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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration

Water systems worldwide are experiencing climate change-induced shifts in drought properties like frequency, intensity, and duration, affecting water security and reliability. To develop and test effective drought preparedness plans, researchers often use synthetic weather generators to create hydrological scenarios that explore drought variability beyond historical records. Existing weather generators typically allow users to adjust streamflow statistics like percentiles or temporal correlation but do not directly control drought properties of frequency, intensity, and duration. To fill this gap, we propose FIND (Frequency, INtensity, and Duration) synthetic weather generator. FIND incorporates a standardized drought index to directly and in dependently control drought frequency, intensity, and duration in generated streamflow time series while preserving observed hydrological variability. Use cases for FIND include i) water systems analysis applications that seek to train and test drought strategies under historical and plausible future drought conditions, and ii) bottom-up vulnerability studies relating system vulnerability outcomes to specific changes in drought properties of frequency, intensity, and duration. Here, we demonstrate FIND’s versatility through three experiments: replicating historically observed drought properties, generating streamflow scenarios for multiple sites preserving correlation between their drought conditions, and generating a set of scenarios with direct and independent changes in drought properties. FIND source code is openly available for applications beyond the scope of this paper.

42 ENGINEERING↗
DOE OSTI2023

Calibrating radar wind profiler reflectivity factor using surface disdrometer observations

Abstract. This study uses surface disdrometer reflectivity factor estimates to calibrate the vertical and off-vertical pointing radar beams produced by an ultra high frequency (UHF) band radar wind profiler (RWP) deployed at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) Central Facility in northern Oklahoma from April 2011 through July 2019. The methodology consists of five steps. First, the recorded Doppler velocity power spectra are adjusted to account for Nyquist velocity aliasing and coherent integration filtering effects. Second, the spectrum moments are calculated. The third step increases the signal-to-noise ratio (SNR) due to inflated noise power estimates during convective rain events that cause SNR to be biased low. The fourth step determines the RWP calibration constant for one radar beam (called the “reference” beam) by comparing uncalibrated RWP reflectivity factors at 500 m above the ground to 1 min resolution surface disdrometer reflectivity factors. The last step uses the calibrated reference beam reflectivity factor to calibrate the other radar beams during precipitation. There are two key findings. The RWP sensitivity decreased by approximately 3 to 4 dB yr−1 as the hardware aged. This drift was slow enough that the reference calibration constant can be estimated over 3-month intervals using episodic rain events. The calibrated moments are available on the DOE ARM data archive, and the Python processing code is available on public repositories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A method to generate heating and cooling schedules based on data from connected thermostats

Internet-connected thermostats are a promising new source of temperature and operational data in homes because they record inside temperatures, setpoints, and HVAC runtimes every five minutes. Over 20 million Internet-connected thermostats have already been installed in American homes. Data from about 20,000 connected thermostats were collected and organized by climate zone, number of occupants, floor area, and day type. A method was developed to create up to 40 representative temperature schedules which, together, can more accurately capture the diversity of heating and cooling behaviors. These results are suitable for input into schedules for building energy simulation models. This information enables more realistic simulations of American heating and cooling behavior, leading to more accurate estimates of energy consumption and savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Narrow-Band Least-Squares Infrasound Array Processing

Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.

58 GEOSCIENCES↗
DOE OSTI2021

CalWave - TEAMER UMaine 2021 Tank Testing

This is the data submission to the teamer report for RFTS1 during which tank testing at UMaine W2 tank occurred. This submission includes the relevant information and recorded data of CalWave's UMaine wave tank testing conducted under TEAMER Request for Support Round 1. During the testing CalWave assessed an autonomous control approach for changing PTO gain parameters based on "on board" sea state detection. The data submitted includes an overview of normalized PTO gains based on sea state values and for a set of 8 relevant sea states the WEC motion. All data was collected on CalWave's SCADA system and UMaine's motion tracking system. The data submitted suffices the creation of figures as shown in the PDF report submitted alongside the data and the submission includes excel sheets (parameter gains), MATLAB structures (absorber motion), and the mentioned PDF report.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Air-coupled tsunamis generated from impacts and airbursts: Our understanding before Hunga-Tonga Hunga-Ha'apai

