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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 OSTI2022

Evaluating the X2 initial core zero power physics tests with Serpent-Ants

The validation of the Ants nodal neutronics code for VVER applications is started by modelling the zero power physics tests for the initial core of the Khmelnitsky 2 nuclear power plant as described in the X2 benchmark using the Serpent-Ants two step neutronics calculation chain. The Ants prediction compare favorably against an earlier continuous energy Monte-Carlo reference solution as well as the measured data, except for discrepancies in the SCRAM worth between predicted and measured values. Similar discrepancies have been previously reported for VVER-1000 reactors, and the Ants predicted SCRAM worths match well with the Serpent results. Additionally, the assembly power distribution and axial power distribution predicted by Ants for the critical HZP state of the reactor are also compared to a reference Serpent solution with very good results. Lastly, an investigation into the effects of the few-group structure used in the nodal calculations and the use or lack of leakage correction for group constants on the results shows that the best accuracy is reached with seven or eight energy groups without leakage correction, although a reasonable accuracy can also be obtained with two or three energy groups using fundamental mode leakage correction. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Golden Eagle Behavioral Modeling Enabled by High-Fidelity Atmospheric Models

The DOE-funded Golden Eagle Behavioral Modeling project combines knowledge of eagle behavior, eagle telemetry data, high-fidelity atmospheric and wind power plant flow models, and machine learning to develop tools that predict behavior and risk around wind power plants. This presentation provides background on the project and modeling approaches, as well as recent modeling and validation results from the region around the Top of the World wind plant in Wyoming.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Fission gas diffusion and release for Cr 2 O 3 -doped UO 2 : From the atomic to the engineering scale

Here, the anticipated benefits of large grains in Cr 2 O 3 -doped UO 2 pellets include improved mechanical and fission gas retention properties. To support the assessment of fission gas release (FGR) from doped pellets, the impact of doping on fission gas diffusivity for in-reactor conditions must be understood. In this work, we tackle this issue by informing the fission gas model within the BISON fuel performance code using material models developed at the atomic scale. The investigation of intra-granular fission gas diffusivity in Cr 2 O 3 -doped UO 2 is carried out by adapting a cluster dynamics model that, accounting for UO 2 thermochemistry, is capable of describing Xe diffusion under irradiation in undoped UO 2 as the starting point. Using a thermodynamic analysis, it is shown that in stoichiometric UO 2 with additions of Cr 2 O 3 the oxygen potential is defined by the Cr-Cr 2 O 3 two-phase equilibrium. Using the cluster dynamics model, the predicted Xe diffusivity in doped UO 2 was significantly increased in both the intrinsic and irradiation-enhanced regimes compared to undoped UO 2 as a result of higher concentrations of uranium and oxygen vacancies, respectively. This is a consequence of the more oxidizing conditions at high temperature, and more reducing conditions at low temperature, as a result of doping. Arrhenius functions have been fitted to the cluster dynamics results to enable implementation of the new diffusivities in the BISON fission gas behavior model. BISON simulations were carried out, showing the competing effects of the enlarged grains and the new fission gas diffusivity model, which act to suppress and enhance fission gas release, respectively. The new physics-informed model was validated against in-reactor experimental measurements under normal operation. Additionally, benchmarking was carried out for power ramp conditions. The predicted fission gas release agreed well with the experimental data, showing noticeable improvements over the standard UO 2 model.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Drell-Yan q T resummation of fiducial power corrections at N 3 LL

