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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Extensions to models of the galaxy–halo connection

ABSTRACT We explore two widely used empirical models for the galaxy–halo connection, subhalo abundance matching (SHAM) and the halo occupation distribution (HOD), and compare them with the hydrodynamical simulation IllustrisTNG (TNG) for multiple statistics quantifying the galaxy distribution at $n_{\rm gal}\approx 1.3\times 10^{-3}\, ({\rm Mpc}\,h^{-1})^{-3}$. We observe that in their most straightforward implementations, both models fail to reproduce the two-point clustering measured in TNG. We find that SHAM models that use the relaxation velocity, Vrelax, and the peak velocity, Vpeak, perform best, and match the clustering reasonably well, although neither captures adequately the one-halo clustering. Splitting the total sample into sub-populations, we discover that SHAM overpredicts the clustering of high-mass, blue, star-forming, and late-forming galaxies and underpredicts that of low-mass, red, quiescent, and early-forming galaxies. We also study various baryonic effects, finding that subhaloes in the dark-matter-only simulation have consistently higher values of their SHAM-proxy properties than their full-physics counterparts. We then consider a 2D implementation of the HOD model augmented with a secondary parameter (environment, velocity anisotropy, σ2Rhalf-mass, and total potential) tuned so as to match the two-point clustering of the IllustrisTNG galaxies on large scales. We analyse these galaxy populations adopting alternative statistical tools such as galaxy–galaxy lensing, void–galaxy cross-correlations, and cumulants of the density field, finding that the hydrodynamical galaxy distribution disfavours σ2Rhalf-mass and the total potential as secondary parameters, while the environment and velocity anisotropy samples are consistent with full physics across all statistical probes examined. Our results demonstrate the power of examining multiple statistics for determining the secondary parameters that are vital for understanding the galaxy–halo connection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

09 - BIOMASS FUELS↗
DOE OSTI2022

Longitudinal dynamics and particle production in relativistic nuclear collisions

This work presents a three-dimensional dynamical initialization model for relativistic heavy-ion collisions, implementing local energy-momentum conservation and baryon charge fluctuations at string junctions. Constraining parameters using experimental data from p + p collisions at various collision energies, the model provides a very good description of the charged hadron and net proton rapidity distributions in Au +Au collisions from 7.7 to 200 GeV and Pb + Pb collisions at 8.77 and 17.3 GeV. Here we demonstrate the importance of fluctuations of baryon densities to string junctions for describing net-proton distributions at collision energies of 62.4 and 200 GeV. Including this improved baryon stopping description along with the requirement of strangeness neutrality also yields a good description of identified particle yields as functions of the collision energy above 7.7 GeV. We further study asymmetric p + Al and (p,d, 3 He) + Au collisions at the top RHIC energy and p + Pb, Xe + Xe, and Pb + Pb collisions at LHC energies. We identify the produced particle rapidity distributions in asymmetric collision systems as particularly useful for constraining models of the early-time longitudinal dynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Holistic Numerical Calibration of the iMESA Electrostatic Analyzer

A drifted Maxwellian velocity distribution is the most common model used to interpret the data from low-energy charged-particle instruments onboard spacecraft that are used to investigate the ambient plasma environment in the low Earth orbit (LEO). An original method is presented for determining the flow parameters (density, temperature, and flow energy) of such a distribution from the output of the integrated miniaturized electrostatic analyzer, which has been successfully flown on several LEO missions. Rather than attempting to deconvolve from the on-orbit data the analyzer’s response to an ideal, monoenergetic input, numerical simulation is used to predict and parameterize the response of the device to an input distribution that includes an isotropic, non-zero temperature, yielding a straightforward method for extracting the flow parameters from the spacecraft data. The method is computationally simple enough to be incorporated into a robust algorithm suitable for rapid batch processing or real-time analysis of data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2024

Space-Time Structured Plasma Waves

Electrostatic waves play a critical role in nearly every branch of plasma physics from fusion to advanced accelerators, to astro, solar, and ionospheric physics. The properties of planar electrostatic waves are fully determined by the plasma conditions, such as density, temperature, ionization state, or details of the distribution functions. Furthermore we demonstrate that electrostatic wave packets structured with space-time correlations can have properties that are independent of the plasma conditions. For instance, an appropriately structured electrostatic wave packet can travel at any group velocity, even backward with respect to its phase fronts, while maintaining a localized energy density. These linear, propagation-invariant wave packets can be constructed with or without orbital angular momentum by superposing natural modes of the plasma and can be ponderomotively excited by space-time structured laser pulses like the flying focus.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Generation of Terawatt Attosecond Pulses from Relativistic Transition Radiation

