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DOE OSTI2024

Artificial correlation heating in PIC simulations

The Particle-in-Cell (PIC) method, a cornerstone in plasma modeling, is widely employed for its ability to simulate kinetic phenomena in device-scale domains. Part of what makes this possible is that computational macroparticles represent many physical particles. It converges under certain constraints, including a grid spacing that resolves the Debye length and a time step small enough to respect the Courant–Friedrichs–Lewy condition and plasma frequency stability limit. Here, we introduce a new constraint necessary to avoid Artificial Correlation Heating (ACH). This requires that the macroparticle coupling strength be smaller than one, Γ ω < 1, where Γ ω ≡ Γω 2/3 ⁠, Γ = Z 2 e 2 /(4πε 0 ak B T) is the physical coupling strength, and w is the macroparticle weight. This is particularly relevant to 3D simulations of dense plasmas, which are becoming common with modern computing power. If this condition is violated, the finite macroparticle weight artificially enhances the coupling strength and causes the plasma to heat until the macroparticle coupling strength is near unity, depending on the grid resolution. A comprehensive model of ACH is developed that incorporates electron density, temperature, macroparticle weight, and grid resolution. It is then tested using PIC simulations, delineating the boundaries of the method's applicability and offering a predictive framework for ACH. Moreover, the research explores a runaway heating process induced by ACH in the presence of ionization, which can lead to numerical instability. A conclusion of this study is that the onset of ACH can impose a more stringent constraint on the macroparticle weight and average number of macroparticles per cell than what is typically expected, particularly in 3D simulations of dense plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Microbial inoculants for soil restoration: A Risk-Proportional Stewardship Framework Integrating Strain-Resolved Genomics and Adaptive Governance

Global soil degradation and increasing reliance on chemical inputs threaten agricultural sustainability, driving interest in microbial inoculants as tools for soil restoration. These biological products have the potential to enhance nutrient cycling, improve soil structure, and support plant resilience, but their environmental release raises important safety and stewardship considerations. Here, we propose a risk-proportional framework for the responsible deployment of microbial inoculants grounded in release-based stewardship. The framework integrates genome-resolved strain identification, exclusionary hazard screening, bioassay-based risk triage, ecological testing under realistic conditions, and monitored field deployment. Drawing on evidence from microbial ecology and invasion biology, we highlight how inoculants can alter resident microbial communities, influence ecosystem function, and, in some cases, facilitate gene flow, underscoring the need for risk assessment. We further outline a federated, genome-informed data infrastructure to support traceability, cross-jurisdiction learning, and adaptive management. Together, this approach provides a scalable and scientifically grounded pathway to balance innovation and safety, enabling microbial technologies to contribute to soil restoration and climate-resilient agriculture.

Edlund, Anna [OATH Inc]↗

DOE OSTI2024

Ristra Project FY23 L2 Milestone Report, Rev.1: MRT #8541: Multiphysics Scaling on EAS-3

The findings of this report were used to close out the ATDM milestone MRT# 8541, which was designed to demonstrate readiness of ATDM multiphysics codes for mission-relevant work on ATS-4, El Capitan. To this end, the closure criteria were to run a 3D shaped charge problem at scale up to 50% of the El Capitan early-access system, RZVernal (AMD Trento CPUs and AMD MI-250X GPUs), demonstrate scalability, and document challenges with the software stack and environment. LANL’s approach to this milestone was to test our modular software capability by developing an entirely new code, Moya, built upon our FleCSI framework. The physics capability and the GPU infrastructure needed for the shaped charge problem on GPUs was added to Moya, and the required calculations were performed at scale. Moya showed good scaling without any fine-tuning of GPU kernels; there is still significant room for performance enhancements, especially for the Legion backend. Tied up in this L2 milestone was a closeout of KPP-3s for the ECP ST Projects at LANL; this material will be covered in a separate document.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Developing and Managing Data Acquisition Software Using Spack

