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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8

Crystals of $\mathrm{TELSAM}$–target protein fusions that exhibit minimal crystal contacts and lack direct inter-$\mathrm{TELSAM}$ contacts

While conducting pilot studies into the usefulness of fusion to TELSAM polymers as a potential protein crystallization strategy, we observed novel properties in crystals of two TELSAM–target protein fusions, as follows. (i) A TELSAM–target protein fusion can crystallize more rapidly and with greater propensity than the same target protein alone. (ii) TELSAM–target protein fusions can be crystallized at low protein concentrations. This unprecedented observation suggests a route to crystallize proteins that can only be produced in microgram amounts. (iii) The TELSAM polymers themselves need not directly contact one another in the crystal lattice in order to form well-diffracting crystals. This novel observation is important because it suggests that TELSAM may be able to crystallize target proteins too large to allow direct inter-polymer contacts. (iv) Flexible TELSAM–target protein linkers can allow target proteins to find productive binding modes against the TELSAM polymer. (v) TELSAM polymers can adjust their helical rise to allow fused target proteins to make productive crystal contacts. (vi). Fusion to TELSAM polymers can stabilize weak inter-target protein crystal contacts. We report features of these TELSAM–target protein crystal structures and outline future work needed to validate TELSAM as a crystallization chaperone and determine best practices for its use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Extension of OpenMC for Fixed Source Transmutation Calculations

This report documents work performed under a Strategic Partnership Project between Argonne National Laboratory (ANL) and the United Kingdom Atomic Energy Authority (UKAEA). The overall goal of this project is to extend OpenMC [1], a community-developed Monte Carlo particle transport code, to be able to perform fixed-source transmutation calculations. In fission and fusion reactors, the high flux of energetic neutrons causes materials within the reactor to “transmute,” or undergo a nuclear reaction that results in the addition/removal of neutrons and protons from the nucleus of an atom. If subjected to these reactions for long enough, the overall composition and physical properties of the material itself begin to change as a result of transmutation. Such a feature is vital for predicting the decrease in tritium production rate within a breeder blanket during the lifetime of a fusion reactor. OpenMC is capable of simulating neutron transport in fission/fusion systems, thereby allowing it to estimate the flux that causes transmutation. It is also capable of solving the transmutation equations, which determine how the composition of a material changes over time due to neutron irradiation and radioactive decay. However, solving the transmutation equations was previously only possible when the source of neutrons came from a fission system. In a fusion system, the source of neutrons is typically determined by a separate code and then given as an input to the particle transport simulation. This is known as a fixed source calculation. Through this project, we have extended OpenMC to solve the transmutation equations for a fixed source calculation. Evaluating the change in material compositions due to transmutation and its effect on physical properties is of key importance to a range of engineering analyses for fission and fusion systems. For example, in a fusion reactor, estimating the dose rate at different physical locations resulting from irradiated materials in the reactor allows designers to ensure that workers are not exposed to doses beyond applicable regulations. In order to properly dispose of irradiated materials, designers also need to estimate the radiotoxicity, which again relies on knowledge of the material composition at some future time. The specific tasks for this project that were agreed to between ANL and UKAEA were as follows: 1. Make changes and additions in the openmc.deplete and related modules in OpenMC to support transmutation calculations following a fixed source transport simulation. 2. Make necessary changes to OpenMC to model transmutation due to an arbitrary set of reactions needed for fusion applications. Use this new capability to generate a depletion chain file based on the TENDL nuclear data library. 3. Improve the openmc.deplete module in OpenMC to keep track of gases produced as a by-product of nuclear reactions during transmutation calculations. 4. Validate the new capabilities by carrying out fixed-source transmutation calculations on a suitable benchmark problem using OpenMC and a comparable Monte Carlo neutron transport code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Fusion-based occupancy sensing for building systems

Sensing and control apparatus for a building HVAC system includes interior and boundary sensors, such as cameras and thermal sensors, generating sensor signals conveying occupancy-related features for an area. A controller uses the sensor signals to produce an occupancy estimate and to generate equipment-control signals to cause the HVAC system to supply conditioned air to the area based on the occupancy estimate. The controller includes fusion systems collectively generating the occupancy estimate by corresponding fusion calculations, the fusion systems producing a boundary occupancy-count change based on sensor signals from the boundary sensors, an interior occupancy count based on sensor signals from the interior sensors, and the overall occupancy estimate. Fusion may be of one or multiple types including cross-modality fusion across different sensor types, within-modality fusion across different instances of same-type sensors, and cross-algorithm fusion using different algorithms for the same sensor(s).