When a femtosecond duration and hundreds of kiloampere peak current electron beam traverses the vacuum and high-density plasma interface, a new process, that we call here relativistic transition radiation (RTR), generates an intense ~ 100 as pulse containing ~ 1 terawatt power of coherent vacuum ultraviolet (VUV) radiation accompanied by several smaller femtosecond duration satellite pulses. This pulse inherits the radial polarization of the incident beam field and has a ring intensity distribution. This RTR is emitted when the beam density is comparable to the plasma density and the spot size much larger than the plasma skin depth. Physically, it arises from the return current or backward relativistic motion of electrons starting just inside the plasma that Doppler up shifts the emitted photons. The number of RTR pulses is determined by the number of groups of plasma electrons that originate at different depths within the first plasma wake period and emit coherently before phase mixing.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

3D-Printable Fluoropolymer Gas Diffusion Layers for CO 2 Electroreduction

The electrosynthesis of value-added multicarbon products from CO 2 is a promising strategy to shift chemical production away from fossil fuels. Particularly important is the rational design of gas diffusion electrode (GDE) assemblies to react selectively, at scale, and at high rates. However, the understanding of the gas diffusion layer (GDL) in these assemblies is limited for the CO 2 reduction reaction (CO 2 RR): particularly important, but incompletely understood, is how the GDL modulates product distributions of catalysts operating in high current density regimes > 300 mA cm -2 . Here, 3D-printable fluoropolymer GDLs with tunable microporosity and structure are reported and probe the effects of permeance, microstructural porosity, macrostructure, and surface morphology. Under a given choice of applied electrochemical potential and electrolyte, a 100× increase in the C 2 H 4 :CO ratio due to GDL surface morphology design over a homogeneously porous equivalent and a 1.8× increase in the C 2 H 4 partial current density due to a pyramidal macrostructure are observed. Overall, these findings offer routes to improve CO 2 RR GDEs as a platform for 3D catalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Eulerian three-fluid flow framework for the study of fuel dispersal behavior under loss-of-coolant accident conditions

As the nuclear industry explores new fuel designs to accommodate increased burnup, studying fuel behavior during loss-of-coolant accidents is essential for ensuring the safe operation of light-water reactors. Here, this study presents a computational model to simulate the intricate three-phase flow dynamics of fuel dispersal following cladding breach, where solid fuel particles and carrier fission gases interact with the surrounding liquid or gas medium. The model utilizes a Eulerian three-fluid framework to depict the phase interaction within interpenetrating continua, treating fragmented fuel as granular material. Closure relationships for bulk-flow properties of the solid phase and detailed interfacial exchange terms in high-temperature, high-speed gas-solid-liquid flow scenarios are proposed. The model was validated using adiabatic experimental results in the literature, focusing on characterizing fuel dispersal behavior during loss-of-coolant accidents conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched probability density functions reported in experimental studies. Key highlights of this study include the theoretical description of a coupled three-phase flow with phase change, interpretation of the high-pressure boundary conditions and insights into the transient behavior of gas-solid-liquid phase dynamics during a simulated high-speed dispersal event.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
DOE OSTI2023

The influence of chemical short-range order on the nanoindentation properties of high-entropy alloys prepared via laser powder bed fusion

Chemical short-range order (CSRO) plays an instrumental role in determining the mechanical properties of high-entropy alloys (HEAs). Current methods for controlling CSRO mainly focus on cast HEAs, which can not solve the problem of grain coarsening caused by homogenization treatments. Laser Powder Bed Fusion (LPBF), which has extremely high cooling rates, can suppress compositional segregation, refine grain structures, and achieve low CSRO. This offers a promising avenue for quantitatively controlling CSRO without the need for homogenization treatments. In this study, we investigate the CSRO of HEAs fabricated via LPBF and elucidate their influence on nanoindentation behavior. The results show that, at the low cooling rate, the average size of CSRO is 0.66 nm and they occupy 2.8% of the cross-sectional area, while at high cooling rate, the corresponding values are 0.92 nm and 9.9%, respectively, both of which are significantly smaller than those of cast HEAs. Furthermore, by analyzing samples with different CSROs, we observe that the hardness increases and then decreases with the increase of CSRO, primarily due to the preferential formation of dislocations in regions enriched with Cr-Mn-Ni. With the improvement of CSRO, both the size and quantity of Cr-Mn-Ni rich region gradually increased; subsequently, as multiple Cr-Mn-Ni rich regions tend to merge into one during the CSRO improvement, the size of the Cr-Mn-Ni rich region continued to increase while the quantity gradually decreased; when the size of the Cr-Mn-Ni rich region is moderate and widely dispersed, the dislocation density is higher with much more uniform distributions, which favorably improves nanoindentation hardness.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental demonstration and modeling of a robotic neutron detector with spectral and directional sensitivity for treaty verification