The Data Acquisition systems of particle physics experiments regularly push the boundaries of high-throughput computing, demanding low-latency collection of data from thousands of devices, collating data into time-sliced events, processing these events and making trigger decisions, and writing the selected data streams to disk. To accomplish these tasks, the DAQ Engineering and Operations department at Fermilab leverages multiple software libraries and builds reusable DAQ frameworks on top. These libraries must be delivered in well-defined bundles and are thoroughly tested for compatibility and functionality before being deployed to live detectors. We have several techniques used to ensure that a consistent set of dependencies can be delivered and re-created at need. We must also support active development of DAQ software components, ideally in an environment as close as possible to that of the detectors. This development often occurs across multiple packages which have to be built in concert and features tested in a consistent and reproducible manner. I will present our scheme for accomplishing these goals using Spack environments, bundle packages, and Github Actions-based CI.

Flumerfelt, Eric [Fermilab]↗

DOE OSTI2025

Status of Multiple Channel Fuel Performance Capabilities Within the SAS4A/SASSYS-1 Safety Analysis Software

SAS4A/SASSYS-1 (SAS) is a fast-running simulation tool used to perform deterministic analysis of anticipated events as well as design basis and beyond design basis accidents for advanced liquid-metal-cooled nuclear reactors. It is a critical element of safety analysis capabilities for the U.S. Department of Energy and is utilized within industry to perform the transient safety analyses required to support the licensing of Liquid Metal-cooled Fast Reactors (LMFRs). Although SAS is exceptionally fast for most transient scenarios, fuel performance calculations, along with the associated pre-transient characterization of the fuel pin, may be required for transient scenarios where fuel pin failure is hypothesized. Both the pre-transient characterization and the transient fuel performance calculation are necessary to properly quantify margins to potential fuel failure and assess the time spent potentially exceeding such margins during events. While safety analysis calculations with fuel performance models provide a more detailed characterization of the reactor during a transient, the pre-transient characterization can be time-consuming and computationally expensive. Often, large numbers of fuel pins have been exposed to similar pre-transient irradiation conditions. Similarly, the same pre-transient fuel characterization may be applicable to numerous transient conditions. This provides an opportunity to optimize the SAS computational framework such that pre-transient fuel characterization can be shared across multiple channels (fuel pins) and across multiple simulations, thus dramatically reducing overall computational costs. This report summarizes progress toward enhancing the SAS computational framework to support shared, multiple channel fuel performance characterizations intended to significantly reduce computational costs. Preliminary testing has shown that the computational time saved by using the pre-transient sharing capability is approximately equal to the time it takes to perform the pre-transient characterization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Complete development of LAROMance-type surrogate models for Zircaloy, as well as initial integration and testing in MOOSE, which addresses industry needs for licensing advanced LWR fuels

This milestone report details the progress in the constitutive surrogate modeling framework for Zircaloy’s mechanical behavior under extreme conditions, within the NEAMS program throughout the U.S. fiscal year 2024 (FY24). The presented work expands on the spectral based mechanical solver and the mechanistic material model for Zircaloy, which were introduced in the NEAMS program during FY22 and detailed in the previous fiscal year (FY23). As part of the NEAMS program, the current effort described in this report focused on the development of a novel viscplastic (creep) surrogate for the mechanistic, polycrystal-based model. To this end, the work was subdivided into two interdependent components related to the necessary improvements to the LApx constitutive model and algorithm and, to the development and use of a surrogate model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Antarctic ice sheet model comparison with uncurated geological constraints shows that higher spatial resolution improves deglacial reconstructions