The effort to prevent or mitigate the effects of an impact on Earth is known as planetary defense. A significant component of planetary defense research involves risk assessment. Much of our understanding of the risk from near-Earth objects comes from the geologic record in the form of impact craters, but not all asteroid impacts are crater-forming events. Small asteroids explode before reaching the surface, generating an airburst, and most impacts into the ocean do not penetrate the water to form a crater in the sea floor. The risk from these non-crater-forming ocean impacts and airbursts is difficult to quantify and represents a significant uncertainty in our assessment of the overall threat. We are currently working to better understand impact scenarios that can generate dangerous tsunamis. One of the suggested mechanisms for the production of asteroid–generated tsunamis is by direct coupling of the pressure wave to the water, analogous to the means by which a moving weather front can generate a meteotsunami. To test this hypothesis, we ran a series of airburst simulations and provided time-resolved pressure and wind profiles to use as source functions for tsunami models. We used the CTH hydrocode to model the various airburst scenarios to compare to the results of other simulations and provide time dependent boundary conditions as input to shallow-water wave propagation codes. The strongest and most destructive meteotsunamis are generated by atmospheric pressure oscillations with amplitudes of only a few hPa1 (mbar), corresponding to changes in sea level of a few cm. The resulting wave is strongest when there is a resonance between the ocean and the atmospheric forcing. A Proudman resonance takes place when the atmospheric disturbance’s translational speed (U) equals the longwave phase speed $\sqrt{gh}$ of shallow water wave. Coupling is strongest when the Froude number (Fr=U/c) is unity. A weather front propagates much slower than the speed of sound, so meteotsunamis are most common and dangerous in shallow bodies of water such as the Mediterranean Sea or Lake Michigan. By contrast, the blast wave from an airburst or crater-forming impact propagates at a speed faster than a tsunami in the deepest ocean, and a Proudman resonance cannot be achieved even though the overpressures are orders of magnitude greater. However, blast wave profiles are N-waves in which a sharp shock wave leading to overpressure is followed by a more gradual rarefaction to a much longer-duration underpressure phase. Even though the blast outruns the water wave it is forcing, the tsunami should continue to be driven by the out-of-resonance gradient associated with the suction phase, which may depend strongly on the details of the airburst or impact scenario. The open question is whether there are any conditions under which such an airburst-driven tsunami can be dangerous enough to contribute to the overall impact risk. We have also identified other potential mechanisms for airburst-generated tsunamis: 1) reaction force at the surface from the plume ejected into space, which carries significant momentum, 2) expanding toroidal vortices at the surface, which travel more slowly than the shock wave and can generate a Proudman resonance in relatively shallow ocean (such as continental shelf), and 3) steam explosion from seawater ablation by a “Type II” (Libyan Desert Glass-type) airburst in which the hot vapor jet descends to the surface. On January 15, 2022, the Hunga-Tonga Hunga-Ha’apai volcano, located approximately 60 km north of Tongatapu, the main island of Tonga, violently erupted with a powerful explosion, culminating the period of volcanic activity that started in December of 2021. This event and resulting tsunamis provided an existence proof for the air pressure wave coupling mechanism we proposed. It also suggests that it can be stronger and more significant over much greater distances than we contemplated, leading to global tsunamis associated with impact events on land as well as in the water. Large atmospheric explosions generate global Lamb waves with larger amplitudes, longer periods, and slower speeds than the local and regional blast waves we modeled prior to that event. This paper reviews our analysis and modeling of airburst-driven tsunamis prior to the 2022 Hunga-Tonga Hunga-Ha’apai tsunami, which was the subject of two presentations at the 2023 Planetary Defense Conference and is the subject of another paper currently in preparation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Full Length Assembly Testing in PELICAN (Final Report)

