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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

US Non-powered Dam Characteristics Inventory

The U.S. Non-Powered Dam (NPD) Characteristics Inventory contains data from various sources, including identifiers, locations, and a wide variety of characteristics. Characteristics included describe US NPDs and their surroundings, including attributes related to the physical nature or design of a dam, environmental conditions, safety conditions, socioeconomic aspects, and hydropower development potential. Data may be continuous or discrete/categorical. Data were obtained for each dam listed as an NPD (based on USACE NID). This inventory supports the NPD Explorer and NPDamCAT web apps developed by ORNL.

13 HYDRO ENERGY↗
DOE OSTI2020

Estimating vehicle fuel economy from overhead camera imagery and application for traffic control

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Dataset (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the dataset utility through three applications: the estimate of fuel consumption based on segmentation bounding boxes, vehicle discrimination for those vehicles with largest bounding boxes, and a fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set based on web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results show the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras.

Karnowski, Thomas↗
DOE OSTI2023

Spatial Microsimulation and Activity Allocation in Python: An Update on the Likeness Toolkit

Understanding human security and social equity issues within human systems requires large-scale models of population dynamics that simulate high-fidelity representations of individuals and access to essential activities (work/school, social, errands, health). Likeness is a Python toolkit that provides these capabilities for Oak Ridge National Laboratory's (ORNL) UrbanPop spatial microsimulation project. In step with the initial development phase for Likeness (2021 - 2022), we built out several foundational examples of work/school and health service access. In this paper, we describe expansion and scaling of Likeness capabilities to metropolitan areas in the United States. We then provide an integrated demonstration of our methods based on a case study of Leon County, FL and perform validation exercises on 1) neighborhood demographic composition and 2) visits by demographic cohorts (gender/age) obtained from point of interest (POI) footfall data for essential services (grocery stores). Taking into account lessons learned from our case study, we scope improvements to our model as well as provide a roadmap of the anticipated Likeness development cycle into 2023 - 2024.

Tuccillo, Joe↗
DOE OSTI2026

3_Drought_Experiment_1

This is an early experiment in the APPL facility at ORNL looking at the effect of drought on various Populus genotypes. Nisqually-1, WV-94, BESC-394, BESC-198, BESC-24, BESC-375

APPL↗
DOE OSTI2026

Early_Commisioning_Data

This is very early commissioning data (2019) from the newly established APPL facility at ORNL. Instruments, and technologies are in active development at this stage.

APPL↗
DOE OSTI2026

23_POPLAR_EXUDATES

This data is derived from sampling poplar trees at the GWAS common garden in Clatskanie, Oregon. This is intermediate level data from LCMS experiments that were done at ORNL from root tissue and soluble root exudates collected from 24 trees in the common garden in October of 2022.

CBI↗
DOE OSTI2022

LBNL Fault Detection and Diagnostics Datasets

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

AC↗
DOE OSTI2020

How to design a focusing guide: The large moderator case1

As continuously shaped super-mirrors are becoming available, the conceptual design of focusing guides should explore a wider range of possibilities to accomplish an efficient neutron beam extraction. Starting from a desired phase-space volume at the sample position and using an upstream ray-tracing approach, the acceptance diagram of any focusing guide can be calculated at the moderator position. To ensure high brilliance transfer and homogeneous coverage, the acceptance diagram should be fully included in the neutron source emission phase-space volume. Following this idea, the guide system can be scaled into dimensionless geometric figures that convey performance limits for a desired cross-section reduction. Moreover, if we impose a monotonic increase of the reflection angle with divergence angle at the sample position, the shape of the mirror is analytically determined. This approach was applied in the design of a focusing guide for SNAP instrument at SNS, at ORNL, USA. The results of McStas simulations are presented with different options included. Our approach facilitates finding an optimal solution for connecting multiple guide pieces to avoid excessive losses and ensure a homogeneous phase space coverage.

