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Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗

Role of chemical disorder on radiation-induced defect production and damage evolution in NiFeCoCr

Understanding chemical disorder in many concentrated solid solution alloys (CSAs) at the levels of electrons and atoms has attracted increasing attention as a path forward to reveal and identify underlying mechanisms for extraordinary mechanical properties and improved radiation tolerance. Single-phase NiFeCoCr CSA is a common base for many high-entropy alloys (HEAs) that have shown improved mechanical strength and radiation tolerance. In this study, defect production and damage evolution in NiFeCoCr under ion irradiation at room temperature to dose over 20 dpa are determined using ion channeling technique along both <100> and <110> directions utilizing multiple probing beam energies. The results obtained from the multi-axial and multi-energy channeling analysis are compared with those previously obtained for Ni crystals irradiated under similar conditions. The influence of chemical complexity on defect production and clustering at early-stage under room temperature irradiation up to dose of 1 dpa is discussed based on positron annihilation spectroscopy results. Defect structure evaluation in Ni and NiFeCoCr is also discussed based on transmission electron microscopy results over a prolonged irradiation at both room and elevated temperatures. Compared with chemically complex NiFeCoCr, larger dislocation loops thus less lattice strain are expected to form in pure Ni. Moreover, the role of chemical disorder in this CSA is also investigated based on ab initio calculations using large supercells. Finally, to understand the impact of chemical complexity effect on defect structure evolution, this integrated research effort attempts to link the relatively large charge redistribution due to difference in valence electron counts resulting from alloying different 3d transition metal elements, moderate lattice distortion arising from similar adaptable atomic size, and notable suppressed or delayed damage evolution in NiFeCoCr.

36 MATERIALS SCIENCE↗

Biomimetic mineralization of positively charged silica nanoparticles templated by thermoresponsive protein micelles: applications to electrostatic assembly of hierarchical and composite superstructures

High information content building blocks offer a path toward the construction of precision materials by supporting the organization and reconfiguration of organic and inorganic components through engineered functions. Here, we combine thermoresponsiveness with biomimetic mineralization by fusing the Car9 silica-binding dodecapeptide to the C-terminus of the (VPGVG) 54 elastin-like polypeptide (ELP). Using small angle X-ray scattering, we show that the short Car9 cationic block is sufficient to promote the conversion of disordered unimers into 30 nm micelles comprising about 150 proteins, 5 °C above the transition temperature of the ELP. While both species catalyze self-limiting silica precipitation, micelles template the mineralization of highly monodisperse (62 nm) nanoparticles, while unimers yield larger polydisperse species. Strikingly, and unlike traditional synthetic silica, these particles exhibit a positive surface charge, likely due to cationic Car9 sidechains projecting from their surface. Capitalizing on the high monodispersity and positive charge of the micelle-templated products, we use smaller silica and gold particles bearing a native negative charge to create a variety of superstructures via electrostatic co-assembly. Furthermore, this simple biomimetic route to positively charged silica eliminates the need for multiple precursors or surface modifications and enables the rapid creation of single-material and composite architectures in which components of different sizes or compositions are well dispersed and integrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Solar Neutrino Detection with a Pixelated Liquid-Argon Time Projection Chamber

