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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 109 records · Page 6

Precursory Off-Fault Deformation in Restraining and Releasing Step Overs: Insights From Discrete Element Method Models

Accelerating geophysical activity is detected preceding some, but not all, large earthquakes. This observation may indicate that no precursors occur before some earthquakes, or that the instrumentation lacks the required sensitivity. Here, to aid crustal monitoring efforts, we use discrete element method models to identify the locations and styles of deformation that may provide useful information about approaching fault reactivation. We model the reactivation of two healed rough faults in a variety of step over configurations, embedded in a host rock with varying amounts of damage subject to shear velocity loading parallel to the faults. Both the fault geometry and ratio of fault to host rock strength control the amount of off-fault deformation. Consistent with field observations, models with larger steps and more preexisting host rock damage produce higher amounts of off-fault deformation. We assess the size of the continuous regions of high velocities and strains to compare the value of the precursory information of each velocity and strain component. Comparing the three components of the velocity vector suggests that the fault-parallel velocity produces the largest and most temporally continuous regions of elevated velocity. The size of these regions increases toward failure, indicating the usefulness of tracking this component. Comparing the volumetric and shear components of the three-dimensional strain tensor suggests that during most of the interseismic period, the shear strain provides more information about approaching fault slip than the volumetric strain. However, in the days and months preceding fault reactivation, both the shear and volumetric strains provide similarly valuable information.

58 GEOSCIENCES↗

Accurate surface estimation from high-resolution 3D microwave images

Active millimeter and microwave imaging techniques can be used to create a high-resolution 3D image volume of a target’s reflectivity. Millimeter-wave imaging is commonly used for personnel security screening and numerous other applications. Backprojection based image reconstruction techniques form a 3D complex-valued volume. The complex-valued volume is commonly reduced to a real-valued volume by taking the magnitude. For anomaly detection and cross image registration of an object it is beneficial to generate an accurate representation of an object’s surface. Extracting a smooth and accurate surface from a magnitude only image is challenging. One difficulty is the magnitude image has limited resolution in the depth direction which normally limits precision to a moderate fraction of the depth resolution. Another difficulty is that the magnitude of the image depends heavily on the geometry and orientation of the object being imaged. The phase information in a complex-valued image volume provides a means to decouple the magnitude of the image from the geometry of an object and provide precision much finer than the depth resolution would indicate. This enables the generation of a smooth and accurate point cloud representation of the surface of an imaged object. A method to extract a point cloud from the phase information in a 3D complex-valued millimeter-wave image volume is developed and results with simulated and experimental data are presented.

Clark, Richard T.↗

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗

Extraction of the Collins-Soper Kernel from a Joint Analysis of Experimental and Lattice Data

We present a first joint extraction of the Collins-Soper kernel (CSK) combining experimental and lattice QCD data in the context of an analysis of transverse-momentum-dependent distributions (TMDs). Based on a neural-network parametrization, we perform a Bayesian reweighting of an existing fit of TMDs using lattice data, as well as a joint TMD fit to lattice and experimental data. We consistently find that the inclusion of lattice information shifts the central value of the CSK by approximately 10% and reduces its uncertainty by 40%–50%, highlighting the potential of lattice inputs to improve TMD extractions.

Avkhadiev, Artur [Massachusetts Inst. of Technolog↗

Machine-learning-assisted automation of single-crystal neutron diffraction

Neutron scattering is a powerful but expensive technique to study materials and discover new matter. Advanced detector technology has significantly improved the efficiency of neutron experiments, increasing the complexity of neutron data reduction and analysis. Machine learning (ML) brings new directions for neutron diffraction data reduction and experiment operation. Here, this work presents an ML-assisted data reduction and analysis method for precise recognition of Bragg peaks and the corresponding regions of interest; it can then automatically screen and align a measured crystal using the recognized peaks, and subsequently plan and optimize the data collection with user-provided information and uncertainty quantification values of detected peaks. This method shows robust performance in different complex sample environments and enables automated single-crystal neutron diffraction.