Accurately reconstructing past changes to the shape and volume of the Antarctic ice sheet relies on the use of physically based and thus internally consistent ice sheet modeling, benchmarked against spatially limited geologic data. The challenge in model benchmarking against geologic data is diagnosing whether model-data misfits are the result of an inadequate model, inherently noisy or biased geologic data, and/or incorrect association between modeled quantities and geologic observations. In this work we address this challenge by (i) the development and use of a new model-data evaluation framework applied to an uncurated data set of geologic constraints, and (ii) nested high-spatial-resolution modeling designed to test the hypothesis that model resolution is an important limitation in matching geologic data. While previous approaches to model benchmarking employed highly curated datasets, our approach applies an automated screening and quality control algorithm to an uncurated public dataset of geochronological observations (specifically, cosmogenic-nuclide exposure-age measurements from glacial deposits in ice-free areas). This optimizes data utilization by including more geological constraints, reduces potential interpretive bias, and allows unsupervised assimilation of new data as they are collected. We also incorporate a nested model framework in which high-resolution domains are downscaled from a continent-wide ice sheet model. We highlight the application of this framework by applying these methods to a small ensemble of deglacial ice-sheet model simulations, and demonstrate that the nested approach improves the ability of model simulations to match exposure age data collected from areas of complex topography and ice flow. We develop a range of diagnostic model-data comparison metrics to provide more insight into model performance than possible from a single-valued misfit statistic, showing that different metrics capture different aspects of ice sheet deflation.

Geosciences↗

DOE OSTI2026

Investigation of low-energy particle remnants in high-energy collisions at the LHC with a skipper-CCD detector

We deployed the Mobile Skipper Testing Apparatus ∼33 m away from the Compact Muon Solenoid collision point, the first skipper-CCD detector probing low-energy particles produced in high-energy collisions at the Large Hadron Collider. In this work, we search for beam-related events using data collected in 2024 during beam-on and beam-off periods. The dataset corresponds to integrated luminosities of 113.3 fb −1 and 1.54 nb −1 for the proton-proton and Pb-Pb collision periods, respectively. We report observed event rates in a model-independent framework across two ionization regions: ≤ 20⁢𝑒 − and > 20⁢𝑒 − . For the low-energy region, we perform a likelihood analysis to test the null hypothesis of no beam-correlated signal. We found no significant correlation during proton-proton and Pb-Pb collisions. For the high-energy region, we present the energy spectra for both collision periods and compare event rates for images with and without luminosity. We observe a slight increase in the event rate following the Pb-Pb collisions, coinciding with a rise in the single-electron rate, which will be investigated in future work. Using the low-energy proton-proton results, we place 95% confidence level constraints on the mass-millicharge parameter space of millicharged particles. Overall, the results in this work demonstrate the viability of skipper-CCD technology to explore new physics at high-energy colliders and motivate future searches with more massive detectors.

Cervantes-Vergara, Brenda A. [Fermi National Accel↗

DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗

DOE OSTI2025

Initial steady-state core simulation capability or thermal and pool-type molten salt reactors, coupling reactor physics, thermal-hydraulics, and evolving chemistry

This report presents the development and validation of an initial steady-state multiphysics capability for molten salt reactors (MSRs) under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in Fiscal Year 2025. The framework integrates neutronics, thermal-hydraulics, species transport, and thermochemistry to capture the coupled dynamics of liquid-fueled systems. Implementation and testing were performed on two representative designs: the Molten Salt Reactor Experiment (MSRE), a thermal-spectrum, channeled-flow reactor, and the Lotus Molten Salt Reactor (L-MSR), a fast-spectrum, pool-type reactor. The modeling suite employs Griffin for reactor physics and depletion, Pronghorn and SAM for thermal-hydraulics, Thermochimica for chemistry, and Saline for thermophysical properties, with benchmarking and validation carried out against historical MSRE data, experimental flow-loop measurements, and reference depletion calculations from Monte Carlo codes. The framework demonstrated the ability to reproduce key reactor behaviors including temperature feedback, reactivity losses, delayed neutron precursor transport, xenon poisoning, and redox potential evolution. The results confirm the feasibility and accuracy of the coupled models in predicting steady-state and selected transient MSR behaviors. This latter ones are used in this report as a proxy indicating that the steady-state models from which the transient starts are accurate. For MSRE, validation showed good agreement with pump start-up and natural circulation tests, while for the L-MSR, benchmarking confirmed hydraulic calibration and consistency of neutronics–thermal coupling. The tools also provided new insights into species transport, noble metal deposition, and salt solidification dynamics. On the Xenon transport front, the code is validated against the steady state Xenon poisoining measurement and showed good agreement with the experimental value. Identified areas for future work include advanced void transport modeling, three-dimensional simulations, improved alloy corrosion models, and tighter integration with high-fidelity Monte Carlo codes. These developments provide a foundation for high-fidelity MSR simulations that can support reactor design optimization, safety assessments, and long-term operational strategies.