Frost, Matthew↗
DOE OSTI2021

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗
DOE OSTI2023

Validation of time-dependent shift using the pulsed sphere benchmarks

The detailed behavior of neutrons in a rapidly changing time-dependent physical system is a challenging computational physics problem, particularly when using Monte Carlo methods on heterogeneous high-performance computing architectures. A small number of algorithms and code implementations have been shown to be performant for time-independent (fixed source and k-eigenvalue) Monte Carlo, and there are existing simulation tools that successfully solve the time-dependent Monte Carlo problem on smaller computing platforms. To bridge this gap, a time-dependent version of ORNL’s Shift code has been recently developed. Shift’s history-based algorithm on CPUs, and its event-based algorithm on GPUs, have both been observed to scale well to very large numbers of processors, which motivated the extension of this code to solve time-dependent problems. The validation of this new capability requires a comparison with time-dependent neutron experiments. Lawrence Livermore National Laboratory’s (LLNL) pulsed sphere benchmark experiments were simulated in Shift to validate both the time-independent as well as new time-dependent features recently incorporated into Shift. A suite of pulsed-sphere models was simulated using Shift and compared to the available experimental data and simulations with MCNP. Overall results indicate that Shift accurately simulates the pulsed sphere benchmarks, and that the new time-dependent modifications of Shift are working as intended. Validated exascale neutron transport codes are essential for a wide variety of future multiphysics applications.

Palmer, Camille J.↗
DOE OSTI2024

Key results from examinations of seven high burnup pressurized water reactor spent nuclear fuel rods

At present, spent nuclear fuel (SNF) assemblies discharged from US commercial power plants are placed into dry storage following a short cooling time (<10 years) in the plant’s spent fuel pool. The process of packaging the spent fuel into dry-storage canisters includes a drying step to remove residual water from the canister. During the drying process, the fuel rod cladding may reach temperatures as high as 400°C. Oak Ridge National Laboratory (ORNL) is performing destructive examinations of high burnup (HBU) (>45 GWd/MTU) SNF rods to address knowledge and data gaps related to extended interim storage and eventual transportation for disposal. The rods examined include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. Three rods were subjected to a thermal transient to assess the effects of decay-heat-driven high temperatures expected during vacuum drying of the fuel as it is prepared for interim dry storage. The examinations focus on the composite fuel rod performance, as compared with the performance of defueled rod cladding, and establish the baseline mechanical properties of a fuel rod before interim dry storage. The key results of these examinations are presented, including the measured mechanical and fatigue properties, observations of cladding hydrogen pickup and hydride reorientation effects on rod performance, effects of the simulated drying temperatures on rod performance, and general conclusions of SNF performance in extended interim dry storage and transport. The rods were found to be strong and durable in the expected loading conditions, even considering the formation of radial hydrides associated with vacuum drying. The combined testing provides a broad body of data supporting extended interim storage and transportation performance of HBU spent fuel.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Octane Index Applicability over the Pressure-Temperature Domain

Modern boosted spark-ignition (SI) engines and emerging advanced compression ignition (ACI) engines operate under conditions that deviate substantially from the conditions of conventional autoignition metrics, namely the research and motor octane numbers (RON and MON). The octane index (OI) is an emerging autoignition metric based on RON and MON which was developed to better describe fuel knock resistance over a broader range of engine conditions. Prior research at Oak Ridge National Laboratory (ORNL) identified that OI performs reasonably well under stoichiometric boosted conditions, but inconsistencies exist in the ability of OI to predict autoignition behavior under ACI strategies. Instead, the autoignition behavior under ACI operation was found to correlate more closely to fuel composition, suggesting fuel chemistry differences that are insensitive to the conditions of the RON and MON tests may become the dominant factor under these high efficiency operating conditions. This investigation builds on earlier work to study autoignition behavior over six pressure-temperature (PT) trajectories that correspond to a wide range of operating conditions, including boosted SI operation, partial fuel stratification (PFS), and spark-assisted compression ignition (SACI). A total of 12 different fuels were investigated, including the Co-Optima core fuels and five fuels that represent refinery-relevant blending streams. It was found that, for the ACI operating modes investigated here, the low temperature reactions dominate reactivity, similar to boosted SI operating conditions because their PT trajectories lay close to the RON trajectory. Additionally, the OI metric was found to adequately predict autoignition resistance over the PT domain, for the ACI conditions investigated here, and for fuels from different chemical families. This finding is in contrast with the prior study using a different type of ACI operation with different thermodynamic conditions, specifically a significantly higher temperature at the start of compression, illustrating that fuel response depends highly on the ACI strategy being used.