47 OTHER INSTRUMENTATION↗

KCMP Minnesota Tall Tower Carbon Dioxide Data 2007-2019

This data set contains carbon dioxide concentration values and supporting metadata information measured at a tall tower (KCMP radio station, 244 meters) site near St. Paul, Minnesota, USA. The data package includes a text file containing hourly continuous carbon dioxide concentration measurements from 2007 to 2019, including raw, de-spiked, and gap filled data. Carbon dioxide concentrations were measured at approximately 100 meters using tunable diode laser spectroscopy. The instrumentation was maintained at the base of the tall tower in a temperature-controlled building. This data was collected to track and improve the understanding of carbon dioxide levels over a twelve year period for the region.

54 ENVIRONMENTAL SCIENCES↗

Publishing statistical models: Getting the most out of particle physics experiments

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Macquarie Island Cloud and Radiation Experiment (MICRE) Field Campaign Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the Australian Antarctic Division (AAD), and the Australian Bureau of Meteorology (BOM) collaborated in deploying a variety of ground instrumentation to Macquarie Island between March 2016 and March 2018. Macquarie Island is located at 54.5° S, 158.9° E and is well situated for studying clouds over the Southern Ocean. The primary objective of the March 2016 to March 2018 deployment, hereafter the Macquarie Island Cloud and Radiation Experiment (MICRE), was to collect observations of surface radiation, cloud, precipitation, and aerosol properties. This report describes the instruments deployed and data sets collected, along with a summary of key data quality issues and information on some value-added products that are being developed to assist scientists in using the data.

54 ENVIRONMENTAL SCIENCES↗

Policy and Regulatory Environment for Utility-Scale Energy Storage: India

This report is part of a series investigating the potential for utility-scale energy storage in South Asia. The first part of the work is focused on opportunities and barriers for energy storage within existing policy and regulatory frameworks in the region. This report applies the previously-developed Energy Storage Readiness Assessment to evaluate the policy and regulatory environment for energy storage in India. Forthcoming reports will expand this analysis to other countries in South Asia. These evaluations are designed to provide insights on the opportunities and barriers for energy storage growth and deployment in each country. The second part of this work consists of techno-economic analysis to understand the drivers of energy storage investments in South Asia. Using NREL’s power system planning and operational models of South Asia, this analysis will identify potential storage applications and growth under various cost, policy, and demand growth scenarios. In addition, the regulatory and policy barriers and incentive mechanisms identified in the first task will be incorporated into the modeling task to understand their impact on energy storage deployment and operation. Together these studies will inform the applications and value of energy storage on South Asia power systems and policy and regulatory pathways to realize this value.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

National and Regional Initiatives to Promote Energy Efficiency and Renewable Energy Through State Energy Offices

The National Association of State Energy Officials (NASEO) worked with the U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE), Weatherization and Intergovernmental Programs Office (WIP) over a ten year period to provide technical assistance, research and analyses, and enhanced coordination between DOE and the State and Territory Energy Offices. Over the life of the agreement, NASEO worked with WIP and the states to provide statewide strategic energy plan analyses and recommendations, including customized technical assistance to the states; peer to peer financing assistance via NASEO’s Financing Committee and supporting activities; training on core energy policies and programs, including dialogues and resources to support energy-air coordination, a training for new State Energy Office Directors, peer to peer exchange via a rural energy taskforce, and support to states to enhance buildings efficiency, home energy labeling, and technology deployment opportunities; support for State Energy Program metrics; and regional coordination via regional coordinators and peer exchange opportunities both in-person and online. Over the life of the project, the position and role of State Energy Offices within state government has changed, with now 80 percent of State Energy Office Directors serving as their Governor’s energy advisor, or reporting directly to their Governor’s energy advisor. This shift in stature has made it ever more crucial for State Energy Offices to receive timely and relevant technical assistance across a range of energy issue areas. NASEO, in collaboration with WIP, was able to deliver this technical assistance, and energy efficiency and renewable energy deployment has accelerated across the country. The State Energy Office Directors and their staff continue to engage in NASEO’s Committees – many of which were supported through this agreement – and have provided formal and informal feedback on the value of the programs supported through this award (e.g., reporting via survey increased understanding of their roles and technical assistance offerings provided by WIP and NASEO following the New Director Trainings). Moreover, resources developed through this award (e.g., Comprehensive State Energy Planning Guidelines, State Energy Loan Fund Database, Rural Data Resources for State Energy Planning and Programs, The Value of Adding home Energy Score to Low-Income Energy Efficiency Programs, etc.) have been cited by states as instrumental to their understanding of specific energy issue areas and in many cases led directly to enhanced program design within a state (e.g. Iowa citing their review of NASEO’s Comprehensive State Energy Planning Guidelines as a necessary first step in their planning process, later following may of the steps outlined in the guidelines). The priorities of the states and federal government have evolved over the last decade, with an increasing focus on climate mitigation and adaptation, equity impacts and considerations, energy security and resilience, and enhanced energy efficiency and renewable energy technology deployment, but the roots of these new priorities are based in the state and federal policies and programs, and research and analyses, that were supported in part through this award and other complementary initiatives. NASEO looks forward to continuing to support the states, and collaborate and coordinate with DOE, as we build on this important foundation in the years ahead.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Addendum to ICSBEP Logbooks 12r, 13r, 14r and 15r for Polyethylene and Graphite Reflected HEU Metal Critical Experiments