This thesis presents a study of low-energy solar neutrino detection using large-scale LArTPCs, focussing on novel pixelated readout technologies. Solar neutrinos offer a unique probe of fundamental neutrino properties and solar physics, but their detection in the MeV range is challenged by backgrounds. We investigate two complementary technologies: SoLAr, which integrates LArPix-based pixelated charge collection with Silicon Photomultipliers (SiPMs) in a hybrid anode design for simultaneous charge and light detection; and Q-Pix, a triggerless pixelated readout architecture based on charge integrate-reset circuits with local clocks, where Reset Time Differences encode ionisation waveforms via time-to-charge conversion. Two SoLAr prototypes were developed and operated, demonstrating VUV-sensitive SiPM performance in liquid argon and accurate charge-light signal matching with a charge detection threshold of $\sim 100 \mathrm{keV}$. We also implement a complete simulation and reconstruction framework, incorporating realistic detector geometry, electron transport, readout response, and detailed signal and background models, including intrinsic argon and radon progeny, as well as site-specific $\gamma$-ray and neutron fluxes. For Q-Pix, we demonstrate that with a pixel size of $4\times 4$ mm$^2$ and a reset threshold of 1 fC ($\sim 0.1475$ MeV), full-scale operation produces data volumes below 1 PB per 10 ktonne-year. For SoLAr, assuming a shielded DUNE-like detector and 100 ktonne-year exposure, we project uncertainties of $0.90\times 10^{-5}$ eV$^2$ on $\Delta m^2_{21}$ and 0.033 on $\sin^2\theta_{12}$, improving to $0.46\times 10^{-5}$ eV$^2$ and 0.025 with 400 kilotonne-year. At this higher exposure, we also obtain a day–night flux asymmetry at the level of $( -5.6 \pm 3.6 ) \%$. Combining Monte Carlo modelling, hardware validation, and advanced reconstruction techniques, this work establishes a path toward next-generation ktonne-scale LArTPCs as observatories for precision solar neutrino physics.

Ruiz Ferreira, Guilherme [Manchester U.] (ORCID:00↗

Lessons Learned from Open-Source Software Training Toward Expanding the Fusion Workforce

Within the United States (U.S.), the Fusion Innovation Research Engine (FIRE) Collaboratives seek to accelerate fusion technology development through wide-ranging community-driven research activities that bring together industry, laboratories, research institutes, and academia. Increasing the maturity of key components and systems for future fusion power plants (FPPs) toward commercialization, will, by necessity, increase the need for knowledgeable engineers, designers, and researchers in industry capable of integrating and further improving these technologies within industry FPP concepts. Further, various modeling and simulation packages support these programs and are under-development within them to drive design iteration and the development of FPP digital twins. To derive the greatest benefit from model output and capabilities, training these same specialists will be vital. Thus, the development of effective and accessible software onboarding and professional development opportunities focused on fusion will be key to keeping the pace of growth high in the coming decade and beyond. A similar need exists within the advanced fission reactor community, driven by an ever-growing need for carbon-free baseload power for industrial and data center applications. Within the U.S. and around the world, several open-source packages and frameworks exist to support this endeavor, and two we will highlight here are the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and OpenMC. These packages, notably, are also being utilized in the FIRE Collaboratives program. In this presentation, we will highlight the lessons learned from over 30 years of combined software development and training experience, focused on developing strong technical software foundations within the nuclear workforce, from students to professional engineers & scientists. We will connect this experience to present and emerging needs in the fusion energy community, and, finally, will outline possible paths forward to develop a large, robust, global fusion workforce.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The CMS Phase-2 Fast Beam Condition Monitor prototype test with beam

The Fast Beam Condition Monitor (FBCM) is a standalone luminometer for the High Luminosity LHC (HL-LHC) program of the CMS Experiment at CERN. The detector is under development and features a new, radiation-hard, front-end application-specific integrated circuit (ASIC) designed for beam monitoring applications. The achieved timing resolution of a few nanoseconds enables the measurement of both the luminosity and the beam-induced background. The ASIC, called FBCM23, features six channels with adjustable shaping times, enabling in-field fine-tuning. Each ASIC channel outputs a single binary asynchronous signal encoding time-of-arrival and time-over-threshold information. The FBCM is based on silicon-pad sensors, with two sensor designs presently being considered. This paper presents the results of tests of the FBCM detector prototype using both types of silicon sensors with hadron, muon, and electron beams. Irradiated FBCM23 ASICs and silicon-pad sensors were also tested to simulate the expected conditions near the end of the detector's lifetime in the HL-LHC radiation environment. Based on test results, direct bonding between the sensor and ASIC was chosen, and an optimal bias voltage and ASIC threshold for FBCM operation were proposed. The current design of the front-end test board was validated following the beam test and is now being used for the first front-end module, which is expected to be produced in summer 2025. These results represent a major step forward in validating the FBCM concept, first version of the firmware and establishing a reliable design path for the final detector.