We consider Drell-Yan production pp → V*X → LX at small q T << Q, where q T and Q are the total transverse momentum and invariant mass of the leptonic final state L. Experimental measurements require fiducial cuts on L, which in general introduce enhanced, linear power corrections in q T /Q. We show that they can be unambiguously predicted from factorization, and resummed to the same order as the leading-power contribution. For the fiducial q T spectrum, they constitute the complete linear power corrections. We thus obtain predictions for the fiducial q T spectrum to N 3 LL and next-to-leading-power in q T /Q. Matching to full NNLO ($α$$^{2}_{s}$), we find that the linear power corrections are indeed the dominant ones, and once included by factorization, the remaining fixed-order corrections become almost negligible below q T ≲ 40 GeV. We also discuss the implications for more complicated observables, and provide predictions for the fiducial Φ* spectrum at N 3 LL+NNLO. We find excellent agreement with ATLAS and CMS measurements of q T and Φ*. We also consider the $p$$^{ℓ}_{T}$ spectrum. We show that it develops leptonic power corrections in q T /(Q – 2$p$$^{ℓ}_{T}$), which diverge near the Jacobian peak $p$$^{ℓ}_{T}$ ~ Q/2 and must be kept to all powers to obtain a meaningful result there. Doing so, we obtain for the first time an analytically resummed result for the $p$$^{ℓ}_{T}$ spectrum around the Jacobian peak at N 3 LL+NNLO. Our method is based on performing a complete tensor decomposition for hadronic and leptonic tensors. We show that in practice this is equivalent to often-used recoil prescriptions, for which our results now provide rigorous, formal justification. Our tensor decomposition yields nine Lorentz-scalar hadronic structure functions, which for Z/γ* → ℓℓ or W → ℓν directly map onto the commonly used angular coefficients, but also holds for arbitrary leptonic final states. In particular, for suitably defined Born-projected leptons it still yields a LO-like angular decomposition even when including QED final-state radiation. Finally, we also discuss the application to q T subtractions. Including the unambiguously predicted fiducial power corrections significantly improves their performance, and in particular makes them applicable near kinematic edges where they otherwise break down due to large leptonic power corrections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Theory, analysis, and testing of an angular resonator for wave energy generation

This article describes the theory, analysis, and initial bench-top testing of a minimally invasive, rotational resonator designed to produce small amounts of electrical energy for use in oceanic observation buoys. Here, this work details the systems of equations that govern such a resonator, its potential power production, and its predicted effects on the modified motion of the buoy. Finally, a bench-top test apparatus is designed and experimented upon to identify the system and verify the system of equations empirically.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Comparative study of decay heat calculations with FLUKA and MCNP/CINDER2008

In designing a high-power tungsten target, decay heat driven temperature rise in the spallation volume is a safety concern during maintenance and in loss of coolant accidents. As tungsten hydrates and becomes volatile in steam at above 800 ◦C, it is important to keep the target temperature below this threshold when active cooling is unavailable. Decay heat in a target is calculated with particle transport simulation codes combined with transmutation codes. The calculated decay heat usually differs depending on the nuclear cross sections and the decay particle transport models built in the code architecture. In this paper, we calculated decay heat of a water-cooled tungsten target using popular particle transport codes, FLUKA and MCNP6® paired with CINDER2008 and compared the results. The target-moderator-reflector (TMR) system is modeled with a water-cooled solid tungsten target, water premoderators, liquid hydrogen cold moderators and beryllium reflectors. Water-cooled stainless-steel shielding is modeled around the TMR system. The tungsten volume is clad with a thin layer of erosion/corrosion resistant material. This study provides information about the uncertainty range in decay heat prediction of high-power spallation targets for hazard analysis.

Lee, Yong Joong↗
DOE OSTI2021

How Snow Aggregate Ellipsoid Shape and Orientation Variability Affects Fall Speed and Self-Aggregation Rates

Snow aggregate shapes and orientations have long been known to exhibit substantial variability. Despite this observed variability, most weather and climate prediction models use fixed power-law functions that deterministically map particle size to mass and fall speed. As such, integrated quantities like precipitation and self-aggregation rates currently ignore nonlinear effects resulting from variation in shape and orientation for aggregates of the same size. This study therefore develops an analytic framework that couples an empirically based bivariate distribution of ellipsoid shapes to classical hydrodynamic theory so as to capture an appropriate dispersion of masses, projected areas, and fall speeds for an assumed size distribution. For a fixed aggregate size, shape variations produce approximately ±0.13 m s -1 standard deviation of fall speed which increases the mass flux fall speed dispersion by more than 100% over traditional microphysics models. This increased fall speed dispersion results predominantly from shape-induced mass dispersion whereas orientation and drag dispersion play a lesser role. Shape variations can increase mass- and reflectivity-weighted fall speeds by up to 60% of traditional models whereas self-aggregation rates can increase by a factor of 100 for very small slope parameters. This implies that aggregate shape variations effectively forestall the theorized onset of fall speed distribution narrowing and subsequent quenching of the aggregation process. As a result, it is likely that secondary ice formation is necessary to prevent an ever decreasing slope parameter. The mathematical theory presented in this study is used to develop simple correction factors for snow forecast and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predictive Direct DC-Link Control for 7.2 kV Three-Port Low-Inertia Solid-State Transformer With Active Power Decoupling

