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DOE OSTI2024

Status of the CERBERUS Evaluation for the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook

Modeling & Simulation (M&S) tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers continue to improve, we are able to enhance resolution in our calculations. Therefore, the limitations of simulation capability are in the quality of data that is being used, including our ability to quantify the uncertainty and sensitivity of that data. In order to model systems of interest with increasing accuracy, the industry must improve key nuclear data measurements. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles and evaluates experiment data in a handbook that can be used by criticality safety engineers and others to validate computer codes and cross section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. These experiments, along with differential measurements, can help improve the quality of nuclear data. Concerns regarding the accuracy of Cu nuclear data have been published. The large values and trend of C-E for the Zeus intermediate energy benchmark, being one of the primary examples. Furthermore, very few experiments have been designed to be sensitive to Cu (as shown in Figure 1), so an integral, critical experiment is needed to help resolve these differences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

The Development of Catalysts for Upgrading of Pyrolysis Vapor for Refinery Feedstocks and Intermediates (CRADA Final Report)

Catalytic fast pyrolysis (CFP) is a versatile technology platform to convert biomass into fungible hydrocarbon transportation fuels and chemical co-products. Key technical barriers to reaching this goal include increasing the product yields and achieving the desired fuel properties for gasoline, diesel, and jet range fuels or blendstocks that would be suitable for introduction into existing refinery unit operations. Overcoming these barriers will require durable catalysts that are effective at upgrading and stabilizing biomass pyrolysis vapors. Towards these goals, this CRADA leveraged NREL experience as a leader in biomass pyrolysis research and Johnson Matthey's (JM) experience as a leader in the production of advanced catalytic materials. The scope spanned CFP catalyst development, characterization, multi-scale reaction testing, and computational modeling. CRADA benefits to DOE, Participant, and U.S. Taxpayer: Assists laboratory in achieving programmatic scope, Uses the laboratory’s core competencies. The purpose of this CRADA was to develop and deploy catalysts for biomass CFP to help achieve cost-competitive biofuels and bio-based products. This was accomplished through a close collaboration between biomass conversion researchers at NREL and catalyst development researchers at JM. Summary of Research Results: Focus Area 1. Foundational research on catalytic conversion and deactivation: Key interactions between pyrolysis vapors and heterogeneous catalysts were probed through catalyst characterization, model compound reaction testing, and atomistic-scale computational modeling. Catalyst development focused on multifunctional materials, which include zeolites, oxides, carbides, and nitrides. Computational modeling identified reaction mechanisms and elucidated surface chemistry to test hypotheses regarding mechanisms of deoxygenation, coupling, cracking, dehydration, coke formation, hydrogen transfer, and aromatic ring reactions. This information was used to design multifunctional catalysts to increase product yields, control product selectivity, and reduce deactivation during CFP and downstream processing steps. The results served to increase fundamental understanding of key catalyst attributes and durability features for the upgrading of biomass pyrolysis vapors. Model compound experiments confirmed the importance of metal-acid bifunctionality for the deoxygenation of lignin-derived phenolic species under hydrodeoxygenation conditions. This insight led to the development of catalysts such as Pt/TiO2 and Mo2C, which were confirmed as high-performing materials during subsequent bench-scale experiments using biomass-derived pyrolysis vapors. This focus area also led to the identification of important catalyst deactivation mechanisms associated with the deposition of inorganic contaminants such as potassium. The molecular-level insight from model compound experiments and computational modeling, shown in Figure 1, informed the development of regeneration procedures that have been shown to be effective for restoration of > 90% of initial catalyst activity. This understanding has subsequently been translated to other catalyst systems, including zeolite materials that can be operated without requirements for co-fed hydrogen.

09 BIOMASS FUELS↗

DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗

DOE OSTI2025

A Portfolio Approach to Massively Parallel Bayesian Optimization

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Exploiting universal nonlocal dispersion in optically active materials for spectro-polarimetric computational imaging