Beam-line instrumentation (beam position and profi↗

ILAW Container Cost Reduction and Optimization Study

This study, performed on behalf of Washington River Protection Solutions (WRPS), examines the potential for making cost savings for the Immobilized Low Active Waste (ILAW) containers over the mission life of the Waste Treatment and Immobilization Plant (WTP). it builds on earlier study assessments 24590-LAW-RPT-M-01-001 Rev 0 ILAW Product Container Specifications Optimization Study and DOE/ORP-2013-02 Rev 0 Decision Analysis of Low-Active Waste Container Finishing Handling System Alternatives. The WTP design and installed equipment has evolved over an extended period ~20 years. Consequently, some of the existing requirements relating the ILAW containers may no longer be applicable under current mission plans where the glass filled containers are placed into the Integrated Disposal Facility (IDF) rather than stacked in an engineered purpose-built ventilated store as originally envisioned. The study team reviewed available WTP project reference documents to establish the functions the container must perform and assign requirements against each of these functions. With the assumptions that some of the existing WTP contract restrictions would not necessarily apply to the subsequent facility operations contract the container requirements were reviewed to identify those which were ‘hard constraints‘, imposed by regulatory requirements. Initially each of these hard constraints were reviewed, evaluated and documented to see if they precluded the use of an alternate less expensive material of construction, carbon steel, to the existing 304 L stainless steel in the current container specification. That review did not generate any fatal flaws for a change in material of construction to carbon steel allowing the study team to examine the impacts of material cost savings, potential changes to the design and requirements and impacts on interfaces of the containers with the existing WTP process equipment from container receipt, transfers, glass filling, cooling and export. In addition, a change in container target fill level was evaluated and qualitative, and quantitative where feasible, impacts to the container fabrication process assessed. Lastly, additional follow on development or testing activities to support the use of carbon steel were assessed and a recommended path forward with a ROM cost and timescale developed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Latency Analysis of the Nexus Digital Twin Framework

Real-time digital catalogs are increasingly relied upon to track metadata and connect disparate data sources for cloud-based data integration efforts. One such tool, Deeplynx Nexus is supporting real-time digital twin efforts through event-driven data integration and time-series queries. Nexus’s usefulness for these applications depends critically on how quickly individual records can be uploaded and downloaded, since delays directly affect the responsiveness of any system built on top of it. However, the actual latency a user should expect from Nexus has not been systematically measured before, particularly for the small, frequent transactions typical of live sensor feeds. Here we show that single-record round-trip latency is 61.1 ms on a local Nexus instance and 391.7 ms on the hosted production infrastructure, a roughly 6.4x difference driven primarily by fixed per-request overhead rather than data volume. This overhead dominates at small scale: comparing single-record and ten-record trials suggests approximately 56 ms of each single-record request is fixed connection and authentication cost rather than data-transfer time, meaning batching even a handful of records is substantially more efficient than transmitting them individually. At large batch sizes, this pattern reverses for uploads, which converge to near parity between local and hosted environments by 25,000-50,000 records, while download latency remains persistently 5.7-6.4x slower on hosted infrastructure even at scale. These results suggest that Nexus deployments intended for real-time digital twin applications should prioritize record batching over single-record transactions, and that download-path optimization on hosted infrastructure offers the largest remaining opportunity to reduce latency at scale. We anticipate these baseline measurements will serve as a reference point for future digital twin projects evaluating whether Nexus’s latency profile meets their real-time requirements, and as a benchmark for tracking the effect of future infrastructure or API changes.

99 - GENERAL AND MISCELLANEOUS↗

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗

RELAP-7 Application and Enhancement for FLEX Strategies and ATF Behavior under Extended Loss of AC Power Conditions