Promising for applications including renewable energy and electric vehicle fast charging, a medium-voltage (MV) solid-state transformer (SST) typically has multiple modules series stacked, which requires the modules to be single phase. One critical issue of the single-phase SST is the double-line-frequency power ripple. Traditionally, large passives are used to buffer the ripple, resulting in significantly increased volume and cost. This article for the first time proposes active power decoupling (APD) for the SST and a predictive direct DC-link control method, using a current-source single-stage SST as an example. An electrolytic capacitor-less buffer port is used to absorb the ripple, which tolerates up to a 30% voltage ripple for small capacitance. The APD SST control is challenging. First, the current-source SST realizes isolation, DC link, and multiport in a single stage. Second, different from conventional SSTs or APD converters, the low-inertia DC link in this SST has a 40% switching ripple, which is difficult to stabilize. To address the control challenge, a direct DC-link control architecture and a predictive control method are proposed. Simulation and experimental results verify the proposed control method on an MV SiC modular soft-switching solid-state transformer (M-S4T) prototype. Here, the proposed APD reduces the SST volume by 53.8%. Importantly, the proposed concept is not limited to the M-S4T but is generic to other SSTs or APD converters.

14 SOLAR ENERGY↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Insights into mixing of non-isothermal multi-polymer melts for complex plastics recycling

Catalytic recycling or upcycling of plastics is often limited not by catalyst performance, but by transport, arising from highly viscous, non-Newtonian polymer melts. In this work, we develop a reactor-scale framework that integrates rheological measurements, constitutive modeling, computational fluid dynamics (CFD), and experiments to quantify mixing, heat transfer, and dispersion in surrogate hydrocarbon melts representing mixed plastics systems. Temperature- and shear rate-dependent viscosity of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) is measured to create two surrogate polymers (PLD and PHD) that capture the dominant shear-thinning flow behavior while neglecting strong elastic effects, enabling tractable simulation of non-isothermal, polymer-melt mixing using a Carreau-Arrhenius generalized Newtonian framework. Three-dimensional CFD simulations are employed to evaluate impeller performance in PLD using mixing time, cavern volume, thermal uniformity, and interfacial area for regimes in which viscoelastic effects are not dominant. We show that magnetic stir bars commonly used in lab-scale studies produce large thermal gradients (~60 °C) and poor mixing, even under idealized power delivery and polymer flow conditions. In contrast, close-clearance anchor impellers achieve near-isothermal operation, reduce mixing times by up to 5×, and provide >90% active circulation volume. We further demonstrate that, at low pseudo-Deborah number (De*), motor power requirements can be predicted directly from shear rate-dependent rheology using the Carreau-Arrhenius framework, enabling rational selection of operating conditions. Extension to surrogate immiscible multi-polymer systems based on PLD and PHD shows that interfacial area is highly sensitive to operating conditions and impeller design, with coaxial anchor-turbine configurations enhancing dispersion by up to 4 × .

Close-clearance impellers↗
DOE OSTI2023

Turbulent entrainment in finite-length wind farms

In this article, we present an entrainment-based model for predicting the flow and power output of finite-length wind farms. The model is an extension of the three-layer approach of Luzzatto-Fegiz & Caulfield ( Phys. Rev. Fluids , vol. 3, 2018, 093802) for wind farms of infinite length, and assumes dependence of key flow quantities, such as the wind farm bulk velocity, on the streamwise distance from the farm entrance. To assist our analysis and validate the proposed model, we undertake a series of large-eddy simulations with different turbine spacing arrangements and layouts. Comparisons are also made with the top-down model with entrance effects of Meneveau ( J. Turbul. , vol. 13, 2012, N7) and data from the literature. The finite-length entrainment model is shown to be capable of capturing the power drop between contiguous rows of turbines as well as describing the advection and turbulent transport of kinetic energy in both the entrance and fully developed regions. The fully developed regime is approximated only deep in the wind farm, after approximately 15 rows of turbines. Our data suggest that for the cases considered in this study, the empirical coefficients that can be used to describe turbulent entrainment and transfers above the wind farm exhibit little dependence on the farm layout and may be considered constant for modelling purposes. However, the flow field within the wind farm layer can be strongly modulated by the turbine density (spacing) as well as the array layout, and to that extent it can be argued that they are both primary factors determining the wind farm power output.