International safeguards and arms control agreements often require labor-intensive, intrusive onsite inspections to perform verification tasks. The ability to localize a neutron source and/or characterize a neutron field may be imperative for identifying anomalies. We are interested in the role of autonomous mobile robots, which, if designed properly, may be more effective and efficient and less intrusive than their human counterparts. Toward developing such a capability, we, for this study, have constructed the N-SpecDir Bot, comprised of three boron-coated straw detectors azimuthally-distributed within a cylinder of high-density polyethylene, which is mounted on an omni-directional robotic platform. Our N-SpecDir Bot is specifically designed to provide spectral and directional sensitivity, in addition to gross counts, by utilizing the signals from the three detectors. The detection system has been extensively characterized by MCNP modeling, which has been benchmarked to experiments conducted at the Princeton Plasma Physics Laboratory. We demonstrate the spectral and directional sensitivity experimentally and in simulation, and provide a simple yet robust method for directional measurements.

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

Protein-Enabled Size-Selective Defect-Sealing of Atomically Thin 2D Membranes for Dialysis and Nanoscale Separations

Atomically thin 2D materials present the potential for advancing membrane separations via a combination of high selectivity (from molecular sieving) and high permeance (due to atomic thinness). However, the creation of a high density of precise nanopores (narrow-size-distribution) over large areas in 2D materials remains challenging, and nonselective leakage from nanopore heterogeneity adversely impacts performance. Here, we demonstrate protein-enabled size-selective defect sealing (PDS) for atomically thin graphene membranes over centimeter scale areas by leveraging the size and reactivity of permeating proteins to preferentially seal larger nanopores (≥4 nm) while preserving a significant amount of smaller nanopores (via steric hindrance). Our defect-sealed nanoporous atomically thin membranes (NATMs) show stability up to ~35 days during size-selective diffusive separations with a model dialysis biomolecule fluorescein isothiocyanate (FITC)-Ficoll 70 in phosphate buffer saline (PBS) solution as well as outperform state-of-the-art commercially available dialysis membranes (molecular-weight-cutoff ~3.5–5 kDa and ~8–10 kDa) with significantly higher permeance for smaller solutes KCl (~0.66 nm) ~5.1–6 × 10 –5 ms –1 and vitamin B12 (B12, ~1.5 nm) ~2.8–4 × 10 –6 ms –1 compared to small protein lysozyme (Lz, ~4 nm) ~4–6.4 × 10 –8 m s –1 , thereby allowing unprecedented selectivity for B12/Lz ~70 and KCl/Lz ~1280. Our work introduces proteins as nanoscale tools for size-selective defect sealing in atomically thin membranes to overcome persistent issues and advance separations for dialysis, protein desalting, small molecule separations/purification, and other bioprocesses.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Kinetics Measurements in Resistive Electrolytes Using Ring-Disk Electrode: Ring as Current “Shield” Enables Uniform Disk Current Distribution

Rotating disk electrodes are commonly used for electrochemical kinetics measurements. A major disadvantage of these types of electrodes is their nonuniform secondary current distribution, especially when performing electroanalytical measurements in resistive electrolytes. Such nonuniform current distribution can render the values of kinetics constants extracted from the disk electrode to be highly inaccurate. Furthermore, one emerging class of electrolytes that suffer from low ionic conductivities is deep eutectic solvents (DES). DES are a promising class of electrolytes for various emerging applications; however, due to their resistive nature, the secondary current distribution when using them is typically highly nonuniform. For example, the Wagner number when measuring Cu²⁺/Cu⁺ kinetics in choline chloride–ethylene glycol DES (1:4 molar ratio of ChCl:EG) is very low (<0.1), indicating highly nonuniform current distribution over the disk electrode. We show here that the Cu²⁺/Cu⁺ exchange current density measured using disk electrodes is very inaccurate due to the aforementioned nonuniform current distribution. To obtain uniform disk current distribution, we employ here a coplanar concentric rotating ring-disk electrode (RRDE), where the ring serves the function of a current “shield.” Specifically, we show using modeling that the ring minimizes the current distribution nonuniformity at the disk by effectively shielding the disk against current spikes near the disk edge. This enables improved precision in electrode kinetics measurements for the Cu²⁺/Cu⁺ couple in resistive DES. To enable broad applicability of this technique, an analytical expression based on the Wagner number is integrated into an iterative algorithm to help users identify ring conditions to achieve uniform current distribution and thus improved electroanalytics at the disk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Convective shells in the interior of Cepheid variable stars: Overshooting models based on hydrodynamic simulations