Recent years have seen significant advancements in exploring novel light-matter interactions such as hyperbolic dispersion within natural crystals. However, current studies have predominantly concentrated on local optical response of materials characterized by a dielectric tensor without spatial dispersion. Here, we investigate the nonlocal response in optically-active crystals with screw symmetries, revealing their lossless, super-dispersive properties compared to traditional optical response functions. We leverage this universal nonlocal dispersion, i.e. the dispersion of optical rotatory power, to explore a novel spectral de-multiplexing scheme compared to conventional gratings, prisms and metasurfaces. We design and demonstrate an ‘Nonlocal-Cam’ - a camera that exploits nonlocal dispersion through sampling of polarized spectral states and the application of computational spectral reconstruction algorithms. The Nonlocal-Cam captures information in both laboratory and outdoor field experiments which is unavailable to traditional intensity cameras - the spectral texture of polarization. Merging the fields of nonlocal electrodynamics and computational imaging, our work paves the way for exploiting nonlocal optics of optically active materials in a variety of applications, from biological microscopy to physics-driven machine vision and remote sensing.

Wang, Xueji [Purdue Univ., West Lafayette, IN (Uni↗

DOE OSTI2024

Measuring Impact: Evaluating Thermal Zoning Simplification on Energy Efficiency Measures Analysis

Building Energy Modeling (BEM) is a versatile tool for designing, retrofitting, ensuring code compliance, obtaining certifications, qualifying for incentives, and enabling real-time building control. However, capturing all the details of building geometry for thermal zoning can be time-consuming, costly, and sometimes computationally challenging. As a result, modelers have been applying zoning simplification based on factors such as space functions and internal loads, as well as relying on their experience and judgment while adhering to zoning rules outlined in industry standards. Despite the prevalence of this common practice, a notable gap exists in the literature regarding studies quantifying the influence of simplified thermal zoning on the evaluation of Energy Efficiency Measures (EEMs). Recognizing this gap, this paper seeks to contribute to the field by enhancing the understanding of how the simplification of thermal zoning influences the evaluation of EEMs against a baseline design. The study utilized a medium office prototype model with a detailed floor plan featuring over 20 zones per floor covering diverse functional spaces with varying internal loads and occupancy schedules. A standard thermal zoning strategy outlined in ASHRAE Standard 90.1 Appendix G was employed as the simplified zoning method. This strategy condenses the zoning into a core zone and four perimeter zones per floor. It was compared with the detailed zoning approach, which involves one zone per space. Common Energy EEMs, such as enhanced envelope, high-efficiency appliances and equipment, and HVAC controls, were individually implemented and evaluated. The results indicate that the performance comparison between the two zoning methods varies depending on the type of measures considered. Basic measures, such as adding wall insulation, demonstrate similar energy impacts, while advanced HVAC control measures, such as static pressure reset, exhibit a more substantial difference that cannot be overlooked.

Xie, Jiarong↗

DOE OSTI2025

Low- n stability and plasma response to RMP in various STEP scenarios

The low-n (n is the toroidal mode number) magnetohydrodynamic (MHD) stability and plasma response are numerically investigated for various scenarios designed for STEP, that are relevant for the H-mode pedestal analysis. Control of the edge-localized modes (ELMs) with externally applied resonant magnetic perturbations (RMPs) is considered. Optimization of the ELM control coil current configuration, based on the computed plasma MHD response and well-established figures of merit validated on present-day experiments, finds reasonable robustness of a fixed coil phasing (for a given n-number) to control ELMs in all five STEP plasmas considered. Based on certain semi-empirical criteria, the required coil current to achieve ELM suppression is estimated to be about 10–20 kAt with the n = 1 or 2 RMP configuration and about 100–200 kAt for the n = 4 RMP. Systematic linear stability calculations are used to map out stability windows for the low-n kink-peeling modes, in terms of the ideal-wall location and variation of the edge safety factor q 95 with respect to the target design. The kink-peeling stability boundary is found to be generally sensitive to the q 95 variation, which has implications for achieving the quiescent H-mode regime in STEP. Full toroidal quasilinear initial-value simulations for these STEP plasmas find that generation of the edge-harmonic oscillations (EHOs) depends sensitively on the plasma scenario, the initial linear stability of the kink-peeling modes, the initial plasma toroidal flow and q 95 . In general, it is easier (more robust) to access the EHO-regime for two of the cases considered with smaller plasma volume and higher on-axis safety factor. Finally, quasilinear simulations find robust density pumpout due to applied RMPs in these STEP plasmas, but the effect on the plasma toroidal flow varies among different cases.

