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This presentation provides an overview of the Alternative Fuels Data Center for the 2026 Transportation Technologies Annual Merit Review, including project objectives, milestone, approach to developing and maintaining the website, project accomplishments and progress, and collaboration with other national labs and external partners.
The 2002 Household Travel Survey for the municipality of Anchorage, Alaska, collected demographic, socioeconomic, and travel information about households and persons (age five and older) on an assigned, 24-hour travel period. The main objective of the study, conducted by NuStats, was to improve the transportation system. 2,035 households were recruited to participate in the study. Of these, 1,293 completed travel logs depicting detailed data on driving habits, such as purpose of the trips, time of the data, and mode of transportation—spanning from April 1, 2002, to May 17, 2002. This dataset is part of the Metropolitan Travel Survey Archive, which includes travel surveys from numerous public agencies across the United States and is archived by the Transportation Secure Data Center to ensure their continued public availability.
Reliable transport and thermodynamic data for multivalent ions in complex molten salts are scarce, limiting model fidelity for electrorefining and impurity control. Here, we report a comprehensive electrochemical characterization of SnCl₂ in LiCl–KCl–CaCl₂ (50.5–44.2–5.3 mol%) at 685 K, including the effects of Ni 2+ and Cu + impurities. Using cyclic voltammetry (CV), chronoamperometry (CA), and chronopotentiometry (CP), we quantified Sn 2+ and Ni 2+ diffusion with exceptional agreement across methods: Sn 2+ averaged (1.03 ± 0.10) × 10 −5 cm 2 s −1 , and Ni 2+ averaged (0.75 ± 0.19) × 10 −5 cm 2 s −1 . The tight confidence-interval overlap across CV, CA, and CP strengthens confidence in these values and is uncommon in molten chloride studies. Open-circuit-potential measurements provided standard apparent reduction potentials that closely match LiCl–KCl literature, indicating minimal shift with CaCl₂ present. The Sn 2+ /Sn couple behaves as a reversible two-electron soluble–insoluble process at 685 K; the Sn 4+ /Sn 2+ couple transitions to soluble–soluble behavior near 788 K, which may correlate with the decomposition of surface bound chlorostannates, though direct characterization remains to be established. In mixed systems, Cu+/Cu overlaps Sn 2+ /Sn, limiting Cusingle bondSn electroseparation, whereas the larger potential gap between Ni 2+ /Ni and Sn 2+ /Sn supports selective Ni removal. These internally consistent transport and thermodynamic data establish a validated basis for process modeling and optimization of Sn electrorefining and impurity management in LiCl–KCl–CaCl₂.
Abstract not provided.
This report documents the calculation of the commercial aviation (CA) non-airport crash density for Air Route Traffic Control Center (ARTCC) area associated with the Contiguous United States (CONUS) using the methodology outlined in the Department of Energy (DOE) Standard 3014-2006, Accident Analysis for Aircraft Crash Into Hazardous Facilities. For analytical purposes, the CA aircraft fleet is comprised of aircraft engaged in operations under Title 14 of the Code of Federal Regulations (CFR) Part 121 (i.e., Air Carrier) and Part 135 (i.e., Air Taxi).
This report documents the calculation of the General Aviation (GA) non-airport crash density for Department of Energy (DOE) sites and power generating facilities licensed by the Nuclear Regulatory Commission (NRC) using the methodology outlined in DOE-STD-3014-2006, Accident Analysis for Aircraft Crash Into Hazardous Facilities. The results of this report will be used by the American Nuclear Society (ANS) for the development of ANS standard 2.36, Accident Analysis for Aircraft Crash into Reactor and Nonreactor Nuclear Facilities.
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This report documents the evaluation of information and data available on the unclassified source term and radionuclide (RN) contamination for Central and Western Pahute Mesa (PM): Corrective Action Units (CAUs) 101 and 102. The Central and Western PM CAUs constitute two areas of the Nevada National Security Site (NNSS) used for underground nuclear testing (Figure 1-1). Eighty-five underground nuclear tests were conducted at the PM CAUs between 1965 and 1992 (NNSA/NFO, 2015b). Underground nuclear tests deposit radioactive elements in the subsurface, resulting in groundwater contamination in the vicinity of the underground test locations. At the PM CAUs, the testing resulted in more than 2.68 × 10 7 curies (Ci) of radioactivity (decay-corrected to September 30, 2012) remaining underground (Finnegan et al., 2016). Of the 85 underground nuclear tests conducted at the PM CAUs, three were shallow tests detonated within 200 meters (m) of the surface and referred to as the “near-surface tests”; the remaining 82 were detonated deeper than 200 m below ground surface (bgs) and are referred to as the “deep underground tests” in this document.
Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.
ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.
The Working Party on International Nuclear Data Evaluation Co-operation Subgroup 47 (WPECSG47) entitled "Use of Shielding Integral Benchmark Archive and Database for Nuclear Data Validation" was organised from 2019 and 2022 with the objectives to promote more systematic and wider use of shielding benchmark experiments in nuclear data (ND) and transport code validation and development, to provide feedback on the Shielding Integral Benchmark Archive and Database (SINBAD), and to promote its further development in coordination with the Expert Group on Physics of Reactor Systems (EGPRS). Altogether 9 meetings, the large majority (8) held remotely, were organised during the past 3 years to discuss the experience on the use of SINBAD, evaluation of new benchmarks and improvements to be contributed to the database which was severely neglected and lacking maintenance over the past ← 10+ years. Several proposals for new or updated benchmark evaluation were presented and discussed, such as FNG copper, LLNL pulsed spheres, CIAE iron sphere, KFK 1977 gamma measurements, Rez Fe sphere, ASPIS, ORNL Oxygen broomstick, TIARA and others. Complementing the database with new features was also discussed, for example providing the nuclear data sensitivity profiles more systematically would facilitate and better guide the use of data. Information on the geometry, (radiation source) and materials available in CAD format is expected to allow an easier and less error prone reference for computational model preparation and a potential input to CAD based workflows. Inputs for various transport codes and other benchmark data from participants have been shared via the NEA GitLab which could hopefully in the future evolve and form a bases for critically checked and validated benchmark data. Future development of SINBAD will be monitored by EGPRS and the newly created SINBAD Task Force.
