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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

GASP: Gradient-Aware Shortest Path Algorithm for Boundary-Confined 2-Manifold Reeb Graph Visualization

Reeb graphs are an important tool for abstracting and representing the topological structure of a function defined on a manifold. We have identified three properties for faithfully representing Reeb graphs in a visualization: they should be constrained to the boundary, compact, and aligned with the function gradient. Existing algorithms for drawing Reeb graphs are agnostic to or violate these properties. In this paper, we introduce an algorithm to generate Reeb graph visualizations, called GASP, that is cognizant of these properties, thereby producing visualizations that are more representative of the underlying data. To demonstrate the improvements, the resulting Reeb graphs are evaluated both qualitatively and quantitatively against the geometric barycenter algorithm, using its implementation available in the Topology ToolKit (TTK), a widely adopted tool for calculating and visualizing Reeb graphs.

Rahman, Sefat [University of Utah]↗
DOE OSTI2024

In-Situ Species Concentration Measurements In Ammonia-Mix Flames Using Ftir Spectroscopy

Hydrogen and ammonia represent two carbon-free fuel sources that could be used in place of current fossil energy sources in combustion systems. To develop optimized ammonia combustion systems, validated modeling tools are needed. In the open literature, it has been shown that the complex chemistry associated with fuel-bound nitrogen contained in ammonia differs greatly from natural gas or hydrogen combustion. As a result, several new chemical kinetic mechanisms have been developed. Many of these mechanisms have been validated experimentally, however this has primarily focused on bulk parameters such as laminar flame speed and ignition delay time. Critically, high quality measurements of species concentrations are needed under controlled conditions which are easily represented by simple models. In this paper, direct, in-situ measurements of species concentrations and gas temperature are performed in a laminar flat-flame burner. This arrangement enables comparison with 1D model predictions, better isolating chemical kinetics from the fluid dynamics. Quantitative species concentrations are determined by absorption spectroscopy using an FTIR spectrometer. Fuel compositions representative of cracked ammonia (NH 3 /H 2 ) and ammonia-natural gas (NH 3 /CH 4 ) are considered for rich and lean equivalence ratios. A major focus of the paper is on the selection of spectral features for nitric oxide and ammonia and correcting for large amounts of baseline H 2 O absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Linear-Complexity Tensor Butterfly Algorithm for Compressing High-Dimensional Oscillatory Integral Operators

This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extension of the so-called complementary low-rank property of existing matrix butterfly algorithms. The algorithm partitions the discretized integral operator tensor into subtensors of multiple levels and factorizes each subtensor at the middle level as a Tucker-type interpolative decomposition, whose factor matrices are formed in a multilevel fashion. For a d-dimensional (d > 1) integral operator discretized into a 2d-mode tensor with n2d entries, the overall CPU time and memory requirement scale as O(nd), in stark contrast to the O(nd log n) complexity of existing matrix algorithms such as matrix butterfly algorithms and fast Fourier transforms (FFTs), where n is the number of points per direction. When comparing with other tensor algorithms such as quantized tensor train (QTT), the proposed algorithm also shows superior CPU and memory performance for tensor contraction. Remarkably, the tensor butterfly algorithm can efficiently model high-frequency Green's function interactions between two unit cubes, each spanning 512 wavelengths per direction, which represents problems of scale over 512× larger than that existing butterfly algorithms can handle, with the same amount of computation resources. On the other hand, for a problem representing 64 wavelengths per direction, which is the largest size existing algebraic matrix algorithms can handle, our tensor butterfly algorithm exhibits 200x speedups and 30× memory reduction compared with existing ones. Moreover, the tensor butterfly algorithm also permits O(nd)-complexity FFTs and Radon transforms up to d = 6 dimensions.