EHO↗

DOE OSTI2024

Simulations, Modeling and Data Analysis of Parity Violating Electron Scattering Experiments

In the Standard Model (SM) of nuclear and particle physics, parity violation is incorporated through the representation of the weak interaction as a chiral gauge interaction. Only the left-handed components of particles and right-handed components of antiparticles participate in weak interactions in the Standard Model. This implies that parity is asymmetric for the weak interaction. Parity violating electron scattering (PVES) experiments are designed to probe the physics parameters related to the SM, with the possibility to discover physics beyond the SM (BSM) by measuring the parity violating asymmetry ¿¿¿ of longitudinally polarized electrons scattered off unpolarized targets with high precision. This dissertation will be focused on two PVES experiments, the next 208Pb Lead Radius Experiment (PREX-II), and the Measurement of a Lepton-Lepton Electroweak Reaction (MOLLER) experiment, as well as in some small sections, the Calcium Radius Experiment (CREX) and P2 experiment which are also PVES experiments). PREX-II and CREX experiments, performed in Hall A at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), measured ¿¿¿ in the elastic scattering of longitudinally polarized electrons from 208Pb and 48Ca targets to provide a precise model independent determination of the neutron skin thickness of 208Pb and 48Ca nuclei, respectively. The MOLLER experiment, proposed to start in 2027 and also to be performed in Hall A at Jefferson Lab, is to measure ¿¿¿ of longitudinally polarized electrons scattered off unpolarized electrons (Møller scattering) to determine the weak charge of electrons ¿¿¿ and the weak mixing angle ¿¿ with high precision. As for the P2 experiment, which will be performed at the upcoming MESA accelerator in Mainz Germany, it is to measure the weak charge of proton ¿¿¿ using ¿¿¿ in the elastic electron-proton scattering of polarized electrons off unpolarized protons. The final results from the PREX-II experiment are presented as ¿¿¿=550±16 (¿¿¿¿)±8 (¿¿¿¿) parts-per-billion (ppb). Combining the PREX-I and PREX-II results, the neutron skin thickness from PREX experiments is determined as ¿¿-¿¿=0.283±0.071 ¿¿ in 208Pb. This thesis lists the software and computational contribution of the author to these PVES experiments, including writing scripts and software to help with the PREX-II/CREX experiments, analyzing data to provide useful information and systematic uncertainty for the PREX-II experiment, modeling and simulations for the MOLLER, and providing an alternative design of an electronic equipment for the P2 experiment.

Chen, Yufan↗

DOE OSTI2024

Simulations, Modeling and Data Analysis of Parity Violating Electron Scattering Experiments

Chen, Yufan↗

DOE OSTI2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Wei, Yuxiang [Georgia Institute of Technology]↗

DOE OSTI2026

California Bridge to the Electron-Ion Collider (EIC) (Final Technical Report)

This report summarizes the activities and outcomes of DOE Grant DE-SC0022358, a traineeship program designed to prepare students for future nuclear physics research at the Electron-Ion Collider. The project leveraged the California EIC Consortium and statewide academic networks to provide immersive research experiences spanning universities and national laboratories. Participants engaged in theoretical, computational, and experimental research related to EIC physics and took part in professional meetings and collaborative research activities. The program supported student preparation for advanced study and research careers in nuclear physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Aerodynamic Sensitivities over Separable Shape Tensors

Here, we present a comprehensive aerodynamic sensitivity analysis of airfoil parameterization informed by separable shape tensors. This parameterization approach uniquely benefits the design process by isolating various well-studied shape characteristics, such as airfoil thickness, and providing a well-regulated low-dimensional parameter domain for aerodynamic designs. Exploring the aerodynamic sensitivities of this novel parameterization can provide valuable insights for more robust designs and future manufacturing efforts. We construct a data-driven parameter space of airfoils using principal geodesic analysis of separable shape tensors informed by a curated database containing almost 20,000 suitable engineering airfoils. Analyzing the shape reconstruction error and the maximum mean discrepancy between joint distributions of aerodynamic quantities, we study the dimensionality of the learned parameter space. This simple numerical experiment demonstrates a dramatic dimension reduction that retains design effectiveness and promotes regularity of the shape representations. Finally, we generate new airfoils and use the HAM2D Reynolds-averaged Navier–Stokes solver to predict lift, drag, and moment coefficients. We compute multiple sensitivity metrics to quantify and assert the consistency of parameter influence on the aerodynamic quantities. We also explore low-dimensional polynomial ridge approximations to motivate physical intuitions and offer explanations of the approximated sensitivities.

