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DOE OSTI2021

Plutonium Production Reactor Progression Problems: Magnox Neutronics Benchmarks

The goal of this document is to provide a set of comprehensive benchmarking neutronics problems for gas-cooled, graphite-moderated reactors. The problems are designed to be representative problems that increase in complexity to test performance of various codes. For the simulation of the benchmarking problems, the Shift Monte Carlo code and MPACT deterministic code are chosen because of their state-of-the-art capabilities, which are optimized for various reactor designs. Because these codes have been developed primarily for light water reactor applications, their application to graphite-moderated, gas-cooled reactors has not been extensively explored. This report attempts to evaluate benchmarks for a Magnox-style reactor, one that is graphite-moderated and gas-cooled. Parameters that are calculated and presented in this benchmark include the eigenvalue, peaking factors, and isotope concentrations. Results show good agreement between Shift and MPACT, and demonstrate the capability of MPACT to model graphite-moderated systems well.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Effective Permeability of a Nuclear Fuel Assembly

This report aids in the development of models to perform characterization studies of aerosol dispersal and deposition within a spent fuel cask system. Due to the complex geometry in a spent-fuel canister, direct simulation of buoyancy-driven flow through the fuel assemblies to model aerosol deposition within the fuel canister is computationally expensive. Identification of an effective permeability as given in this work for a nuclear fuel assembly greatly simplifies the requirements for thermal hydraulic computations. The results of computations performed using OpenFOAM® to solve the Navier-Stokes Equations for laminar flow are used to determine an effective permeability by applying Darcy's Law. The computations are validated against an analytical solution for the special case of an infinite array of pins for which the numerical and analytical solutions have excellent agreement. The effective permeability of a 1717 PWR nuclear fuel assembly in a basket without spacer grids is numerically determined to be 1.85010 -6 m 2 for the range of fluid viscosities and pressure drops expected in a spent fuel storage canister. However, the flow is not uniform on the scale of multiple pins. Instead, significantly higher velocities are attained in the space between the assembly and the basket walls compared to the flow between the fuel pins within the assembly. Comparison with an analytical solution for fully developed flow through an infinite array of pins shows that the larger spacing near the basket walls results in about a 20% larger permeability compared to the analytical solution which does not include the enhanced flow in the space between the assembly and basket wall, or entrance and exit effects. A preliminary assessment of turbulence effects shows that with a k-epsilon model, significantly higher flow velocities are attained between the fuel pins within the assembly compared to the flow velocity in the space between the assembly and the basket walls. This is the opposite of what is determined for laminar flow.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

ML-based Data Assimilation and History Matching: Application to the IBDP CCS Project

It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficiency. However, the computational cost of existing data assimilation methods can be prohibitively expensive due to the complex physics with multi-component non-isothermal simulation and high dimensionality of large-scale reservoir models. We address this challenge by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir.The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project (IBDP), a large-scale CO2 storage test in saline aquifer. The data assimilation process is implemented rapidly by the proposed workflow with given field measurements including distributed pressure and temperature sensing (DTS) data at an injection and a monitoring well. CO2 plume evolution is predicted by running the simulations of the calibrated reservoir models.

Nagao, Masahiro↗

DOE OSTI2024

6G Sparse Transmitarray

This powerpoint presents a sparse transmitarray design for 6G technology. The proposed approach leverages aperiodic arrays with large inter-element spacings, offering grating lobe-free operation, enhanced gain, and narrow beamwidths. The transmitarray topology, designed for sub-THz and THz frequencies, simplifies feed-network complexity, and minimizes losses. Numerical analysis and full wave simulation results show this approach is feasible.

Christian, Thomas Edward↗

DOE OSTI2026

Pulse control of superconducting cavity-based qudits with a Fock-state basis

Fock-basis-encoding qudits allow quantum gates to operate on multiple qudit levels simultaneously. This capability is advantageous for applications involving complex local gate operations, such as the digital quantum simulation of non-Abelian gauge theories. However, compiling unitary operations to native qudit gates remains a challenging task, often requiring substantial computational resources. In this work, we theoretically investigate the direct pulse control of a superconducting radio-frequency cavity coupled to an ancilla transmon for gate synthesis. We will discuss the improvements and limitations applicable to high Fock states. This work paves the way toward more efficient and robust qudit-gate synthesis.