Konrad, Janusz L.↗

NEML2: A High Performance Library for Constitutive Modeling

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

36 MATERIALS SCIENCE↗

Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics

In this report, during laser melting of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if processing conditions are chosen with high power density. An unstable keyhole can have deleterious effects in certain applications (e.g., laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, we propose a pipeline that enables complete segmentation and extraction of various geometric features in keyholing conditions. In situ synchrotron high-speed X-ray visualization at the Advanced Photon Source provides large datasets of experimental images with a high spatio-temporal resolution across a range of laser parameters for Ti-6Al- 4 V. Computer vision image processing techniques were used to extract time-resolved quantitative geometric features (e.g., depth, width, front wall angle) throughout keyhole evolution which were subsequently analyzed to understand the relationship between the variation of local keyhole geometry and processing conditions. This analysis is the first to employ a data-driven approach to further our understanding of the keyholing process regime.

36 MATERIALS SCIENCE↗

Charpy impact tests of tungsten fiber–reinforced composite from -150 °C to 1000 °C

Tungsten features a unique combination of properties that makes it a favorable plasma-facing material in fusion reactors. The main drawbacks of tungsten are its brittleness at room temperature and the potential embrittlement during operation. Tungsten fiber–reinforced composites have the potential to overcome these limitations. Charpy impact tests ranging from -150 °C to 1000 °C have been performed. To identify the ductile-to-brittle transition temperature and understand the behavior at high temperature a detailed microscopic investigation was used to identify the transition temperature of fiber and matrix, which was between -100 °C to -50 °C and 1000 °C, respectively.

36 MATERIALS SCIENCE↗

Atypical chemoreceptor arrays accommodate high membrane curvature

The prokaryotic chemotaxis system is arguably the best-understood signaling pathway in biology. In all previously described species, chemoreceptors organize into a hexagonal (P6 symmetry) extended array. Here, we report an alternative symmetry (P2) of the chemotaxis apparatus that emerges from a strict linear organization of the histidine kinase CheA in Treponema denticola cells, which possesses arrays with the highest native curvature investigated thus far. Using cryo-ET, we reveal that Td chemoreceptor arrays assume an unusual arrangement of the supra-molecular protein assembly that has likely evolved to accommodate the high membrane curvature. The arrays have several atypical features, such as an extended dimerization domain of CheA and a variant CheW-CheR-like fusion protein that is critical for maintaining an ordered chemosensory apparatus. Furthermore, the previously characterized Td oxygen sensor ODP influences CheA ordering. These results suggest a greater diversity of the chemotaxis signaling system than previously thought.

59 BASIC BIOLOGICAL SCIENCES↗

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗

Diverted Negative Triangularity plasmas on DIII–D: The benefit of high confinement without the liability of an edge pedestal

Diverted discharges at negative triangularity on the DIII-D tokamak sustain normalized confinement and pressure levels typical of standard H-mode scenarios (H 98y,2 ≅1, β N ≅3) without developing an edge pressure pedestal, despite the auxiliary power far exceeding the L → H power threshold expected from conventional scaling laws. The power degradation of confinement is substantially weaker than the ITER-89P scaling, resulting in a confinement factor that improves with increasing auxiliary power. Furthermore, the absence of the edge pedestal is beneficial in several aspects, such as eliminating the need for active mitigation or suppression of edge localized modes, low impurity retention and a reconstructed scrape-off layer heat flux width at the mid-plane that exceeds the ITPA multi-machine scaling law by up to 50%. Together with technological advantages granted by placing the divertor at larger radii, plasmas at Negative Triangularity without an edge pedestal feature both core confinement and power handling characteristics that are potentially suitable for operation in future fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗

