HGT events while also accounting for incomplete lineage sorting. Our results indicate that most conflicts among gene trees are likely due to incomplete lineage sorting linked to an ancient rapid radiation, rather than to HGTs. Moreover, the short internodes of this radiation fit the expectations of the anomaly zone, i.e., a region of the tree parameter space where a species tree is discordant with its most likely gene tree. In this work, we demonstrated that concatenation of different sets of loci can recover up to 17 distinct and well-supported relationships within the putative anomaly zone of Nostocales, corresponding to the observed conflicts among well-supported trees based on concatenated data sets from previous studies. Our findings highlight the important role of rapid radiations as a potential cause of strongly conflicting phylogenetic relationships when using phylogenomic data sets of bacteria. We propose that polytomies may be the most appropriate phylogenetic representation of these rapid radiations that are part of anomaly zones, especially when all possible genomic markers have been considered to infer these phylogenies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Deep Learning for Multigroup Cross-Section Representation in Two-Step Core Calculations

Here we investigate using deep learning, a type of machine-learning algorithm employing multiple layers of artificial neurons, for the mathematical representation of multigroup cross sections for use in the Griffin reactor multiphysics code for two-step deterministic neutronics calculations. A three-dimensional fuel element typical of a high-temperature gas reactor as well as a two-dimensional sodium-cooled fast reactor lattice are modeled using the Serpent Monte Carlo code, and multigroup macroscopic cross sections are generated for various state parameters to produce a training data set and a separate validation data set. A fully connected, feedforward neural network is trained using the open-source PyTorch machine-learning framework, and its accuracy is compared against the standard piecewise linear interpolation model. Additionally, we provide in this work a generic technique for propagating the cross-section model errors up to the k eff using sensitivity coefficients with the first-order uncertainty propagation rule. Quantifying the eigenvalue error due to the cross-section regression errors is especially practical for appropriately selecting the mathematical representation of the cross sections. We demonstrate that the artificial neural network model produces lower errors and therefore enables better accuracy relative to the piecewise linear model when the cross sections exhibit nonlinear dependencies; especially when a coarse grid is employed, where the errors can be halved by the artificial neural network. However, for linearly dependent multigroup cross sections as found for the sodium-cooled fast reactor case, a simpler linear regression outperforms deeper networks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

THE INTERPRETATION OF ROTATION MEASURES IN THE PRESENCE OF INHOMOGENEOUS FOREGROUND SCREENS

We analyze the redshift evolution of the rotation measure (RM) in the Taylor et al. data set, based on NVSS radio data at 21 cm, and compare with results from our previous work based on RMs determined at lower wavelengths, e.g., 6 cm. We find that, in spite of the same analysis, Taylor et al.'s data set produces neither an increase of the RM dispersion with redshift nor the correlation of RM strength with Mg II absorption lines that we found previously. We develop a simple model to understand this discrepancy. The model assumes that the Faraday rotators, namely the QSO's host galaxy and the intervening Mg II host galaxies along the line of sight, contain partially inhomogeneous RM screens. We find that this leads to an increasing depolarization toward longer wavelengths and to wavelength-dependent RM values. In particular, due to cosmological redshift, observations at fixed wavelength of sources at different redshift are affected differently by depolarization and are sensitive to different Faraday active components. For example, at 21 cm the polarized signal is averaged out by inhomogeneous Faraday screens and the measured RM mostly reflects the Milky Way contributions for low-redshift QSOs whereas polarization is relatively unaffected for high-redshift QSOs. Similar effects are produced by intervening galaxies acting as inhomogeneous screens. Finally, we assess the performance of RM synthesis on our synthetic models and conclude that the study of magnetic fields in galaxies as a function of cosmic time will benefit considerably from the application of such a technique, provided there is sufficient instrumental bandwidth. For this purpose, high-frequency channels appear preferable but not strictly necessary.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The kinematic properties of Milky Way stellar halo populations

ABSTRACT In the Gaia era, stellar kinematics are extensively used to study Galactic halo stellar populations, to search for halo structures, and to characterize the interface between the halo and hot disc populations. We use distribution function-based models of modern data sets with 6D phase space data to qualitatively describe a variety of kinematic spaces commonly used in the study of the Galactic halo. Furthermore, we quantitatively assess how well each kinematic space can separate radially anisotropic from isotropic halo populations. We find that scaled action space (the ‘action diamond’) is superior to other commonly used kinematic spaces at this task. We present a new, easy to implement selection criterion for members of the radially anisotropic Gaia–Enceladus merger remnant. Assuming a 1:1 ratio of Gaia–Enceladus stars to more isotropic halo, we find our selection achieves a sample purity of 86 per cent in our models with respect to contamination from the more isotropic halo. We compare this criterion to literature criteria, finding that it produces the highest purity in the resulting samples, at the expense of a modest reduction in completeness. We also show that selection biases that underlie nearly all contemporary spectroscopic data sets can noticeably impact the E−Lz distribution of samples in a manner that may be confused for real substructure. We conclude by providing recommendations for how authors should use stellar kinematics in the future to study the Galactic stellar halo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

2025 Annual INMM Graph and Tables for High Purity Germanium Detector Normalization Presentation

The data set includes gamma spectroscopy peak data for measurements taken with two different high purity germanium detectors using a mixed nuclide source and a U-235 fuel rod. There are a total of 5 specific energy peaks that were analyzed for the mixed nuclide source stemming from Am-241, Cd-109, Cs-137, and Co-60. There are a total of 3 specific energy peaks that were analyzed for the U-235 fuel rod. The data set includes the calculations and results from using a linear correction factor, absolute efficiency curve, and relative efficiency curve to compare the net peaks counts from two different detectors.

Drumm, Natalie Daphne [Sandia National Laboratorie↗