This report documents the experimenters’ data sheets that were recently (2023) discovered for critical experiments with highly enriched uranium metal with polyethylene and graphite reflectors. These data sheets were produced at the time of the measurements, when the dimensional inspection reports for the polyethylene and graphite were available. However, at this writing, those inspection reports are not available but may be in unmarked storage for the Y-12 National Security Complex. However, the data sheets presented herein contain sketches of the experimental configurations and other additional information that is not in the logbooks, such as dimensions and masses of the graphite measurements. This report reproduces the experimental data sheets that complement the logbooks. Some of the data on these sheets and the logbooks can be used to infer the dimensions and masses that are not documented. Some obvious mistakes in the logbook and data sheets have been corrected. In the reproduced data sheets, some of the information is not clearly visible: some information, such as average values, can be obtained from other data in the sheets or logbook. This report should be sent to the International Criticality Safety Benchmark Program at Idaho National Laboratory to complement the existing Oak Ridge Critical Facility logbooks there.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Post-irradiation Examination of Eurofer-97 Steel Irradiated to 20 dpa at 200–400°C in HFIR under the EUROfusion (ORNL-KIT) Collaboration Program

The Oak Ridge National Laboratory/Karlsruhe Institute of Technology (ORNL/KIT) collaboration focuses on research involving irradiation experiments and post-irradiation examinations (PIE) of isotopically modified Eurofer-97 steels for fusion reactor applications. This collaboration capitalizes on ORNL’s expertise in radiation effects on materials and its capabilities in irradiation and PIE. The research aims to qualify Fe-9Cr-based Eurofer-97 steel under simulated fusion conditions, which include high doses and high transmutation rates. A unique isotopic modification technique is utilized in the research, wherein high-transmutation isotopes such as Fe-54 and Ni-58 are alloyed into the Eurofer steel to align with the expected helium production rates in fusion reactor conditions. This report presents the results of post-irradiation mechanical testing activities for the EUROFER-97/2 specimens after irradiation to ~20 dpa at various irradiation temperatures. The irradiation doses and temperatures for the ES capsules ranged from 18.4–20.4 dpa and 202–256°C, respectively. The mechanical property datasets, obtained through baseline testing and PIE of the tensile and fracture specimens, include microhardness data from 93 irradiated and non-irradiated tensile specimens and 28 irradiated bend bar specimens, uniaxial tensile property data from 45 irradiated and non-irradiated tensile specimens, and fracture toughness data from the bend bar specimens. Further data analyses provide statistical information on the microhardness values and tensile properties, as well as the reference ductile-brittle transition temperature (T 0 ) data.