Context. Because Cepheid variable stars have long been used as a cosmic benchmark for scaling distances in our Galaxy and beyond, the accuracy of stellar evolution models for Cepheids have wide-reaching effects. However, our understanding of the dynamics in the interiors of these physically complex stars is limited. Aims. Our goal is to provide a detailed multi-dimensional picture of hydrodynamic convection and convective boundary mixing in the interior of Cepheids. Methods. Using the Modules for Experiments in Stellar Astrophysics (MESA), we studied the structure of intermediate-mass stars that cross the instability strip. Then, we performed two-dimensional hydrodynamic simulations of six stars with the fully compressible Multidimensional Stellar Implicit Code (MUSIC). Our simulations did not model the radial pulsations but focused on the interior structure of this family of stars. We developed and applied a new statistical analysis to examine convection and convective boundary mixing in the interior of these stellar simulations. Results. Based on a grid of MESA models, we demonstrated that a common structure for intermediate mass Cepheids includes an interior convective shell as well as a thin outer convective envelope. Using the extreme value theory approach to analyze our MUSIC simulation data, we found that overshooting above the convective shell fills the space between these convectively unstable layers. We developed a new statistical analysis that provides a clearer picture of how overshooting fills this layer; it also allowed us to formulate a detailed comparison between overshooting above and below the convective shell. Our analysis effectively decomposes the overshooting layer into two layers: a weak overshooting layer and a strong overshooting layer. Statistically, this is accomplished by decomposing the strongly non-Gaussian probability density function into a mixture of gamma distributions. Using our mixture model, we showed that the ratio of overshooting lengths above and below the convective shell depends directly on the radial extent of the convective shell as well as its depth in the star. We proposed a new form for the diffusion coefficient that addresses the need for overlapping overshooting layers between convective shells. We introduced the idea of a “super-mixing layer” where overshooting from both the convective shell and the convective envelope results in efficient mixing and could be viewed as merging the two adjacent convective zones.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Hugoniot properties of porous stainless steel: Insights from molecular dynamics simulations

Here, we present a systematic study of Hugoniot properties of porous 316L stainless steel using both a simple interpolation scheme and direct shock simulations in order to probe pore collapse kinetics as well as final thermodynamic states. Both methods indicate that equilibrated Hugoniot properties depend on pore density only and not on the pore distribution or size. We then create a simple porous equation of state model that is shown to be accurate for a range of validation data. This allows us to extend our simulations to make direct comparison to experimental data that have generally significantly larger system sizes and durations. In addition, our direct shock simulations indicate that the relaxation time after hotspot formation is system size dependent and can reach nanosecond timescales for the largest pores investigated in our study, thereby possibly having a measurable effect on fast dynamic loading experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An implementation of a high-order generalized finite difference method for solving the time-harmonic cold plasma wave equation in toroidal geometry

A high-order physics-informed meshless finite difference numerical technique is introduced for solving the time-harmonic cold plasma wave equation in toroidal geometries, presenting a novel application of the generalized finite difference (GFD) method to plasma wave simulations. The algorithm employs an irregular distribution of computational points, with local point density informed by the shortest wavelength derived from the cold plasma dispersion relation. Numerical stability and robustness are addressed using regularization techniques. The algorithm, implemented for two spatial dimensions, solves for the wave electric field and is demonstrated to achieve convergence rates of $\mathcal{O}$($\mathcal{h}$ $\mathcal{P}$ )⁠. Verification tests reproduce plane wave solutions, and example simulations of ion cyclotron resonance heating and electron cyclotron resonance heating demonstrate its capability, approaching realistic tokamak plasma scenarios. This work contributes to laying a foundation for the GFD method to be used in more sophisticated, optimized, and physically realistic full-wave simulations in time-harmonic plasma wave research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