In support of the development of the U.S. Department of Energy (DOE) Versatile Test Reactor (VTR), a thermal hydraulics test facility was constructed to generate experimental measurement of the pressure drop across a single full-scale assembly containing prototypic axial reflectors, fuel, and plena components. Constructed and operated at Argonne National Laboratory, the Pressure drop Experimental Loop for Investigations of Core Assemblies in Nuclear reactors (PELICAN) facility was designed to achieve hydraulic conditions identical to those anticipated for a full-scale fuel assembly located in the VTR core in the region with the highest flow rate. Using water as surrogate for liquid sodium, the flow loop was operated at elevated temperatures and pressures to match the thermophysical properties of liquid sodium and ensure matching Reynolds and Euler numbers. The measurement objectives for data generated from this test facility was driven primarily by the validation needs for code calculations and simulations of the reference VTR core. These objectives focused on the need to validate pressure drop results across the various segments of the fuel assembly as they relate directly to the pumping power and safety of the reactor. Presented in this report are experimental results and analytical comparisons based on testing of a full-length assembly in PELICAN. Housed within a hexagonal test section extending 3.4 m in length, the tested assembly features a prototypic lower reflector, grid plates, wire-wrapped rod bundle, upper reflector, and exit region. The rod bundle extends over 1.5 m in length and contains 217 individual wire-wrapped rods with dimensions that best reflect the reference VTR design. The as-tested bundle assembly was fabricated using 316 stainless steel 0.25-inch (6.35-mm) diameter rods wrapped with 0.04-inch (1.016-mm) diameter wire at a helical pitch of 10.51 inch (26.6 cm). Details of the method for in-house wire-wrapping, assembly, and installation are provided later in this report. Experimental measurements of pressure drop at 19 positions along the test assembly were recorded for a range of flow conditions, with special attention paid to key locations within the assembly, including component inlet and outlet, transition, and wire-wrapped rod bundle regions. Testing conditions were based on 110°C water with flow rates ranging from 50 to 450 GPM (3 to 27 kg/s) at the inlet of the test assembly generating Reynolds numbers and velocities up to ~8.0×10 4 and ~7.8 m/s, respectively, within the rod bundle region. Non-dimensional values for the friction factor were then calculated based on these experimental measurements and compared against those predicted by various analytical correlations available from open literature. Predictions by the upgraded Cheng and Todreas, Rehme, and Novendstern correlations fell within 4% to those values measured experimentally.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

NGEE Arctic Plant Traits: Plant Community Composition, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016

This dataset reports the characteristics of the dominant vegetation communities at the NGEE Arctic Kougarok field site, Kougarok Road Mile Marker 64 on the Seward Peninsula. Selected plots were surveyed from 18-23 July 2016. Environmental data (e.g., elevation, slope, soil moisture regime, disturbance type and degree, mean canopy height, etc.) were also recorded for each plot. Elevation measurements were updated 2020-08-18. All plant species (vascular plants, bryophytes and lichens) were recorded along with their percent cover in the plots as determined visually by the lead author. Plots were chosen subjectively in areas of homogeneous and representative vegetation and varied in size from 1-25 m2 depending on canopy structure and height. The site is an east-facing hillslope with vegetation that varies from the summit to the toeslope. The 30 total plots were selected as five replicate plots sampled from each of the six identified habitat types: 1) non-acidic mountain complex at the hillcrest, 2) dwarf-shrub lichen tundra on the shoulder, 3) alder shrublands predominantly on an elevational band across the backslope, 4) willow-birch tundra on the upper backslope, 5) tussock tundra on the footslope in inter-water tracks, and 6) tussock tundra mixed with willow-birch tundra or alder savanna in poor developed water tracks on the footslope. This dataset is comprised of two comma-separated (*.csv) files containing species and environmental data for the plant community composition plots. It also contains seven zipped folders of plot photographs, one map showing plot locations (*.pdf), one blank datasheet with the key to codes used in the field (*.pdf), select photos organized in a (*.pdf), and one User file (*.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Two-Phase Turbulence Statistics from High Fidelity Dispersed Droplet Flow Simulations in a Pressurized Water Reactor (PWR) Sub-Channel with Mixing Vanes