36 MATERIALS SCIENCE↗

Impact of Lateral Alignment on the Energy Savings of a Truck Platoon

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The impact of different following-truck lateral offsets had a measurable impact, with up to 4% reduction in total fuel-savings (relative to an isolated vehicle condition) observed for offsets up to 1.3 m. The fuel-consumption savings on the straight segments of the track exceeded those on the curved segments by upwards of 6% and highlight some potential differences expected between close-track testing and on-highway use.

adaptive cruise control↗

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), 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↗

Impact of Lateral Alignment on the Energy Savings of a Truck Platoon: Preprint

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The impact of different following-truck lateral offsets had a measurable impact, with up to 4% reduction in total fuel-savings (relative to an isolated vehicle condition) observed for offsets up to 1.3 m. The fuel-consumption savings on the straight segments of the track exceeded those on the curved segments by upwards of 6% and highlight some potential differences expected between close-track testing and on-highway use.

adaptive cruise control↗

Quantifying the Tangible Value of Public Electric Vehicle Charging Infrastructure

The lack of an extensive public recharging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Because charging infrastructure is likely to be underutilized during the early stages of market development, it is difficult for decision makers to decide how much to invest in public charging stations. Quantifying the value of public charging infrastructure to current and potential future owners of PEVs is essential for estimating the benefits of charging stations to current PEV owners and for predicting the impact on future PEV sales. This paper estimates consumers’ willingness to pay for public charging infrastructure in the context of utility maximization. The objective is to provide a method for valuing charging infrastructure that can inform investment decisions and be used in forecasting models to predict the impacts on future PEV sales. A basic theory of the tangible value of charging infrastructure is developed as a function of PEV type, range, recharging time and existing infrastructure. Existing simulation studies provide functional relationships that quantify the ability of charging infrastructure to enable additional miles of electrified travel. The enabled travel functions are used to predict impact of infrastructure deployment on incremental electrified travel for 1) plug-in hybrids and 2) intra-regional and 3) inter-regional travel by all-electric vehicles. The willingness to pay for increased electrified miles is derived from the willingness to pay for increased electric driving range, based on econometric studies of plug-in vehicle choice. The result is a set of three functions that can be used to calculate the marginal willingness-to-pay for public charging infrastructure as a function of vehicle attributes, existing charging infrastructure, energy prices and annual vehicle travel.

33 ADVANCED PROPULSION SYSTEMS↗

Parameter Calibration for Johnson Cook and Preston-Tonks-Wallace Material Strength Models with Uncertainty Quantification

In this study we perform a Bayesian calibration of the parameters in the Johnson Cook (JC) material strength model, with uncertainty, using experiments with for a range of low and medium strain rates. For the parameter calibration, we used a variational Bayesian approach with experimental data from Hopkinson bar and quasi-static tests done on Oxygen Free High Conductivity (OFHC) copper. The estimated parameter values matched well with experimental data for a modest range of strain rates and temperatures. Through this method, we also recovered uncertainty and correlation information for the estimated parameter values. We also compared our results to a calibration of parameters in the Preston-Tonks-Wallace (PTW) material strength model, using the same variational Bayesian method. The parameters estimated for both models provided good agreement with the experimental data.

36 MATERIALS SCIENCE↗

Statistical Approaches for Pebble Bed Reactor Operations and Safeguards

The design of pebble bed reactors (PBRs) and their method of operation align more closely with statistical approaches used in manufacturing and process control than traditional safeguards statistical approaches. The reason is PBRs will employ a nondestructive assay (NDA) measurement (burnup measurement system [BUMS]) that is part of the fuel handling system supporting discharge decisions in addition to the reactor code which monitor performance. The integration of these two approaches provides the opportunity to monitor reactor performance statistically for both operations and safeguards in ways not achievable using other reactor designs. For light water reactors (LWRs), knowledge about reactor code performance in predicting irradiated special nuclear material (SNM) content historically was only achieved from special measurement campaigns or from fuel reprocessing. Conversely, through statistical comparison of the BUMS with the reactor code-predicted values, PBRs can achieve this in real time. The resulting SNM distribution is an indicator of reactor performance because factors such as transit time and path of the pebble fuel through the reactor determine the plutonium production and uranium depletion. By analyzing the predicted and measured values, opportunities exist to adjust operating parameters, fuel design, and other characteristics to optimize performance and fuel utilization. From a safeguards perspective, this approach also provides the information necessary to validate declared values and evaluate whether the reactor is being operated as expected. This paper outlines statistical approaches for PBRs that can be used to support both operations and safeguards.

Ball, Cory↗