Kielstra, P Michael↗
DOE OSTI2024

Performance assessment of near-fault buildings subjected to physics-based simulated earthquake ground motions with fling step

The effects of the co-seismic static offset (known as fling step) and associated velocity pulses on civil structures have been difficult to study because the static offset is typically removed during the processing of earthquake ground motion records. Simulated ground motions contain fling features and require no processing; therefore, they create new opportunities for representing fling features in seismic hazard analysis and assessing their influence on the seismic demands on near-fault structures. We use physics-based fault rupture simulations to study the characteristics of ground motions with fling step and the sensitivity of the near-fault structural demands to strong fling features. We uncover that simulated ground motions with a large fling step tend to have higher spectral intensity than those without a fling step at the same rupture distance, especially at periods longer than 2 s. As a result, the structural demands on flexible buildings tend to be the most sensitive to the fling features. Statistical analysis suggests that the ground motion spectral shape (represented by spectral accelerations at multiple periods) is—in most cases—a sufficient predictor of the structural demands on near-fault low-rise and mid-rise buildings at locations that are susceptible to strong fling effects. Finally, ground motion record selection experiments reveal that representing the spectral shape features at periods that are most relevant to a given structure may be an effective strategy to reduce the bias in the estimated demands on near-fault long-period structures when the available database of records is considered deficient in fling features.

Fling step↗
DOE OSTI2024

NbZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-zirconium (Nb-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

Thermochemical Modeling of Radionuclide Vapor-Liquid Equilibria in Sodium Pools for SFR Mechanistic Source Term Analysis

Mechanistic source term (MST) analysis of sodium fast reactors (SFR) requires understanding of various radionuclide (RN) transport phenomena influencing potential releases from the fuel to the environment. One such phenomenon includes the retention or release of RNs from the sodium coolant pool, representing the step after possible fuel failures and influencing transport to the cover gas region. Thermodynamic vapor-liquid equilibria (VLE) calculations were performed on systems representing SFR sodium pools containing oxygen impurities and radionuclide (RN) inventories. First, an assessment and recreation of a previously developed thermodynamic database was completed, including updates to thermodynamic parameters. The RN inventories used in VLE calculations represented hypothetical source terms that might be released to the pool during previously analyzed fuel failure scenarios. The calculations were performed for all possible combinations of sodium pool size (i.e., total oxygen) and number of failed fuel pins (i.e., total RNs). In this way, multiple ratios of the RN relative to the oxygen impurity (RN:O) were compared for their impact on RN volatility, which is discussed in terms of the vapor fraction (VF), defined as the fraction of the RN that is calculated to exist in the vapor phase at equilibrium above condensed phases of that element. Similar trends in VLE behavior are seen in the equilibrium calculation results for elements of similar chemistry, and for some element types, it was found that the RN:O ratio can be important due to oxide formation, which typically exist in the condensed phase.

Shahbazi, Shayan↗
DOE OSTI2024

Competitiveness Assessment of Decarbonizing Electricity and Process Heat Supply to a Campus with a Small Nuclear Reactor

This paper analyzes the competitiveness of siting a small nuclear reactor to support decarbonization of sites requiring tens of MW of electricity and/or process heat to support centralized heating and cooling system. This paper focuses on campuses as representative of sites with collections of buildings and research facilities with decarbonization needs represented by buildings heating, and electricity consumption by electrical loads which may include cooling via chilled water (e.g., for air conditioning and to cool down computer clusters). A nuclear reactor can be considered to decarbonize a site’s high-temperature steam generation used mostly for building heating needs, climate control, and hot water, by supplying process heat capabilities, while electricity decarbonization would be achieved mostly by the grid. However, a secondary application can be considered to maximize reactor utilization and avoid ramping down the reactor if the steam demand varies significantly throughout the year. Chilled water generation through steam-driven systems was identified as an attractive secondary option for the site analyzed, due to potential for plant design simplification, while electricity generation could be considered as well to reduce electricity purchases for a wider range of site applications. For a campus with peak 60MW thermal power demand, a small nuclear reactor with similar thermal power rating would almost eliminate CO2 emissions from steam generation and reduce electricity imports for chilled water production. A preliminary techno-economic feasibility study shows that a small nuclear reactor design that is optimized to support process heat can represent an economically feasible option when compared with other decarbonization alternatives.