17 WIND ENERGY↗

DOE OSTI2024

Reactive Processing of Furan‐Based Monomers via Frontal Ring‐Opening Metathesis Polymerization for High Performance Materials

Frontal ring-opening metathesis polymerization (FROMP) presents an energy-efficient approach to produce high-performance polymers, typically utilizing norbornene derivatives from Diels–Alder reactions. This study broadens the monomer repertoire for FROMP, incorporating the cycloaddition product of biosourced furan compounds and benzyne, namely 1,4-dihydro-1,4-epoxynaphthalene (HEN) derivatives. A computational screening of Diels–Alder products is conducted, selecting products with resistance to retro-Diels–Alder but also sufficient ring strain to facilitate FROMP. The experiments reveal that varying substituents both modulate the FROMP kinetics and enable the creation of thermoplastic materials characterized by different thermomechanical properties. Moreover, HEN-based crosslinkers are designed to enhance the resulting thermomechanical properties at high temperatures (>200 °C). The versatility of such materials is demonstrated through direct ink writing (DIW) to rapidly produce 3D structures without the need for printed supports. This research significantly extends the range of monomers suitable for FROMP, furthering efficient production of high-performance polymeric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring

Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, Hydra has analyzed over 2 million images and has extended its capabilities to offline monitoring and validation. Hydra utilizes computer vision to continually analyze sets of images of monitoring plots generated 24/7 during experiments. Generally, these sets of images are produced at a rate and quantity that is exceedingly difficult for shift crews to effectively monitor. Significant effort has been devoted to enhancing Hydra’s user interface, to ensure that it provides clear, actionable insights for shift workers and other users. Gradient Weighted Class Activation Maps (GradCAM) provide added interpretability, allowing users to visualize important regions of the image for classification. Hydra has been containerized to enable the creation of portable demos and seamless integration with container-based technologies such as Kubernetes and Docker. With the user interface enhancements and containerization, Hydra can be rapidly deployed for new use cases and experiments. This talk will describe the Hydra framework, its user interface and experience, and the challenges inherent in its design and deployment.

Britton, Thomas [Thomas Jefferson National Acceler↗

DOE OSTI2025

Designing Antifouling and Antimicrobial Interfaces: Structural Characterization using CryoEM, Automated Microscopy, and AI Image Segmentation

The design of functionalized surfaces for interactions with biological systems is critical across sectors such as healthcare, energy, and agriculture. Tailoring materials for specific applications, such as antifouling and antimicrobial surfaces, demands a comprehensive understanding of topology and chemistry across multiple length and time scales on both biological and materials systems. This work presents the development and characterization of nanostructured surfaces with controlled topographies and chemistries that enhance bacterial membrane disruption, reduce biofilm formation, and improve antimicrobial and antifouling capabilities. Two specific use cases will be presented - the use of cellulose nanocrystals (CNCs) for bacterial growth inhibition and the development of antifouling surfaces to prevent protein and bacterial adsorption [1-4]. By leveraging large language models (LLMs) for image segmentation and training [5], we enable automated analysis of terabyte-scale cryogenic electron microscopy (cryoEM) datasets. This analysis provides statistical insights into the biotic/abiotic interface and facilitates automated electron microscopy experiments to mitigate time and dose. The integration of cryogenic electron tomography (cryoET) and cryogenic focused ion beam (cryoFIB) milling enables high-resolution, near-native-state imaging and 3D reconstructions of bio/material interfaces [6]. Orthogonal characterization techniques and computational modeling further enhances our understanding, offering a robust platform for the design and optimization of next-generation functional surfaces [7].

Williams, Alexis [ORNL] (ORCID:0000000252835822)↗

DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗

DOE OSTI2025

NeuroCoreX: Brain-Inspired Computing from Code to Circuit

NeuroCoreX is an open-source codebase that enables the implementation of brain-inspired, energy-efficient neuromorphic computing models on FPGA hardware. Designed to support real-time learning, all-to-all neural connectivity, and flexible network architectures, NeuroCoreX offers a hands-on, accessible platform for exploring biologically inspired models of neural computation. It empowers researchers, students, and developers to implement and experiment with adaptive systems—bringing the power of neuromorphic computing to a broader community through a low-cost, scalable, and reconfigurable framework.

Gautam, Ashish [Oak Ridge National Laboratory (ORN↗