Li, Andy C.Y. [Fermilab] (ORCID:0000000345423739)↗

DOE OSTI2026

Pulse control of superconducting cavity-based qudits with a Fock-state basis

Li, Andy C.Y. [Fermilab] (ORCID:0000000345423739)↗

DOE OSTI2026

Realization of a Quantum Slide Rule for 1+1 Dimensional Quantum Field Theories Using Josephson Superconducting Circuits

We have built a special type of electronic circuit—a “superconducting quantum simulator”—that allows us to mimic the behavior of complex quantum systems that are impossible to compute with ordinary computers. Our device is a long transmission line made of thousands of tiny superconducting junctions, acting as a one-dimensional “vacuum” for microwave photons. By terminating this line with a tunable, weak Josephson junction, we can control how strongly photons interact with each other.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Fundamental Investigations into the Properties and Performance of Advanced Materials

Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over ii the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.

Schichtel, Jacob J.↗

DOE OSTI2025

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

42 ENGINEERING↗

DOE OSTI2025

RE-INTEGRATE EMT Simulation Tool: Input Data Processing Layer for Bulk Power System

This paper introduces an advanced input data processing layer for EMT simulations of large-scale bulk power systems. The paper proposes two versions of the RE-INTEGRATE EMT simulation tool, RE-INTEGRATE Gen-0 and RE-INTEGRATE Gen-1, which are developed to enhance simulation generalizability, scalability, and accuracy. The framework leverages a generic class design for components to incorporate linear equations, which are generated by discretizing the Differential-Algebraic Equations (DAEs) that represent the dynamics of the components. In addition, the framework employs a parsing algorithm that parses a power system’s raw and dyr files to generate a connectivity graph which is then traversed to form the overall system’s dynamics. The proposed input data processing layer is used to simulate the IEEE 39-bus test system. The obtained results demonstrate the framework’s capability to achieve simulation scalability and accuracy. Further, the results indicate that EMT simulations performed using the proposed automations can effectively handle complex grid configurations.

Mishra, Rahul [ORNL] (ORCID:0000000328205932)↗

DOE OSTI2022

hifem_transport

SAND2022-14657 O The hifem-transport software is used to simulate transport processes in large geologic environments characterized by complex fractures and man-made infrastructure at an affordable computational cost. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Beskardes, Gungor↗

DOE OSTI2025

Seismic response of vertical dry storage casks under three-dimensional earthquake motions

Ensuring the long-term seismic safety of dry storage casks (DSCs) is becoming increasingly critical as these systems evolve from temporary to de facto permanent repositories for spent nuclear fuels. Traditional seismic soil–structure interaction (SSI) assessment methods use one-dimensional deconvolution or simplified boundary conditions to model incident waves. Although computationally appealing, simplifying assumptions may alter the seismic risk by neglecting the full complexity of three-dimensional (3D) wave propagation effects. To address this challenge, this paper introduces a novel high-fidelity computational framework that leverages the Domain Reduction Method (DRM) with perfectly matched layers (PML) to accurately transfer complex, 3D seismic wavefields from regional-scale fault-rupture simulations into local-scale finite element models of DSCs. Using broadband, physics-based ground motions from a generic M w 7.0 strike-slip event, both single-cask and multi-cask configurations were investigated under near- and far-field conditions. Emphasis is placed on capturing complex SSI, spatial variability in the ground motion, and nonlinear phenomena such as cask rocking and sliding. Numerical results demonstrate that near-field conditions, where forward directivity and fling-step effects dominate, lead to significantly higher DSC rocking and sliding. Far-field cases, by contrast, generally exhibit modest responses. Incorporating SSI tends to amplify or alter DSC response spectra and introduce response variability, which underscores the need for site-specific evaluations and robust modeling approaches to ensure the seismic integrity of DSCs in interim spent fuel storage installations.

Das, Tonmoy↗

DOE OSTI2021

Theoretical analysis of the saturation phase of the 1/1 energetic-ion-driven resistive interchange mode

The aim of the present study is to analyze the saturation regime of the energetic-ion-driven resistive interchange mode (EIC) in the LHD plasma. In this work, a set of nonlinear simulations are performed by the FAR3d code that uses a reduced MHD model for the thermal plasma coupled with a gyrofluid model for the energetic particle (EP) species. The hellically trapped EP component is introduced through a modification of the averaged drift velocity operator to include their precessional drift. The nonlinear simulation results show similar 1/1 EIC saturation phases with respect to the experimental observations, reproducing the enhancement of the n/m = 1/1 resistive interchange modes (RIC) amplitude and width as the EP β increases, the EP β threshold for the 1/1 EIC excitation, the further destabilization of the 1/1 EIC as the population of the helically trapped EP increases and the triggering of burst events. The frequency of the 1/1 EIC calculated during the burst event is 9.4 kHz and the 2/2 and 3/3 overtones are destabilized, consistent with the frequency range and the complex mode structure measured in the experiment. In addition, the simulation shows the inward propagation of the 1/1 EIC due to the nonlinear destabilization of the 3/4 and 2/3 energetic particle modes, leading to the partial overlapping between resonances during the burst event. Finally, the analysis of the 1/1 EIC stabilization phase shows the excitation of the 1/1 RIC as soon as the flattening induced by the 1/1 EIC in the pressure profile vanishes, leading to the retrieval of the pressure gradient at the plasma periphery and the overcoming of the RIC stability limit.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Annular Flow Simulation Supported by Iterative In-Memory Mesh Adaptation