This report summarizes the results of a three-year research project sponsored by the U.S. Department of Energy (DOE) Nuclear Energy University Program (NEUP) to enhance and apply the RELAP-7 code by adding and improving several important components (e.g., a mechanistic Reactor Core Isolation Cooling (RCIC) system model) for thermal hydraulic studies of LWRs under ELAP conditions and evaluating the time available for transition to portable FLEX equipment. The project team included University of Massachusetts–Lowell, The Ohio State University, Texas A&M University, Idaho National Laboratory and Oak Ridge National Laboratory. In the Fukushima accident, it was found that the RCIC system played a crucial role in delaying core meltdown by almost three days in Fukushima Daiichi Unit 2, because of self-regulated operation of the steam driven RCIC turbine-pump injection system. Steam flow in the convergent-divergent nozzles of the RCIC Terry turbine is two-phase non-equilibrium transonic flow with homogenous nucleation condensation. To more accurately predict the dynamic process and behavior of the transonic compressible steam flow, a one-dimensional transient two-phase analytical model is presented. A simplified four-fluid model was employed in the present work with the consideration of four separate fluid fields: vapor, liquid film, entrained droplets and condensed droplets. The mass, momentum and energy interactions between the fluids were considered and modeled. An extended seven-equation non-equilibrium critical flow model was developed to obtain the critical pressure and velocities of each phase at the nozzle throat. To predict the wetness in the divergent section, a mechanistic nucleation condensation model was integrated in the nozzle analysis model, considering the generation and consequent growth of droplets. The governing differential equations on a staggered grid were discretized using the second-order Lax-Wendroff scheme with a flux limiter, and the Semi-Implicit Method for Pressure-Linked Equation (SIMPLE) algorithm was employed to solve the discrete linear system. To demonstrate the predictability and reliability of the physical models and the numerical method proposed in the present work, three representative nozzles were modeled and simulated. The results show good agreement with the available experimental data, even for condensation shock. Then, the 1D nozzle model was employed to obtain nozzle flow tables of the Terry turbine nozzle for different working pressures which can cover the operation pressure range of the RCIC system. A mechanistic RCIC turbine-pump system model was developed and implemented in the system code TRACE to simulate dynamic responses of the RCIC system under Beyond Design Basis Accident (BDBA) conditions. The turbine-pump governing equations are based on the control volume approach of the angular momentum balance. The physics based mechanistic RCIC model was developed using the TRACE control system components (i.e., signal variables, control blocks, and tables), and incorporated into a TRACE boiling water reactor (BWR) model. The TRACE model in this report has a detailed nodalization of the reactor pressure vessel (RPV), and all of the major flow paths and system components, including the safety relief valves (SRVs) and the containment suppression pool and drywell. Based on the nozzle flow tables generated from the 1D nozzle model developed, the turbine drive torque can be calculated from table lookup. Since the detailed specifications of the RCIC pump are unavailable, the homologous curves for a Bingham pump were used in the current pump component. A station black-out (SBO) accident test problem was selected to demonstrate the TRACE RCIC model. The short-term SBO simulations were performed for two cladding materials: Zircaloy and FeCrAl, to demonstrate the effect of the accident tolerant fuel cladding on fuel heat-up under BDBA accident conditions. The wetwell plays a vital safety role in SBO and other BWR accident scenarios in that it can reduce containment pressure and supply additional core make-up water. The suppression pool temperature distribution has a very large impact on both RPV and containment pressure. Thus, another novel contribution of the project comes mainly from an improved, systems-level wetwell model which can capture buoyancy-induced thermal stratification effects due to steam injection and condensation. A two-zone stratified wetwell model has been implemented in RELAP-7 and some results from that model are presented. This wetwell model is capable of simulating thermal stratification due to a low steam mass injection rate. With a low mass flow rate, the model assumes that all the steam condenses within the pipe and the resulting plume can be approximated with a purely buoyant, heat-source driven model. The wetwell model developed with these assumptions is adequate to simulate slow transients such as extended SBO transients.

42 ENGINEERING↗

Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold ($\tau$) and the maximum radiance ( T max ). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that T max is more important to the voxel state than $\tau$ . The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Finally, since ML models are fast, they may play integral roles in the optimization and control of such AM technologies.