17 WIND ENERGY↗
DOE OSTI2025

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Here, the migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

14 SOLAR ENERGY↗
DOE OSTI2024

A Reactor Scale-Up Methodology from Lab-Scale to Pilot-Scale Operations: Numerical Modeling of THFA Dehydration to DHP in Packed-Bed Reactors

This manuscript discusses developing a model-based scale-up methodology for a successful technology transfer of gas-phase catalytic reactors from a lab-scale to a pilot-scale operation. The manuscript demonstrates the methodology for gas-phase dehydration of tetrahydrofurfuryl alcohol (THFA) to dihydropyran (DHP) process over commercial Al 2 O 3 catalysts. A two-dimensional reactor model was developed using COMSOL Multiphysics 6.1 software. The model solves heat and mass transport equations in bed-scale and particle scales simultaneously. This powerful feature enables accurate prediction of the heat and mass transfer limitations in pilot-scale reactors, if any exists. Further, the model uses isothermal lab-scale experimental data to derive and validate the reaction chemistry, flow fields and boundary conditions. The model was then scaled-up to project conversion, selectivity, yield and formation rate of DHP in a pilot-scale reactor. The results highlight the complex nature of chemistry, heat, and mass transfer effects in lab-scale and pilot-scale reactors. The model results inform the possible operational limitations of the pilot-scale reactor and design strategies to improve process efficiency. Although the scale-up approach is explained through the THFA dehydration process, the methodology is applicable to any catalytic packed-bed reactor models for a successful process scale-up.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Implementation and Validation of Constrained Density Functional Theory Forces in the CP2K Package

Constrained density functional theory (CDFT) is a powerful tool for the prediction of electron transfer parameters in condensed phase simulations at a reasonable computational cost. In this work we present an extension to CDFT in the popular mixed Gaussian/plane wave electronic structure package CP2K, implementing the additional force terms arising from a constraint based on Hirshfeld charge partitioning. This improves upon the existing Becke partitioning scheme, which is prone to give unphysical atomic charges. We verify this implementation for a variety of systems: electron transfer in (H 2 O) 2 + in vacuum, electron tunnelling between oxygen vacancy centers in solid MgO, and electron self-exchange in aqueous Ru 2+ -Ru 3+ . We find good agreement with previous plane-wave CDFT results for the same systems, but at a significantly lower computational cost, and we discuss the general reliability of condensed phase CDFT calculations.

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

The Convergence of Vehicle Electrification and Automation - Enabling Sustainable Automated Mobility Districts

The transportation industry is entering a new frontier with three major simultaneous transformations: electrification, automation, and on-demand services. All three directly affect the same spaces of vehicle technology, infrastructure, and system operational performance. Further, each area of change adds its own driving forces of complexity to the others' design challenges. This paper explores these transformations, framing the implications for vehicle technology and infrastructure and their impact on operating concepts and strategies, which in turn impact the sustainability of the transportation infrastructure in the built environment. Specifically, this paper examines battery-electric vehicles operating in a fully automated fleet within an "automated mobility district" (AMD) setting with multiple on-demand services. From this examination, the paper assesses the implications for transportation electrification, comparing and contrasting traditional fixed-route services with those of on-demand transit and automated transit networks, including the trade-offs between vehicle range (battery capacity), charging parameters, power availability, and operations predictability with respect to policy and infrastructure configurations. The paper also addresses the configuration and management of boarding and alighting zones (also called pickup and drop-off zones), and the need for complementary infrastructure intelligence at intersections and curbs. Finally, conclusions are drawn with respect to the system integration and analysis needs for AMDs regarding resilience and sustainability of mobility infrastructure.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Pulsed power to control growth of silicon nanoparticles in low temperature flowing plasmas

Low-temperature plasmas have seen increasing use for synthesizing high-quality, mono-disperse nanoparticles (NPs). Recent work has highlighted that an important process in NP growth in plasmas is particle trapping—small, negatively charged nanoparticles become trapped by the positive electrostatic potential in the plasma, even if only momentarily charged. In this article, results are discussed from a computational investigation into how pulsing the power applied to an inductively coupled plasma (ICP) reactor may be used for controlling the size of NPs synthesized in the plasma. The model system is an ICP at 1 Torr to grow silicon NPs from an Ar/SiH 4 gas mixture. This system was simulated using a two-dimensional plasma hydrodynamics model coupled to a three-dimensional kinetic NP growth and trajectory tracking model. The effects of pulse frequency and pulse duty cycle are discussed. We identified separate regimes of pulsing where particles become trapped for one pulsed cycle, a few cycles, and many cycles—each having noticeable effects on particle size distributions. For the same average power, pulsing can produce a stronger trapping potential for particles when compared to continuous wave power, potentially increasing particle mono-dispersity. Pulsing may also offer a larger degree of control over particle size for the same average power. Experimental confirmation of predicted trends is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