In the dispersed flow film boiling regime (DFFB), which exists under post-LOCA (loss-of-coolant accident) conditions in pressurized water reactors (PWRs), there is a complex interplay between droplet dynamics and turbulence in the surrounding steam. Experiments have accredited particular significance to droplet collision with the spacer-grids and mixing vane structures and their consequent positive feedback to the heat transfer recorded in the immediate downstream vicinity. Enabled by high-performance computing (HPC) systems and a massively parallel finite element-based flow solver—PHASTA (Parallel Hierarchic Adaptive Stabilized Transient Analysis)—this work presents high fidelity interface capturing, two-phase, adiabatic simulations in a PWR sub-channel with spacer grids and mixing vanes. Selected flow conditions for the simulations are informed by the experimental data found in the literature, including the steam Reynolds number and collision Weber number (Wec={40,80}), and are characteristic of the DFFB regime. Data were collected from the simulations at an unprecedented resolution, which provides detailed insights into the continuous phase turbulence statistics, highlighting the effects of the presence of droplets and the comparative effect of different Weber numbers on turbulence in the surrounding steam. Further, axial evolution of droplet dynamics was analyzed through cross-sectionally averaged quantities, including droplet volume, surface area and Sauter mean diameter (SMD). The downstream SMD values agree well with the existing empirical correlations for the selected range of Wec. The high-resolution data repository from the simulations herein is expected to be of significance to guide model development for system-level thermal hydraulic codes.

Saini, Nadish↗
DOE OSTI2020

Conserved white-rot enzymatic mechanism for wood decay in the Basidiomycota genus Pycnoporus

White-rot (WR) fungi are pivotal decomposers of dead organic matter in forest ecosystems and typically use a large array of hydrolytic and oxidative enzymes to deconstruct lignocellulose. However, the extent of lignin and cellulose degradation may vary between species and wood type. Here, we combined comparative genomics, transcriptomics and secretome proteomics to identify conserved enzymatic signatures at the onset of wood-decaying activity within the Basidiomycota genus Pycnoporus. We observed a strong conservation in the genome structures and the repertoires of protein-coding genes across the four Pycnoporus species described to date, despite the species having distinct geographic distributions. We further analysed the early response of P. cinnabarinus, P. coccineus and P. sanguineus to diverse (ligno)-cellulosic substrates. We identified a conserved set of enzymes mobilized by the three species for breaking down cellulose, hemicellulose and pectin. The co-occurrence in the exo-proteomes of H 2 O 2 -producing enzymes with H 2 O 2 -consuming enzymes was a common feature of the three species, although each enzymatic partner displayed independent transcriptional regulation. Finally, cellobiose dehydrogenase-coding genes were systematically co-regulated with at least one AA9 lytic polysaccharide monooxygenase gene, indicative of enzymatic synergy in vivo. This study highlights a conserved core white-rot fungal enzymatic mechanism behind the wood-decaying process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Updates to Relevance Vector Machine: Multiclass Classification, Variable Selection, and Proof-of-Concept Application to Safeguards Fresh Fuel Verification using List-Mode Neutron Collar Data

To expand the capabilities of safeguards authorities to verify the integrity of fresh fuel assemblies, Oak Ridge National Laboratory has retrofit the existing electronics of the JCC-71 uranium neutron coincidence collar, which contains 18 3 He neutron detectors and an external 241 AmLi(α, n) neutron interrogation source arranged to surround a fresh nuclear fuel assembly. The new electronics system allows analysts to record list-mode neutron multiplicity data in addition to the singles and doubles rates that are currently measured. Based on previous proof-of-concept research, analysis of these new data will identify off-normal fuel configurations in an assembly and characterize or localize the specific partial fuel defects. The purpose of this report it to document the analysis algorithm development and then to demonstrate its capability for the safeguards verification of fresh fuel assemblies using list mode neutron collar data. To analyze the complex list-mode data collected with the upgraded uranium neutron collar, multivariate classification algorithms are being developed using a novel classification method, the relevance vector machine. This approach may be applied to multiclass problems to estimate the probability that test data belongs to one of many possible classes of data. In addition, our method identifies the most useful variables/channels for making predictions, which illuminates the basis for the model’s predictions, and this interpretability is largely unique among data analytics methods. Variable selection occurs during model training and parameter tuning and does not need any external hyperparameter tuning routines. Finally, we apply the modified relevance vector machine to a simulated dataset of list-mode neutron collar data generated with the radiation transport code MCNP. The method can correctly identify off-normal fuel configurations, categorize the data according to four fuel defect scenarios, and rank the channels in the data according to prediction utility. For nuclear safeguards applications, it is concluded that this method has the potential to increase the sensitivity and reliability to detect missing fuel rods from a standard 17 x 17 Pressurized Water Reactor (PWR) fresh fuel assembly. Within this analysis, “off-normal” (i.e., missing fuel rods) were correctly classified in 17 simulated test scenarios with one quarter (25%) of the fresh fuel rods missing using a training data set of 58 simulated measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Hawaii Wave Surge Energy Converter (HAWSEC) OSU O.H. Hinsdale Basin