36 MATERIALS SCIENCE↗

Report on Year-4 of Water NSTF Matrix Testing: Facility Maintenance and Accident Testing

Under support from the Department of Energy (DOE) and the Office of Advanced Reactor Technologies (ART), a large-scale test facility has been constructed at Argonne National Laboratory to generate NQA-1 qualified validation data for passive decay heat removal systems in advanced reactors. The Natural convection Shutdown heat removal Test Facility (NSTF) reflects key features of a ½ scale, water-based, Reactor Cavity Cooling System (RCCS) and is intended to study the behavior, bound performance, and ultimately guide design decisions for passive decay heat removal systems for advanced reactors. In addition to the experimental activities detailed in this report, a supportive computational modeling effort is on-going which has been demonstrated to significantly strengthen the experimental program while also improving accuracy of the computer models. Together these create a mutually beneficial relationship integral to meeting the overall program objective of examining the heat removal performance of the RCCS concept. This report serves as a summary of maintenance and experimental activities during the program’s fourth year of water-based operation. A planned six-month maintenance period began in August 2021, during which major inspections, repairs, cleaning, and installation of new instrumentation and data acquisition hardware were conducted. Most significantly, two heaters that faulted during Year-3 were repaired, allowing the facility to resume use of the full heated section area and full range of available electric power. The remainder of the year consisted of eight months of test operations, during which the facility logged 211 hours of active heating across one bake-out (following the maintenance period) and seven matrix tests; five classifieds as Accepted per NQA-1, one as Trending, and one as Failed. Testing began by performing two repeat cases to confirm expected facility response and behavior during both single- and two-phase flow conditions, ensuring no changes were introduced during the maintenance period that might have altered the thermal-hydraulic characteristics of the facility. In continuation of the power parametric series initiated in previous years, a high-power test case was then performed examining heat removal performance at a decay heat load equivalent to 2.4 MWt, full-scale, a level exceeding maximum design targets. Additional testing then introduced various blockage scenarios along the network piping, examining the effects of partial and complete blockages of the flow paths on the system behavior and heat removal performance. A study of static boiling tests directed at understanding the geysering two-phase instability was also conducted. The loop was filled only to the bottom of the tank outlet, creating an open loop configuration that prevents any natural circulation flow from occurring, and the heaters were powered on until the facility reached saturation conditions. Following, a series of quasi-steady-state conditions were introduced by adjusting the inventory level in the adiabatic chimney piping at decreasingly lower elevations above the heated region. A strong correlation of geysering characteristics and loop level was observed, with flow and temperature excursions decreasing in intensity, but increasing in frequency, as the fill were reduced to lower elevations along the chimney piping. Once the level fell very low in the chimney, at points near the top of the heated section, the system reached a stable state of continuous boiling without any occurrence of geysering eruptions. A final significant testing accomplishment this year was successful completion of an “accident scenario” test, whose operating conditions were based on a prototypic decay heat curve provided by Framatome and scaled for the NSTF. This test began by establishing steady-state, single-phase “normal operation” conditions, before simulating an accident trip where the availability of active cooling systems was lost. Loop temperatures gradually increased until reaching saturation and subsequent two-phase boiling flow. Over the course of an extended operational period along the defined decay heat curve, steam boil-off caused gradual but continued depletion of liquid inventory until reaching a critically low level causing flow stagnation and cessation of natural circulation heat removal. At this point, after nearly 72 hours of continuous operation, a cold refill was performed to replenish the system inventory and allow the facility to re-establish closed loop natural circulation flow and return to a safe operational state.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Flexible FlueCO2

