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At least 1,495 records · Page 83

Coupled 2D MHD and runaway electron fluid simulations of SPARC disruptions

Runaway electrons (REs) generated during disruption events in tokamaks can carry mega-Ampère level currents, potentially causing damage to plasma-facing components. Understanding RE evolution during disruption events is important for evaluating strategies to mitigate RE damage. Using two-dimensional toroidally symmetric magnetohydrodynamic (MHD) simulations in M3D-C1, which incorporates a fluid RE model evolved self-consistently with the bulk MHD fluid, we examine the seeding and avalanching of REs during disruptions in the SPARC tokamak – a compact, high-field, high-current device designed to achieve a fusion gain Q > 2 in deuterium–tritium plasmas. The M3D-C1 simulations of unmitigated disruptions demonstrate RE plateau formation and peaking of the final current density, which agree well with the results of lower-fidelity reduced RE fluid models. This work provides the first systematic comparison and benchmarking of different primary sources, including activated tritium beta decay and Compton scattering, in SPARC disruption simulations with self-consistent MHD and RE coupling.

Compton scattering↗

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗

Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence–function relationships across protein engineering campaigns

The discovery and engineering of new enzymes is important across the bioeconomy, with diverse applications from foods to pharmaceuticals, sensors to agriculture. However, enzyme engineering, in particular machine learning-guided engineering, is hampered by a lack of data. Currently there exists no database designed to capture and interpret datasets created in this domain, nor are there easy analysis and visualisation tools. We developed the Enzyme Engineering Database to provide a centralized resource and an online analysis tool to consolidate sequence-function data from enzyme engineering campaigns, thereby making three contributions: (i) a database into which researchers can deposit public data, (ii) visualisation and analysis tools for protein engineers to analyse their own data or compare enzyme variants to other engineering campaigns, and (iii) a gold-standard dataset for benchmarking automated extraction along with the first large language model extraction pipeline specific for enzyme engineering campaigns. The Enzyme Engineering Database is accessible at http://enzengdb.org/.

Long, Yueming [California Institute of Technology ↗

Combined search for neutrinos from dark matter self-annihilation in the Galactic Center with ANTARES and IceCube

In this work, we present the results of the first combined dark matter search targeting the Galactic Centre using the ANTARES and IceCube neutrino telescopes. For dark matter particles with masses from 50 to 1000 GeV, the sensitivities on the self-annihilation cross section set by ANTARES and IceCube are comparable, making this mass range particularly interesting for a joint analysis. Dark matter self-annihilation through the $\tau^+\tau^-$, $\mu^+\mu^-$, $b\bar{b}$ and $W^+W^-$ channels is considered for both the Navarro-Frenk-White and Burkert halo profiles. In the combination of 2,101.6 days of ANTARES data and 1,007 days of IceCube data, no excess over the expected background is observed. Limits on the thermally-averaged dark matter annihilation cross section $\langle\sigma_A\upsilon\rangle$ are set. These limits present an improvement of up to a factor of two in the studied dark matter mass range with respect to the individual limits published by both collaborations. When considering dark matter particles with a mass of 200 GeV annihilating through the $\tau^+\tau^-$ channel, the value obtained for the limit is $7.44 \times 10^{-24} \text{cm}^{3}\text{s}^{-1}$ for the Navarro-Frenk-White halo profile. For the purpose of this joint analysis, the model parameters and the likelihood are unified, providing a benchmark for forthcoming dark matter searches performed by neutrino telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗

Search for heavy right-handed Majorana neutrinos in the decay of top quarks produced in proton-proton collisions at s = 13 TeV with the ATLAS detector

A search for heavy right-handed Majorana neutrinos is performed with the ATLAS detector at the CERN Large Hadron Collider, using the 140 fb -1 of proton–proton collision data at $\sqrt{s}$ =13 TeV collected during Run 2. This search targets $t\overline{t}$ production, in which both top quarks decay into a bottom quark and a W boson, where one of the W bosons decays hadronically and the other decays into an electron or muon and a heavy neutral lepton. The heavy neutral lepton is identified through a decay into an electron or muon and another W boson, resulting in a pair of same-charge same-flavor leptons in the final state. This paper presents the first search for heavy neutral leptons in the mass range of 15–75 GeV using $t\overline{t}$ events. No significant excess is observed over the background expectation, and upper limits are placed on the signal cross sections. Assuming a benchmark scenario of the phenomenological type-I seesaw model, these cross section limits are then translated into upper limits on the mixing parameters of the heavy Majorana neutrino with Standard Model neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

PNNL-CompBio/coderdata

Automation scripts and benchmark dataset package for cancer drug prediction deep learning models.