42 - ENGINEERING↗

DOE OSTI2024

Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks

There has been much recent interest in designing symmetry-aware neural networks (NNs) exhibiting relaxed equivariance. Such NNs aim to interpolate between being exactly equivariant and being fully flexible, affording consistent performance benefits. In a separate line of work, certain structured parameter matrices -- those with displacement structure, characterized by low displacement rank (LDR) -- have been used to design small-footprint NNs. Displacement structure enables fast function and gradient evaluation, but permits accurate approximations via compression primarily to classical convolutional neural networks (CNNs). In this work, we propose a general framework -- based on a novel construction of symmetry-based structured matrices -- to build approximately equivariant NNs with significantly reduced parameter counts. Our framework integrates the two aforementioned lines of work via the use of so-called Group Matrices (GMs), a forgotten precursor to the modern notion of regular representations of finite groups. GMs allow the design of structured matrices -- resembling LDR matrices -- which generalize the linear operations of a classical CNN from cyclic groups to general finite groups and their homogeneous spaces. We show that GMs can be employed to extend all the elementary operations of CNNs to general discrete groups. Further, the theory of structured matrices based on GMs provides a generalization of LDR theory focussed on matrices with cyclic structure, providing a tool for implementing approximate equivariance for discrete groups. We test GM-based architectures on a variety of tasks in the presence of relaxed symmetry. We report that our framework consistently performs competitively compared to approximately equivariant NNs, and other structured matrix-based compression frameworks, sometimes with a one or two orders of magnitude lower parameter count.

Samudre, Ashwin↗

DOE OSTI2025

In‐situ Analysis of Paste Properties in Resonant Acoustic Mixers for Quality Monitoring

Formulation control is key to achieving consistent target properties of energetic materials, as feedstock variations and slight deviations in the ratios of different ingredients can have major effects on final product properties, particularly in dense pastes with high particle loading >65 vol.%. In large‐scale operations, it is imperative to either correct or remove batches of material that perform outside baseline property specifications as early as possible to avoid unnecessary processing of suboptimal material. Quality monitoring is the practice of measuring material properties during processing using process analytical technologies as opposed to only testing the properties of the final product; it is a key principle in the quality‐by‐design frameworks used for designing formulations and manufacturing processes. Herein, a process analytical technology method for correlating material properties of dense pastes directly after mixing in a Resonant Acoustic Mixer to motor data is developed and used to detect differences in the particle content of dense paste formulations. This method was also capable of detecting variations in powder feedstock properties, such as particle packing efficiency, and is sensitive enough to detect changes of 2 wt.% in the total solids content of the formulation. The techniques presented herein show excellent promise for use as a process analytical technology capable of quantifying formulation effects on material movement modes during resonant acoustic mixing.

Materials science↗

DOE OSTI2024

Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning

The movement of solutes in a watershed is a complex process with multiple interactions and feedbacks across spatial and temporal scales. Modeling the dynamics of solute transport along diverse hydrologic pathways within watersheds – from hillslopes to stream channels and in and out of the hyporheic zones – is challenging but critically important, as these processes integrate and contribute to the biogeochemical functioning of the river corridor up to the river network scale. Here we use results from a long-term network-scale tracer test at the H.J. Andrews experimental forest in western Cascade Mountains, Oregon, USA to inform a multiscale framework for transport in stream corridors. The framework uses a Lagrangian-based subgrid model to represent the effects of hyporheic exchange flow and advective transport at stream network scales. The spatially and temporally resolved stream discharge needed for the transport model is imputed across the river system by an entity-aware long short-term memory network. Modeled concentrations show good agreements with the observations and exhibit power scaling laws indicative of a very wide range of timescales over which hyporheic exchange flow occurs. Our results demonstrate a data-informed modeling framework that links dynamical processes occurring at small scales to a network context to help understand how changes at reach scale cascade into network-scale effects, providing a useful tool for sustainable river basin management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