Stauff, Nicolas E.↗
DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗
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 OSTI2026

Dataset 3: A National Dataset on Actionable Items in Improving Pooled Rideshare, 2025.

Dataset 3: A National Dataset on Actionable Items in Improving Pooled Rideshare.” 2025. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 3 (2025, N = 8,296). This dataset represents the third and final phase of a national survey aimed at understanding user acceptance and preferences related to pooled rideshare (PR) services in the United States. Building on insights from earlier phases, this phase expands both the sample size and the depth of analysis to support policymaking, transportation planning, and service design for sustainable mobility systems. The Phase 3 survey was administered online to a nationally representative sample of 8,296 U.S. adults. The sample includes a wide range of demographics. The survey retained core questions from previous phases while introducing 77 detailed service features (actionable items) to evaluate potential improvements to PR offerings. Each feature was designed to assess whether a specific improvement, such as enhanced safety measures, real-time ride tracking, or user training would increase participants’ willingness to adopt PR services. In addition, behavioral predictors, current rideshare habits, environmental attitudes, and perceived barriers (e.g., safety, privacy, and comfort) were captured. - Phase_3_Final - The dataset contains rows corresponding to individual respondents and columns representing survey items, demographic characteristics, and response values. The data is available in both .CSV and .SAV formats. - Phase_3_Final_MapFile - Accompanying this dataset is a data dictionary explaining each variable, value range, and coding schema. An .XLSX format of the full survey instrument is included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Variable Refrigerant Flow With 25% Upsizing Allowance for Heating

This documentation focuses on a single end-use savings shape upgrade - a variable refrigerant flow with heat recovery (VRF HR) heating and cooling system coupled with a dedicated outdoor air system (DOAS) for ventilation and where 25% upsizing (or 125% of the original size) is allowed for heating in colder climates (i.e., heating dominant regions). This document will primarily discuss the additional changes to the sizing algorithm and modeling approach, while a comprehensive overview of the fundamental modeling methodology and background of the VRF HR DOAS upgrade, including applicability and other key assumptions can be found in the original documentation: Variable Refrigerant Flow with Heat Recovery and Dedicated Outdoor Air System. To provide more context on the 25% upsizing algorithm, if the building is a cooling dominant (e.g., design cooling load higher than design heating load ), then the outdoor unit capacity of the VRF heat pump is sized based on the design cooling load. However, if the building is heating dominant, then this building is a candidate for the 25% upsizing allowance. Once the 25% upsizing is allowed, if the 25% upsized capacity (or 125% from the original size) represented with design condition exceeds the design heating load, then the design heating load is used for calculating the rated capacity of the outdoor unit. And if the 25% upsized capacity represented with the design condition does not exceed the design heating load, then the 25% upsized capacity represented with rated condition is used for the capacity of the outdoor unit while the remaining heating load is handled with the supplemental/backup electric resistance coil.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Radiological Impact of 2023 Operations at the Savannah River Site