The following information and metadata applies to both the Phase I (Hydrodynamics) and Phase II (Full System Power Take-Off) zip folders which contain testing data from the OSU (Oregon State University) O.H. Hinsdale Wave Research Laboratory, from both OSU and the University of Hawaii at Manoa (UH). See zip folders provided further below in the downloads section. For experimental data of the full system, including PTO, see Phase II dataset. There are two main directories in each Phases's zip folder: "OSU_data" and "UH_data". The "OSU_data" directory contains data collected from their DAQ (data acquisition system), which includes all wave gauge observations, as well as body motions derived from their Qualisys motion tracking system. The organization of the directory follows OSU's convention. Detailed information on the instrument setup can be found under "OSU_data/docs/setup/instm_locations". The experiments conducted are documented in the "OSU_data/docs/daq_logs", which provides the trial number to the corresponding data located under "OSU_data/data" in several formats (e.g., ".mat" and ".txt"). Inside the trial directory, data is provided for each of the instruments defined in "OSU_data/docs/setup/instm_locations". The "UH_data" directory contains data collected from their DAQ. The data is stored in a ".tdms" file format. There are free plug-ins for Microsoft Excel and MathWorks MATLAB to read the ".tdms" format. Below are a few links providing methods to read in the data, but a Google search should identify alternatives sources if these no longer exist (valid as of January 2024): Excel: http://www.ni.com/example/27944/en/ MATLAB: https://www.mathworks.com/matlabcentral/fileexchange/30023-tdms-reader The Excel plugin is recommend to get a quick overview of the data. The UH data is organized by directory name, in which the sub-directories for each experiment contains a directory whose name defines the wave height and period for the experimental data within. For example, a directory name "H02_T0275" corresponds to an experiment with wave height 0.1m and a period of 2.75s. For random wave data, the gamma value is also included in the directory name. For example, a directory name "H02_T0225_G18" corresponds to an experiment with a significant wave height of 0.2m, a peak period of 2.25s, and a gamma value of 1.8, with each spectra being a TMA spectrum. For the free decay experiments, the directory name is defined by the initial angular displacement. For example, a directory name "ang05_run01" corresponds to an experiment with an initial angular displacement of 5 degrees. There is a dataset in the UH data for each corresponding experiment defined in the OSU DAQ logs. The ".tdms" data is output from the DAQ at fixed intervals. Therefore, if multiple files are contained within the folder, the data will need to be stitched together. Within the UH dataset, there are two input channels from the OSU DAQ providing a random square wave signal for time synchronization ("ENV-WHT-0010") and a high/low signal ("ENV-WHT-0012") to identify when the wave maker is active (+5V). The UH data is logged as a collection of channel outputs. Channels not in use for the OSU testing (either Phase I or Phase II) are marked "nan" below. If the sensor is disconnected, it will record noise throughout the experiment. Below are the channel definitions in terms of what they measure: GPS Time = time CYL-POS-0001 = position between flap and fixed reference CYL-LCA-0001 = force between flap and hydraulic cylinder REC-LPT-0001 = nan REC-HPT-0001 = nan REC-HPT-0002 = nan REC-HPT-0003 = nan HHT-HPT-0001 = pressure at exhaust ("head" only) REC-FQC-0001 = nan REC-FQC-0002 = nan HHT-FQC-0001 = flow at exhaust ("head" only) ENV-WHT-0001 = nan ENV-WHT-0002 = nan ENV-WHT-0003 = nan ENV-WHT-0010 = random signal from OSU DAQ ENV-WHT-0012 = high/low signal from OSU DAQ Also included is a calibration curve to convert the string pot data to flap pi...

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