RD53 pixel readout integrated circuits for ATLAS and CMS HL-LHC upgrades

The RD53 collaboration has since 2013 developed new hybrid pixel detector chips with 50 × 50 μm2 pixels for the HL-LHC upgrades of the ATLAS and CMS experiments at CERN. A common architecture, design and verification framework has been developed to enable final pixel chips of different sizes to be designed, verified and tested to handle extreme hit rates of 3 GHz/cm2 (up to 12 GHz per chip) together with an increased trigger rate of 1 MHz and efficient readout of up to 5.12 Gbits/s per pixel chip. Tolerance to an extremely hostile radiation environment with 1 Grad over 10 years and induced SEU (Single Event Upset) rates of up to 100 upsets per second per chip have been major challenges to make reliable pixel chips. Three generations of pixel chips, and many specific mixed signal building blocks and radiation test chips, have been submitted and extensively tested to get to final production chips. The large, complex and high rate pixel chips have been developed with a strong emphasis on low power consumption together with a concurrent development and qualification of novel serial powering at chip, module and system level, to minimize detector material budget.

Alimonti, G↗

DOE OSTI2025

Hybrid SO(10) Axion Model without Quality Problem

Invisible axion models that solve the strong C P problem via the Peccei-Quinn (PQ) mechanism typically have a quality problem that arises from quantum gravity effects which violate all global symmetries. These models therefore require extreme fine-tuning of parameters for consistency. We present a new solution to the quality problem in a unified SO ( 10 ) × U ( 1 ) a gauge model, where U ( 1 ) a is an anomaly free axial gauge symmetry. PQ symmetry emerges as an accidental symmetry in this setup, which admits a PQ breaking scale as large as 7 × 10 11 GeV , allowing for the axion to be the cosmological dark matter. We call this a hybrid axion model due to its unique feature that it interpolates between the popular KSVZ and DFSZ axion models. Its predictions for the experimentally measurable axion couplings to the nucleon and electron are distinct from those of the usual models, a feature that can be used to test it. Furthermore, the model has no domain wall problem and it provides a realistic and predictive framework for fermion masses and mixings. Published by the American Physical Society 2025

Babu, K. S. (ORCID:0000000161475155)↗

DOE OSTI2025

A Game-Theoretic Quantum Algorithm for Solving Magic Squares

Variational quantum algorithms (VQAs) offer a promising near-term approach to finding optimal quantum strategies for playing non-local games. These games test quantum correlations beyond classical limits and enable entanglement verification. In this work, we present a variational framework for the Magic Square Game (MSG), a two-player non-local game with perfect quantum advantage. We construct a value Hamiltonian that encodes the game’s parity and consistency constraints, then optimize parameterize quantum circuits to minimize this cost. Our approach build on the stabilizer formalism, leverages commutation structure for circuit design, and is hardware-efficient. Compared to existing work, our contribution emphasizes algebraic structure an interpretability. We validate our method through numerical experiments and outline generalizations to larger games.

Chehade, Sarah [ORNL]↗

DOE OSTI2025

Application of Flex-QA Arrays in HTS Magnet Testing

Flexible PCB quench antennas have been very useful in providing high-quality high-resolution data in low temperature superconducting magnet tests. Similar multi-sensor arrays have been employed recently to cover a high temperature superconductor magnet tested at FNAL. In the present work, data taking conditions and magnet features to support the analysis framework are discussed. Then observations made during complete magnet powering cycles are described and analysis of quench antenna data are presented. Based on results, improvements to instrumentation and data taking are debated. Views on the future of flexible quench antenna sensors for HTS magnet diagnostics and operational support are shared.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Application of Flex-QA Arrays in HTS Magnet Testing

Stoynev, S.↗