Various flow regimes exist in a boiling water reactor (BWR) as the steam quality increases in the uprising coolant flow, from bubbly flow, slug/churn flow, to annular flow. The annular flow is characterized by the presence of a fast-moving gas core and the surrounding liquid film flowing on the conduit wall. Additionally, entrained droplets can be observed in the gas core with ingested bubbles in the liquid film. The dynamics occurring on the wavy interface between the liquid film and gas core plays a crucial role in affecting the heat transfer rate and pressure drop within the BWR core. However, a fundamental understanding of annular flow is still lacking, partly due to the difficulty in obtaining detailed local data in annular flow experiments. In the current study, a novel simulation framework is developed for the annular flow by coupling a computational fluid dynamics flow solver with state-of-the-art meshing software. The gas-liquid interface is tracked with the level set method. Based on the computed flow solutions, the computational mesh is dynamically adapted in memory to meet the local mesh resolution requirement. This iterative simulation-adaptation framework can ensure the fine mesh resolution across the interface, which not only helps mitigate the mass conservation degradation known to level set methods but also improves the representation of dramatic interface topological changes such as wave breaking and droplet entrainment. The present investigation will shed light onto the complex interfacial processes involved in annular flow and generate much needed simulation data for annular flow modeling.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗

DOE OSTI2023

AWAKEN Wind Plant Simulation Comparison

A series of numerical simulations of wind farms, using different model fidelities and for different atmospheric stability conditions, were performed as a part of the American WAKE ExperimeNt. The simulations included using FLORIS wake models, a number of microscale AMR-Wind and Nalu-Wind runs, as well as idealized and complex terrain WRF runs. The largest computations used the AMR-Wind LES solver to simulate a 100 km x 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements, while other LES computations focused on sections of the King Plains wind farm. Results of this qualitative comparison illustrate the interactions with wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.

17 WIND ENERGY↗

DOE OSTI2025

Closing the Accuracy Gap in Tandem Photovoltaic Testing: An Accessible and Efficient Spectral Tuning Method Using LED-Based Simulators for Research Laboratories and Industry

Accurate performance calibration of multijunction (MJ) solar cells is critical for advancing this technology toward large-scale terrestrial application, yet existing testing methods developed by photovoltaics calibration laboratories remain prohibitively complex and resource intensive for most research laboratories. Current approaches rely on expensive multisource simulators and/or intricate spectral tuning algorithms, limiting accessibility and hindering standardized comparisons of emerging MJ technologies such as perovskite-based tandems. This paper introduces a streamlined spectral irradiance adjustment method for light-emitting diode (LED)-based solar simulators, which are increasingly adopted in the photovoltaics community due to their versatility and cost-effectiveness. The method we present bins LED channels into groups based on the number of junctions of the MJ photovoltaic device under test (DUT) and their corresponding band gaps and incorporates an automated tuning algorithm that eliminates the need to adjust each channel’s irradiance individually. This tuning algorithm requires the relative spectral responsivities of both the DUT and a broadband reference cell, as well as a calibrated spectroradiometer. Measurement validation across various MJ devices, including III-V and perovskite/silicon tandems, demonstrates excellent agreement within 1% with well-established xenon-tungsten multisource simulators and associated tuning algorithms. By enabling precise spectral tuning with readily available equipment and a simple tuning algorithm, our approach bridges the measurement accuracy gap between research laboratories and accredited testing facilities, fostering more reliable device comparisons and accelerating the translation of MJ technologies into real-world energy systems.

14 SOLAR ENERGY↗

DOE OSTI2026

Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and a supplier-rating model adapted from existing literature. The model’s core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas—Atlanta, Chicago, Dallas–Fort Worth, and Los Angeles—the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.

Ismael, Abdelrahman (ORCID:0000000303712110)↗