Carbon dioxide (CO2) emission reductions remain a significant challenge on the path to clean energy. There are increasing legislative, social, and environmental factors motivating CO2 emissions reduction from power plants with carbon capture and storage (CCS). CCS in natural gas combined cycle (NGCC) power plants is critical to achieve a net-zero carbon electricity grid. Enhanced 45Q tax credits provide new incentives, but currently available technologies are unable to profitably operate in grids with deep variable renewable penetration which require flexible NGCC operation. Luna Labs has developed the FlueCO2 membrane to enable a profitable NGCC-CCS process. The FlueCO2 membrane couples steam transport across the membrane to CO2 transport in the opposite direction, enabling high capture efficiencies and low energy costs even at low CO2 concentrations. The dual-phase membrane can operate in the range of typical flue gas temperatures and pressures and does not require temperature or pressure cycling. Luna Labs’ FlueCO2 technology enables flexible and profitable operation of NGCC plants with lower capital investment and impact on electricity prices. In this Phase 1 project, Luna Labs utilized experimental testing, modeling, process simulation, and standardized costing methodologies to evaluate the techno-economic value of a 650 MW greenfield NGCC plant with FlueCO2 (NGCC-FlueCO2). Key design requirements for operation were established and plant performance under load-leveling conditions was validated through computational fluid dynamics and process modeling. Luna Labs developed a dynamic modeling tool which modeled plant operational modes across a variety of tax structures and electricity pricing scenarios to project the overall Net Present Value (NPV) of the NGCC-FlueCO2. FlueCO2 minimizes the impact of CCS integration on plant operation by integrating directly into the NGCC heat recovery steam generator (HRSG). By tapping into the plant’s low-pressure (LP) steam, operators can divert LP steam to the greenfield NGCC and/or CCS process in response to dynamic markets. Since FlueCO2 will not significantly affect HRSG (or NGCC) operation, CCS only turns off during peak power demand (>$250/MWh). Under baseload conditions, FlueCO2 lowers the capital (37%), energy (36%) and carbon capture (<$40/tonne) costs and can increase the overall plant lifetime NPV by approximately ~$1B in comparison with NGCC solvent-based capture reference cases (NETL Case 31B). Luna Labs has shared its costing tools with several interested partners and customers, which follows a generalizable approach to costing analysis.

Kelly, Jesse↗

Investigation of powertrain system decarbonization using electrically assisted turbocharging and hybridization in off-road vehicles

In recent years, the causes, effects, and existential threat of a global anthropogenic climate shift have drawn significant attention and stimulated mitigation efforts from all genres of scientific, political, and industrial bodies. The greenhouse effect and the detrimental environmental impact of excess greenhouse gas (GHG) emissions, like carbon dioxide, is well studied and targeted as the primary culprit for aversive action. However, some fields exist where reducing GHG emissions is met by considerable challenges. One such field is the production and operations of off-road vehicles. Applications of these vehicles are highly diverse and are often characterized by rugged, especially transient, and power intensive duty cycles that make one-fits-all vehicle configuration solutions impractical. This research surveys existing literature to identify the modern technologies packages and challenges that face development and configuration of powertrain systems which currently navigate the regulated emissions and unique duty cycle requirements of this market space. A novel powertrain concept is proposed and evaluated with respect to the pinnacle objectives of load performance improvement, criteria pollutant reduction, and reducing the life cycle GHG intensity of its operation. A sure-fire path to vehicular GHG reduction is through improving the fuel efficiency of internal combustion engines (ICEs), an entrenched component of the off-road vehicle sector. Unfortunately, this is more easily said than done. An approach that has proven successful in this endeavor is engine downsizing and turbocharging, where a larger engine is replaced with a smaller one with added air system boosting via turbocharger to reduce frictional and pumping losses while also enabling access to additional fuel energy. However, practical realization of these potential benefits is often impeded by the transient response capability of the smaller engine across the operating space. For this reason, downsized ICE powertrains have turned to electrified forced induction systems (EFISs) for a decoupling of exhaust energy and engine speed from boost capability. Platformed on 48V hybrid technology, these systems introduce the need for more sophisticated controls around the engine gas-exchange process for the management of boost performance and exhaust gas emissions. On this account, simulation studies utilizing a GT-POWER model of a turbocharged 4.5 L engine outfit with an EFIS using an electrically driven compressor (eBooster®) are conducted to provide insight into the performance of this air handling architecture on an off-road engine. The results show that improvement in transient torque response time in sync with reduction in engine-out soot and NOx emissions are possible with an engine recalibration that leverages the transient air-fuel ratio authority of the eBooster®. Benefits are further demonstrated when duty cycle simulations of the 48V mild-hybrid engine concept are exercised, showing an acute decrease in cumulative fuel usage and soot production. Powertrain hybridization is another technological pathway achieving pronounced GHG reduction successes in modern on-road vehicle applications through integration of Li-ion battery technology. In the off-road vehicle segment, a review of available literature concludes that hybridized architectures are present but generally lack the depth of technologies that have both high specific energy and power capabilities, and thus are limited in their inclusion of Li-ion batteries for ICE assistance and enhanced energy storage capability. Therefore, building on the mild-hybrid engine results, the downsized and eBoosted engine concept was integrated into a larger high-voltage battery-hybrid series-electric powertrain system. Hybrid powertrain parameter sensitivity studies were carried out in a numerical charge-sustaining framework, providing novel insights into power flows between the battery and the engine and how their respective capabilities and operation contribute to GHG and criteria pollutant emissions of diverse duty cycles. Application of supervisory power management introduced robustness into the power sourcing and battery SOC control process and showed that optimum specification of battery properties can yield synergies between GHG emissions and battery energy capacity. Furthermore, examination of recent literature on Li-ion batteries has shown that pack manufacturing is a highly energy intensive process, thereby producing considerable quantities of GHGs that scale with energy storage capacity. Also scaling with a battery’s energy storage capacity is its investment cost. In consideration of these factors, an inclusive technoeconomic and GHG life cycle analysis is conducted. This analysis systematically compares the carbon footprint and total cost of ownership associated with the proposed hybrid powertrain concept to reference and alternative powertrain configurations, facilitating a thorough evaluation of the decarbonization effectiveness and economic viability.