Gosline, Sara JC [Pacific Northwest National Labor↗

Bayesian Optimization Framework for Imperfect Data or Models

Conventional Bayesian optimization methods implicitly assume that the data and model being optimized are “perfect.” This assumption leads to inaccurate posterior probability distribution functions (PDFs) when applied to “imperfect” data or models. The new Bayesian optimization framework presented in this report provides a way to parameterize the effect of imperfections usually encountered in a prior PDF of generalized data or a model on the posterior PDF. The effects of imperfections are parameterized by a set of constraints imposed on the posterior expectation values of deviations between the data and the model and on their covariance matrix elements. A particular set of values for these constraints conveys an evaluator’s best estimate of the effect of imperfections on the corresponding posterior expectation values. When a prior PDF of generalized data is assumed to be normal, an expression for a posterior PDF satisfying an arbitrary set of constraints is derived analytically for linear models. An analogous iterative algorithm is given for nonlinear models. The corresponding posterior PDF should be used to estimate any posterior expectation values in the presence of imperfections parameterized by that set of constraints. A posterior PDF of a conventional Bayesian optimization method is recovered analytically when all evaluator-specified constraints are set to zero (i.e., in the absence of any imperfections). The analytical expressions derived in this report for normal PDFs and linear models were verified numerically by a Metropolis–Hastings Monte Carlo method. The methods presented herein could be applied to any kind of data or models, including differential cross-section data or integral benchmark experiments.

97 MATHEMATICS AND COMPUTING↗

Relating Oxidative Protein Damage to Antioxidant Status in Health and Disease (Full Technical Report for 24-LW-026)

This two-year project evaluated how dietary antioxidants influence oxidative damage in cancer using complementary analytical and in-vivo approaches. We initially developed a protein oxidation labeling workflow and a parallel accelerator and molecular mass spectrometry (PAMMS) quantification method, but ultimately discontinued the labeling strategy due to unresolved separation challenges; PAMMS was instead leveraged to quantify radiolabeled catechol in rat plasma as a methodological benchmark. The biological study used a genetically engineered murine model (GEMM) for breast cancer (n = 40; four groups of 10: cancer/high antioxidant diet, cancer/normal diet, healthy/high-antioxidant diet, healthy/normal diet). In lieu of the abandoned labeling assay, untargeted metabolomics profiled plasma across groups, revealing widespread treatment-dependent changes in metabolites.

59 BASIC BIOLOGICAL SCIENCES↗

A Review of Candidates for a Validation Data Set for High-Assay Low-Enrichment Uranium Fuels

Many advanced reactor concept designs rely on high-assay low-enriched uranium (HALEU) fuel, enriched up to approximately 19.75% 235 U by weight. Efforts are underway by the US government to increase HALEU production in the United States to meet anticipated needs. However, very few data exist for validation of computational models that include HALEU, beyond a few fresh fuel benchmark specifications in the International Reactor Physics Experiment Evaluation Project. Nevertheless, there are other data with potential value available for developing into quality benchmarks for use in data- and software-validation efforts. This paper reviews the available evaluated HALEU fuel benchmarks and some of the potentially relevant benchmarks for fresh highly enriched uranium. It then introduces experimental data for HALEU fuel irradiated at Idaho National Laboratory, from relatively recent irradiation programs at the Advanced Test Reactor. Such data should be evaluated and, if valuable, collected into detailed benchmark specifications to meet the needs of HALEU-based reactor designers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗

HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs

Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for hardware platforms supporting heterogeneous arbitrary precision arithmetic. In our experiments, HGQ shows superior performance compared to existing network compression methods, achieving orders of magnitude reduction in resource consumption and latency while maintaining the accuracy on several benchmark tasks. These improvements enable the deployment of complex models previously infeasible due to resource or latency constraints. HGQ is open-source and is used for developing next-generation trigger systems at the CERN ATLAS and CMS experiments for particle physics, enabling the use of advanced machine learning models for real-time data selection with sub-microsecond latency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA Low-Speed Centrifugal Compressor for Fundamental Research

A centrifugal compressor facility being built by the NASA Lewis Research Center is described; its purpose is to obtain benchmark experimental data for internal flow code verification and modeling. The facility will be heavily instrumented with standard pressure and temperature probes and have provisions for flow visualization and laser Doppler velocimetry. The facility will accommodate rotational speeds to 2400 rpm and will be rated at pressures to 1.25 atm. The initial compressor stage for testing is geometrically and dynamically representative of modern high-performance stages with the exception of Mach number levels. Design exit tip speed for the initial stage is 500 ft/sec with a pressure ratio of 1.17. The rotor exit backsweep is 55 deg from radial.