Data in this package were used to calculate subsurface flow and solute (specifically dissolved nitrogen) transport in the Pumphouse Lower Montane (PLM) hillslope. These data are included in separate CSV files associated with each figure presented in the manuscript. The files describe site locations and elevations, weather data (air temperatures and precipitation), modeled evapotranspiration, time dependent groundwater elevations, calculated annual subsurface flow, hydraulic conductivity values, time trends in subsurface flow within different zones, pore water concentrations of total dissolve nitrogen (TDN), rates of TDN transport, soil water matric potentials following snowmelt, soil water contents during drainage, and evapotranspiration calculated by soil water mass balance versus by CLM4 model.
Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.
Solid polymer electrolytes have yet to achieve the desired ionic conductivity (>1 mS/cm) near room temperature required for many applications. This target implies the need to reduce the effective energy barriers for ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer–Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules 2023, 56, 15, 6051), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify anions leading to the energy barriers <30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers <20 kJ/mol, we have performed density functional theory (DFT) based calculations to connect the chemical structures of boron-based anions via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion hopping. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity >1 mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes.
Protein crystals grown in microfluidic droplets have been shown to be an effective and robust platform for storage, transport and serial crystallography data collection with a minimal impact on diffraction quality. Single macromolecular microcrystals grown in nanolitre-sized droplets allow the very efficient use of protein samples and can produce large quantities of high-quality samples for data collection. However, there are challenges not only in growing crystals in microfluidic droplets, but also in delivering the droplets into X-ray beams, including the physical arrangement, beamline and timing constraints and ease of use. Here, the crystallization of two human gut microbial hydrolases in microfluidic droplets is described: a sample-transport and data-collection approach that is inexpensive, is convenient, requires small amounts of protein and is forgiving. It is shown that crystals can be grown in 50–500 pl droplets when the crystallization conditions are compatible with the droplet environment. Local and remote data-collection methods are described and it is shown that crystals grown in microfluidics droplets and housed as an emulsion in an Eppendorf tube can be shipped from the US to the UK using a FedEx envelope, and data can be collected successfully. Details of how crystals were delivered to the X-ray beam by depositing an emulsion of droplets onto a silicon fixed-target serial device are provided. After three months of storage at 4°C, the crystals endured and diffracted well, showing only a slight decrease in diffracting power, demonstrating a suitable way to grow crystals, and to store and collect the droplets with crystals for data collection. This sample-delivery and data-collection strategy allows crystal droplets to be shipped and set aside until beamtime is available.
ENDF/B-VIII.0 cross section data is benchmarked on a simple Monte-Carlo model representative of the ex-core geometry of a PWR used for reactor vessel neutron fluence calculations. The benchmarking indicates that the ENDF/B-VIII.0 results show large differences as compared to previous evaluations, which have been extensively compared to measured data. This exercise suggests that ENDF/B-VIII.0 nuclear data is not suitable for ex-core LWR radiation transport applications. Evaluators are aware of the problems with the ENDF/B-VIII.0 evaluation of iron and work is underway to improve the evaluations for an upcoming release.
This document is the primary deliverable for a scoping study proposed to DOE National Laboratory Announcement Number LAB 19-2114 in the NNSA research area. The study supports user applications employing active neutron interrogation by providing a science plan to improve the modeling capability and the nuclear data that radiation transport codes use. Users rely on the accuracy of the elastic scattering and non-elastic cross-sections spanning thermal energies to 14 MeV (and higher in some cases) for modeling the neutron transport through complex geometries of materials potentially composed of many elements. While the elastic scattering cross-section data are accepted for all commonly occurring elements, the non-elastic cross-section data and the associated emission data include reaction channels that require attention. The study focused on the non-elastic reactions that emit secondary, also known as prompt, gammas with the premise that many users would benefit from improved modeling of these reactions. Many users develop material assay technologies based upon gamma signatures from radiative capture, inelastic scattering, and reactions on low-Z isotopes emitting multiple particles, so the nuclear data gaps, modeling deficiencies, and recommendations for addressing the shortfalls were assessed for these reactions. Fission gammas were excluded from this study because there are other efforts underway to address known shortfalls. Follow-on efforts that successfully execute the recommendations will tangibly improve to the ability to model gamma signatures and backgrounds for user applications, such as controlled substance detection, oil-well logging, and space exploration.
Thermal neutron scattering law (TNSL) data describe low-energy neutrons scattering off of bound materials, and can have a significant impact on modeling any system with slow neutrons, including nuclear reactors. Previous work to introduce TNSL data to neutron transport codes at LLNL focused on COG and TART, with the limitation that these codes require highly specialized data processing and formatting. We have recently increased efforts to process TNSL data with the LLNL nuclear data processing code FUDGE. FUDGE reads and writes the evaluated and processed files using the generalized nuclear database structure (GNDS). This process uncovered some significant difficulties in processing TNSL data, and unearthed assumptions made in current TNSL data processing that we have found inadequate. (authors)