99 GENERAL AND MISCELLANEOUS↗

Proliferated Resilient Economical Half-Meter Aperture Space Telescopes (PREEMPT)

The PREEMPT LDRD was motivated by a major national need for better space-based imaging systems that are both high performing and affordable for the US Government. Current optical payloads for intelligence, surveillance and reconnaissance (ISR) and space domain awareness (SDA) missions can cost hundreds of millions of dollars and take years to develop, which makes it difficult to build the large constellations required for persistent, world-wide coverage. To address this, the team aimed to advance a different kind of large-aperture (>25 cm) telescope, called a monolithic telescope, in which key optical surfaces are built into a single piece of fused silica. This design greatly reduces payload and spacecraft complexity, the need for precision focus actuators, improves mechanical and thermal robustness, and lowers cost when compared with traditional Cassegrain telescopes that rely on many precisely aligned components. The project focused on five primary thrusts. The first thrust was to advance the concept of a V10, 25 cm, monolithic telescope forward from optical design to flight-ready stage. This was accomplished in partnership with Optimax, who delivered the first test unit in the early stages of the LDRD. The team developed several technologies necessary for this optic to be integrated into a flight demonstration. These include carbon fiber housings, highly detailed structural and thermal models and stress-reducing elastic averaging Hirth groove designs. These technologies resulted in a successful maturation of the optic, which is now slated to fly in late 2026/early 2027 for a demonstration mission. The second thrust was to advance the manufacturability of these optics. In collaboration with NIF’s optical manufacturing shop, we reduced polishing time from 480 hours to 65 hours through the implementation of optimized processes and new tools. The NIF team utilized a conceptual V8 (18 cm) optic to demonstrate this optimization, though it can be applied to the rest of the monolithic optic portfolio. Third, the team developed the first conceptual V20 (50 cm) payload, which is slated to be the next generation of LLNL optical payload systems. A set of structural, dynamic and thermal simulations were performed to identify potential challenges in the future development of this payload. Early-stage simulations suggest the payload is feasible, though thermal management will be the key focus area to maintain optimal performance. Fourth, a non-linear model of Viton was developed, to further enhance the reliability of our structural and dynamic models for future payloads. Viton acts as the primary interface material between the optic and its housing. Lastly, the team focused on successfully displaying the feasibility of using additively manufactured metal composites for optical space payloads. The team successfully demonstrated layer by layer deposition of Al-SiC composites, which have highly tunable structural and coefficient of thermal expansion (CTE) properties. These are crucial for optical payloads because CTE mismatch is one of the causes for degraded optical performance for telescopes in orbit. Overall, the work showed that monolithic telescopes could become a practical, lower-cost path to high-resolution space imaging for both national security and scientific missions.

42 ENGINEERING↗

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. Here, the contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