02 PETROLEUM↗
DOE OSTI2021

As-Built Simulation of the High Flux Isotope Reactor

The Oak Ridge National Laboratory High Flux Isotope Reactor (HFIR) is an 85 MWt flux trap-type research reactor that supports key research missions, including isotope production, materials irradiation, and neutron scattering. The core consists of an inner and an outer fuel element containing 171 and 369 involute-shaped plates, respectively. The thin fuel plates consist of a U 3 O 8 -Al dispersion fuel (highly enriched), an aluminum-based filler, and aluminum cladding. The fuel meat thickness is varied across the width of the involute plate to reduce thermal flux peaks at the radial edges of the fuel elements. Some deviation from the designed fuel meat shaping is allowed during manufacturing. A homogeneity scan of each fuel plate checks for potential anomalies in the fuel distribution by scanning the surface of the plate and comparing the attenuation of the beam to calibration standards. While typical HFIR simulations use homogenized fuel regions, explicit models of the plates were developed under the Low-Enriched Uranium Conversion Program. These explicit models typically include one inner and one outer fuel plate with nominal fuel distributions, and then the plates are duplicated to fill the space of the corresponding fuel element. Therefore, data extracted from these simulations are limited to azimuthally averaged quantities. To determine the reactivity and physics impacts of an as-built outer fuel element and generate azimuthally dependent data in the element, 369 unique fuel plate models were generated and positioned. This model generates the three-dimensional (i.e., radial–axial–azimuthal) plate power profile, where the azimuthal profile is impacted by features within the adjacent control element region and beryllium reflector. For an as-built model of the outer fuel element, plate-specific homogeneity data, 235 U loading, enrichment, and channel thickness measurements were translated into the model, yielding a much more varied azimuthal power profile encompassed by uncertainty factors in analyses. These models were run with the ORNL-TN and Shift Monte Carlo tools, and they contained upwards of 500,000 cells and 100,000 unique tallies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Effect of Collimation on Diffraction Signal-to-Background Ratios at a Neutron Diffractometer

High diffraction signal-to-background ratios (SBRs), the ratios of diffraction peak integrated intensity over its background intensity, are desired for a neutron diffractometer to acquire good statistics for diffraction pattern measurements and subsequent data analysis. For a given detector, while the diffraction peak signals primarily depend on the characteristics of neutron beam and sample coherent scattering, the background largely originates from the sample incoherent scattering and the scattering from the instrument space. In this work, we investigated the effect of collimation on neutron diffraction SBRs of Si powder measurements using one high-angle area detector bank coupled with six different collimation configurations in a large and complex instrument space at the engineering materials diffractometer VULCAN, SNS, ORNL. The results revealed that the diffraction SBRs can be significantly improved by a proper coarse collimator that leaves no gap between the detector and the collimator, and the improvement of SBRs by a fine radial collimator was remarkable with a proper coarse collimator in place but not distinguishable without one. It was also found that the diffraction SBRs were not effectively improved by adding neutron absorbing element boron to the fine radial collimator body, which indicates that either the absorption of secondary scattered neutrons by the added boron is insignificant or the collimator base material (resin and ABS) alone attenuates background scattering sufficiently. These findings could serve as a useful reference for diffractometer developers and/or operators to optimize their collimation to achieve higher diffraction SBRs.

Yu, Dunji↗
DOE OSTI2021

Effects of Hybridization on Selective Catalytic Reduction (SCR) Thermal Management of a Medium Heavy-Duty Hybrid Work Truck

The increased market penetration of hybrid electric powertrains in medium heavy-duty (MHD) applications has provided a novel platform for vehicle research. One example of such a platform is the MHD parallel hybrid truck developed by Odyne Systems, LLC. In collaboration with Odyne Systems, LLC and the Department of Energy (DOE), Oak Ridge National Laboratory (ORNL) developed a validated vehicle plant model for this truck and tested the Odyne powertrain in a hardware-in-the-loop (HIL) environment. While testing in the HIL environment, the effects of reduced engine load, and thus catalyst heating, on the selective catalytic reduction (SCR) catalyst produced diminished hybrid improvement as the level of energy storage usage increased. This article will discuss these results and the potentially unforeseen interactions with modern aftertreatment systems when hybridizing conventional powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Real-Sim Interface: Enabling Multi-resolution Simulation and X-in-the-Loop Development for Connected and Automated Vehicles

We report connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory’s (ORNL’s) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network communication protocols. The network communication delays are analyzed for simultaneous connection of up to 20 simulators. Then, two example applications are studied to demonstrate the potential usage of the Real-Sim interface: (1) a centralized merging scenario where the dynamics of two ego vehicles are emulated with detailed vehicle and powertrain dynamics models and (2) a signal-controller-in-the-loop (SCIL) evaluation where one intersection of the simulated traffic scenario is controlled by a physical signal controller. This Real-Sim interface is a tool that allows researchers to bring real, tangible hardware and software into simulated environments to comprehensively evaluate and support a wide variety of CAV applications.

33 ADVANCED PROPULSION SYSTEMS↗