This report presents the environmental dose assessment methods and the estimated potential doses to the public from 2023 Savannah River Site (SRS) air and liquid radioactive releases. Also documented are potential doses from special-case exposure scenarios, such as the consumption of wildlife or goat milk. Dose to the Offsite Representative Person The 2023 dose to the offsite representative person from SRS liquid releases was 0.14 mrem and from SRS air releases it was 0.016 mrem. To show compliance with the U. S. Department of Energy (DOE) all pathway dose standard of 100 mrem/yr, SRS conservatively adds these two doses for a total representative person dose of 0.16 mrem which is 0.16% of the DOE standard. Sportsman Doses Onsite Hunter: SRS conducts annual hunts to control onsite deer and feral hog populations. The estimated dose from consuming harvested deer or hog meat is determined for every onsite hunter. During 2023, the maximum potential dose an onsite hunter received was 9.42 mrem, or 9.42% of DOE’s 100 mrem/yr all pathway dose standard. Creek Mouth Fisherman: SRS estimated the maximum potential dose from fish consumption at 0.17 mrem from bass collected at the mouth of Lower Three Runs. This dose is 0.17% of the DOE standard. SRS bases this hypothetical dose on the low probability scenario that, during 2023, a fisherman consumed 24 kg (53 lbs) of bass caught exclusively from the mouth of Lower Three Runs. Release of Material Containing Residual Radioactivity SRS did not release any real property (land or buildings) in 2023. SRS unconditionally released a total of 13,324 items of personal property (such as tools) from radiological areas in 2023. Most of these items did not leave the Site. However, all of these items required no additional radiological controls post-survey as they met DOE Order 458.1 release criteria. Radiation Dose to Aquatic and Terrestrial Biota SRS conducts screening evaluations of plant and animal doses for aquatic and terrestrial ecosystems. For 2023, all SRS aquatic system locations passed the initial (Level 1) screenings and no further assessments were required at those locations. For the land-based systems evaluation, SRS performed initial screenings using concentration data from the five onsite radiological soil sampling locations. Typically, SRS collects and analyzes only one soil sample per year from each location. For 2023, all land-based locations passed their initial (Level 1) pathway screenings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High-Fidelity Modeling of a Type-5 Wind Turbine Gearbox (Intern Poster) [Poster]

Type-5 wind turbines are unique in their use of a permanent magnet synchronous generator, as well as their use of a hydraulic torque converter. This architecture presents an opportunity to provide steady and grid-ready energy without the need for a power converter. With infrastructure continuity and reliability being an important topic amongst renewable energies, researchers have been prompted to further investigate the benefits of type-5 turbines’ unique electromechanical configuration on stable electricity generation. Researchers involved in the WindSG project, SG standing for synchronous generator, are aiming to model a type-5 turbine using Real Time Digital Simulation (RTDS) to evaluate its efficacy in the grid. RSCAD, the software run on the RTDS, comes pre-loaded with electrical and electromechanical components to help simulate electrical generation and grid conditions. However, within this repertoire there is a lack of a component to represent a gearbox with high-fidelity. Within RSCAD’s case studies, the gearbox is often represented simply by a gear ratio value. This presented the task of developing a high-fidelity gearbox model in RSCAD for use in the larger RTDS type-5 wind turbine model. This poster describes a method of developing a lumped parameter mathematical model to represent a planetary-parallel-parallel gearbox in RSCAD for use in RTDS.

17 WIND ENERGY↗
DOE OSTI2024

IEA Wind TCP Task 49: Reference Site Conditions for Floating Wind Arrays

This report, prepared within Work package 1 of IEA Wind Task 49, presents reference site conditions for floating wind arrays to serve as a design basis for the techno-economic design of reference floating wind arrays. Data of the reference sites presented here are publicly available in an open database and thus support fast development and comparable design of floating wind arrays for various relevant conditions. The development of these reference sites drew on existing open access datasets and ongoing research projects of task participants. Six classes were identified that describe relevant key conditions for the design and development of floating wind arrays: met-ocean conditions, seabed conditions, coastal infrastructure, environmental impact, socio-economic impact, as well as regulations and permissions. The reference sites for the techno-economic design of floating wind arrays are based on a concept with building blocks to synthesize purpose-built site representations. In each of the identified classes with influencing design factors, building blocks are used to describe the characteristic properties and their spread. However, the latter three classes (i.e., environmental impact, socio-economic impact, regulations and permissions) are not included in the reference site conditions due to limited knowledge and lack of reliable criteria to quantify their impact on the techno-economic design in numeric parameters. Building blocks with key parameters for the techno-economic design of floating wind arrays are provided for met-ocean conditions, seabed conditions, and coastal infrastructure. For met-ocean conditions, multiple sites were selected for detailed analysis that represent a range of conditions across the pipeline of floating wind projects. Wind conditions and sea states are separated, and each location considers both the severity of wind and waves e.g. one site may have a moderate wave condition but severe wind condition. From this pipeline, eleven representative sites were selected where both site-specific analysis was available within the consortium, and where they represent different parts of the global pipeline. The eleven sites are: Hannibal (Italy), Humboldt (US), Ulsan (South Korea), MoneyPoint One (Ireland), Havbredey (UK), Fukushima (Japan), Utsira Nord (Norway), Gulf of Maine (US), Sud de la Bretagne II (France), Sorlige Nordsjo II (Norway). Each of these sites is summarized in the main report while more details about the studies and analyses behind the datasets are provided in the appendix. For seabed conditions, general information about the geotechnical parameters is provided and a baseline is established for the geotechnical parameters and stratigraphy that may be encountered on the sites. A set of six 'synthetic cases' is defined as building blocks providing the different parameters required for design under each case/soil condition. For the coastal infrastructure, general information about the main requirements is provided that a port should comply with to provide a satisfactory service during the construction of floating offshore wind arrays. Minimum port infrastructural requirements are provided for three types of ports.