Simulated JWST Data Sets for Multispectral and Hyperspectral Image Fusion

The James Webb Space Telescope (JWST) will provide multispectral and hyperspectral infrared images of a large number of astrophysical scenes. Multispectral images will have the highest angular resolution, while hyperspectral images (e.g., with integral field unit spectrometers) will provide the best spectral resolution. This paper aims at providing a comprehensive framework to generate an astrophysical scene and to simulate realistic hyperspectral and multispectral data acquired by two JWST instruments, namely, NIRCam Imager and NIRSpec IFU. We want to show that this simulation framework can be resorted to assess the benefits of fusing these images to recover an image of high spatial and spectral resolutions. To do so, we make a synthetic scene associated with a canonical infrared source, the Orion Bar. We develop forward models including corresponding noises for the two JWST instruments based on their physical features. JWST observations are then simulated by applying the forward models to the aforementioned synthetic scene. We test a dedicated fusion algorithm we developed on these simulated observations. We show that the fusion process reconstructs the high spatio-spectral resolution scene with a good accuracy on most areas, and we identify some limitations of the method to be tackled in future works. The synthetic scene and observations presented in the paper can be used, for instance, to evaluate instrument models, pipelines, or more sophisticated algorithms dedicated to JWST data analysis. Besides, fusion methods such as the one presented in this paper are shown to be promising tools to fully exploit the unprecedented capabilities of the JWST.

79 ASTRONOMY AND ASTROPHYSICS↗

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Reduction or Enhancement of Stellarator Turbulence by Impurities

A systematic study of the impact of impurities on the turbulent heat fluxes is presented for the stellarator Wendelstein 7-X (W7-X) and, for comparison, the Large Helical Device and ITER. By means of nonlinear multispecies gyrokinetic simulations, it is shown that impurities, depending on the sign of their density gradient, can significantly enhance or reduce turbulent ion heat losses. For the relevant scenario of turbulence reduction, an optimal impurity concentration that minimizes the ion heat diffusivity emerges as a universal feature. This result demonstrates the potential of impurities for controlling turbulence and accessing enhanced confinement regimes in fusion plasmas and, in particular, in W7-X. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Simple Integral Transport for Charged Particles

A short description of the simple integral transport (SIT) design, framework, and code is given. Explicit details are given for problems with spherical symmetry, as an explanation of the general features in context. This discussion sets up a simple calculation of alpha particle transport in inertial confinement fusion systems, as a proof of concept.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System: Preprint

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS↗

Printed Targets with Micron-Scale Feature Patterns for the Study of Ablator Defects on OMEGA

As per present models, laser imprint and implosion symmetry are insufficient to account for observed performance degradation of direct-drive cryogenic fusion implosions. More and better data are needed on ablator defects as a source of hydrodynamic instability and mix. To investigate this, a series of OMEGA experimental campaigns is underway to study isolated target defects. Key requirements are systematic variation of the laser intensity and pulse shape at shot, with highly controlled defect type, geometry, and location. Here, given the need for sub-micron resolution and precise registration of multiple features, two-photon polymerization (TPP) printing was identified as an ideal method to fabricate these targets. TPP printing has enabled controlled formation of designed domes, divots, and vacuoles for studying the combined effect of size and proximity of these features on the hydro performance.

Two-photon polymerization printing↗

Temperature effects of ion irradiation on the nanostructural features in ductile-phase-toughened tungsten composites

Ductile-phase toughened tungsten (DPT W) composites have emerged as promising candidates for load-bearing components behind the plasma-facing tungsten armor in fusion reactors due to their enhanced thermomechanical properties. This study focuses on a composite consisting of W particles embedded in a ductile NiFeW solution matrix, hot-rolled to a thickness reduction of 87% (87R DPT W). Sequential irradiations with Ni2+ and He+ ions were performed to identical doses and helium concentrations at room temperature (RT) and 1273 K. Irradiation at RT produced no discernible nanostructural features due to the immobility of mono-vacancies, whereas cavity formation was observed at 973 K. At 1273 K, the W phase exhibited larger cavities, reduced cavity number density, and lower volumetric swelling compared to 973 K. Notably, nanosized NiFeW precipitates formed within the W phase at 1273 K, a phenomenon absent at 973 K. A new phase of cubic (NiFe)6W6C was also observed at the interphase boundary. In contrast, the NiFeW matrix showed no nanostructural changes at 1273 K, likely due to cavity dissociation. Separate irradiations at 1273 K indicated that Ni2+ ions induced precipitate formation in the W phase, while He+ ions exclusively caused cavity formation. The microstructure of 87R DPT W irradiated at RT and subsequently annealed at 1273 K closely resembled that of material irradiated directly at 1273 K. Like oxide-dispersion-strengthened steels, the observed nanoparticle-embedded W can inhibit dislocation propagation, potentially delaying the ductile-to-brittle transition temperature. These findings highlight the potential of NiFeW nanoparticle-reinforced W composites as irradiation-resistant materials for fusion reactors.

Jiang, Weilin (ORCID:0000000183028313)↗