Wood, J. R.↗

NASA low-speed centrifugal compressor for fundamental research

A new centrifugal compressor facility being built by the NASA Lewis Research Center is described; its purpose is to obtain 'benchmark' experimental data for internal flow code verification and modeling. The facility will be heavily instrumented with standard pressure and temperature probes and have provisions for flow visualization and laser Doppler velocimetry. The facility will accommodate rotational speeds to 2400 rpm and will be rated at pressures to 1.25 atm. The initial compressor stage for testing is geometrically and dynamically representative of modern high-performance stages with the exception of Mach number levels. Design exit tip speed for the initial stage is 500 ft/sec with a pressure ratio of 1.17. The rotor exit backsweep is 55 deg from radial. The facility is expected to be operational in the first half of 1985.

Wood, J. R.↗

Finite element analysis of flexible, rotating blades

A reference guide that can be used when using the finite element method to approximate the static and dynamic behavior of flexible, rotating blades is given. Important parameters such as twist, sweep, camber, co-planar shell elements, centrifugal loads, and inertia properties are studied. Comparisons are made between NASTRAN elements through published benchmark tests. The main purpose is to summarize blade modeling strategies and to document capabilities and limitations (for flexible, rotating blades) of various NASTRAN elements.

Mcgee, Oliver G.↗

Aeroassist flight experiment aerodynamics and aerothermodynamics

The problem is to determine the transitional flow aerodynamics and aerothermodynamics, including the base flow characteristics, of the Aeroassist Flight Experiment (AFE). The justification for the computational fluid dynamic (CFD) Application stems from MSFC's system integration responsibility for the AFE. To insure that the AFE objectives are met, MSFC must understand the limitations and uncertainties of the design data. Perhaps the only method capable of handling the complex physics of the rarefied high energy AFE trajectory is Bird's Direct Simulation Monte Carlo (DSMC) technique. The 3-D code used in this analysis is applicable only to the AFE geometry. It uses the Variable Hard Sphere (VHS) collision model and five specie chemistry model available from Langley Research Center. The code is benchmarked against the AFE flight data and used as an Aeroassisted Space Transfer Vehicle (ASTV) design tool. The code is being used to understand the AFE flow field and verify or modify existing design data. Continued application to lower altitudes is testing the capability of the Numerical Aerodynamic Simulation Facility (NASF) to handle 3-D DSMC and its practicality as an ASTV/AFE design tool.

Brewer, Edwin B.↗

Importance of real gas effects in entry technology

Critical issues concerning real gas effects in hypervelocity flows where the thermochemical nonequilibrium effects are pronounced, are described. Emphasis is on the development and validation of benchmark analysis tools. Approaches to develop and/or enhance phenomenological models, including test techniques to acquire databases, and incorporate them into computational flow field simulation codes are described. The performance characteristics of shock tubes, ballistic ranges and arc heated wind tunnels, state of the art diagnostics are included. The following aerothermodynamic phenomena are discussed: aerodynamic parameters, viscous interactions, turbulent transition, forebody heating/heat transfer, radiative heating, Lee-Base flows and low density real gas.

Deiwert, George S.↗

Design of an Improved Heater Array to Measure Microscale Wall Heat Transfer

An improved array of microscale heaters is being developed to measure the heat transfer coefficient at many points underneath individual bubbles during boiling as a function of space and time. This heater array enables the local heat transfer from a surface during the bubble growth and departure process to be measured with very high temporal and spatial resolution, and should allow better understanding of the boiling heat transfer mechanisms by pin-pointing when and where in the bubble departure cycle large amounts of wall heat transfer occur. Such information can provide much needed data regarding the important heat transfer mechanisms during the bubble departure cycle, and can serve as benchmarks to validate many of the analytical and numerical models used to simulate boiling. The improvements to the heater array include using a silicon-on-quartz substrate to reduce thermal cross-talk between the heaters, decreased space between the heaters, increased pad sizes on the heaters, and progressive heater sizes. Some results using the present heater array are discussed.

Kim, Jungho↗