17 WIND ENERGY↗
DOE OSTI2024

High-Fidelity Modeling of a Type-5 Wind Turbine Gearbox (Intern Technical Presentation) (Poster)

Type-5 wind turbines are unique in their use of a permanent magnet synchronous generator, as well as their use of a hydraulic torque converter. This architecture presents an opportunity to provide steady and grid-ready energy without the need for a power converter. With infrastructure continuity and reliability being an important topic amongst renewable energies, researchers have been prompted to further investigate the benefits of type-5 turbines’ unique electromechanical configuration on stable electricity generation. Researchers involved in the WindSG project, SG standing for synchronous generator, are aiming to model a type-5 turbine using Real Time Digital Simulation (RTDS) to evaluate its efficacy in the grid. RSCAD, the software run on the RTDS, comes pre-loaded with electrical and electromechanical components to help simulate electrical generation and grid conditions. However, within this repertoire there is a lack of a component to represent a gearbox with high-fidelity. Within RSCAD’s case studies, the gearbox is often represented simply by a gear ratio value. This presented the task of developing a high-fidelity gearbox model in RSCAD for use in the larger RTDS type-5 wind turbine model. This presentation describes a method of developing a lumped parameter mathematical model to represent a planetary-parallel-parallel gearbox in RSCAD for use in RTDS.

17 WIND ENERGY↗
DOE OSTI2024

Iron-Chromium-Aluminum Accident Tolerant Fuel Concept Source Term Accident Sequence Analysis - High Burnup Fuel Source Term Accident Sequence Analysis Supplement

To extend NUREG-1465 and high burnup fuel source term (SAND2023-01313) recommendations, representative radiological releases to containment – patterned after NUREG-1465 – have been evaluated for LWRs utilizing iron-chromium-aluminum (FeCrAl) alloys in place of zirconium-based alloys in major core structures (cladding and fuel canisters) and high burnup fuel with enrichments of 8% and 10% for PWRs and BWRs, respectively. Representative radionuclide releases are generated for this accident tolerant fuel concept by applying non-parametric bootstrap methods to MELCOR simulation results. Accident scenarios considered in this analysis include principle contributors to historical core damage frequency estimates for a range of nuclear reactor technologies representative of the operating U.S.A. fleet of nuclear reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Cr-coated Accident Tolerant Fuel Concept Source Term Accident Sequence Analysis - High Burnup Fuel Source Term Accident Sequence Analysis Supplement

To extend NUREG-1465 and high burnup fuel source term (SAND2023-01313) recommendations, representative radiological releases to containment – patterned after NUREG-1465 – have been evaluated for LWRs utilizing the chromium-coating on major zircaloy structures (cladding and fuel canisters) and high burnup fuel with enrichments of 8% and 10% for PWRs and BWRs, respectively. Representative radionuclide releases are generated for this accident tolerant fuel concept by applying non-parametric bootstrap methods to MELCOR simulation results. Accident scenarios considered in this analysis include principle contributors to historical core damage frequency estimates for a range of nuclear reactor technologies representative of the operating U.S.A. fleet of nuclear reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