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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MHD flow in liquid metal blankets: Major design issues, MHD guidelines and numerical analysis

The design of breeding blankets represents the major challenge for fusion reactor engineering because of performance requirements and severe operating conditions in terms of heat load and neutron flux. Liquid metal alloys such as lead-lithium, due to their lithium content, can be used to breed tritium, one of the plasma fuel components, and owing to their high thermal conductivity, they may serve as coolants. On the other hand, there are technical issues related to the fact that the liquid metals are electrically conducting and interact with the plasma-confining magnetic field. Induced electric currents and generated electromagnetic forces affect velocity and pressure distribution in the blankets. Magnetohydrodynamic (MHD) flows for fusion applications have been often investigated in simplified geometries, such as pipes, ducts, bends, with focus on their fundamental features. These analyses are essential, since results remain valid as background for the development of blanket designs, even when a concept is dismissed. However, the conceptual study of fusion blankets requires to take into account the global multiple effects, that arise when the full system is considered. Progress made in fusion-related MHD research results from combined numerical and experimental activities. In this paper we review and summarize features of 2D and 3D MHD flows that are typical in liquid metal blankets, together with available correlations for MHD pressure losses. This knowledge can provide simple design MHD guidelines that support a preliminary estimate of MHD effects in a blanket concept, in terms of pressure drop and flow distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2004

Registration and Fusion of Multiple Source Remotely Sensed Image Data

Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

LeMoigne, Jacqueline↗
NASA NTRSDate not supplied

Experimental Characterization of Surface Roughness and Geometric Evaluations of Thin-Wall Laser Powder Directed Energy Deposition

Additive Manufacturing (AM) has offered many new design and manufacturing opportunities for components across various industries. As AM evolves there is a need to better understand outputs of the process including geometric limitations and surface roughness based on specific application requirements. One possible application area of AM are components using thin-wall (<2 mm) microchannel heat exchangers for subsystems across aerospace, and industrial applications. AM offers the opportunity to significantly reduce the cost and lead time associated with fabricating these complex parts with internal features such as an array of cooling channels. Laser Powder Bed Fusion (L-PBF) has been continuously demonstrated as a technique to fabricate heat exchangers with internal geometric complexities. However, L-PBF is limited in the scale at which parts can be fabricated. Laser Powder Directed Energy Deposition (LP-DED) has been demonstrated as an AM technology to significantly increase the scale of which thin-wall microchannel heat exchangers could be manufactured. Successful deployment of the LP-DED technology requires critical understanding of the resulting features from the build process. Surface roughness is one of the critical areas that effects the friction factor and pressure drop within a heat exchanger and must be well understood. This presentation will provide an overview of the characterization work completed to understand the LP-DED process for thin-walls representative of hydrogen resistant alloy NASA HR-1 that would be used in a Hydrogen-environment application. An overview of the experiments conducted with varying LP-DED parameters, evaluation of various geometric channel and geometric build features and resulting surface finish will be provided along with a summary of conclusions from these experiments. Results indicate that powder size is a major contributor and surface roughness varies between closed channel shapes and external surfaces.

Directed Energy Deposition↗
DOE OSTI2023

Hybrid Cyber-attack Detection in Photovoltaic Farms

Here, to address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment.

14 SOLAR ENERGY↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mechanisms behind impurity spreading in a channeled, long-leg divertor configuration

One suggested benefit of long-leg, tightly baffled divertor configurations, a common feature included in the designs of many next-generation fusion devices, is access to a delocalized “spread” of impurity radiation along the leg, allowing for a substantial radiation region while maintaining both sufficient distance from the core to prevent pollution of the plasma fuel and avoiding local overheating of the plasma-facing materials. However, it is unclear whether this increased connection length and divertor volume afforded by the long-leg configuration is compatible with impurity transport, and whether it will enable this desired impurity radiation spread at all. Here, the SOLPS 4.3 code is used to assess the distribution of nitrogen and neon impurity radiation in a long-leg, tightly baffled divertor geometry. Further, there is a strong correspondence between the distribution of impurity radiation in the long leg and impurity recycling on the divertor side walls with both the nitrogen simulation set and the neon simulation set, suggesting that impurity spreading along these long legs is due to the interplay between the cross-field transport and associated neutralization of the impurity on the material surfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

First result of boronization assisted by the ICWC on EAST with full metal wall

Boron (B), a low-Z (atomic number) material, has been widely utilized in wall conditioning to improve plasma performance in fusion devices. In 2023, boronization was successfully conducted on EAST featuring an ITER-like tungsten divertor and fully metallic first wall. The process employed predischarge coating with carborane (C 2 B 10 H 12 ) as the working material, assisted by ion cyclotron wall conditioning (ICWC). After one time 12 g boronization, it was found the thickness of B film was approximately 120 nm. Post-boronization observations indicated that substantial hydrogen (H) release during initial plasma discharges compared with the consumed W/B wall, attributed to H co-deposition during the ICWC-boronization processing, which led to uncontrollable divertor neutral pressure and plasma density. The H/(H + D) ratio demonstrated a gradual reduction from ∼85% to 30% over more than 1850 s of deuterium plasma, with a cumulative injected energy of 2325 MJ. The B coating significantly enhanced the stored energy in plasma and improved confinement performance. The stored energy in plasma showed an increase of about 20%, primarily due to a reduction in impurity radiation, including oxygen (O) and heavy impurities such as tungsten (W), iron (Fe), and copper (Cu). The effective ion charge (Z eff ) decreased from 2.3 to 2.0. Following ICWC-boronization, the line-integrated radiation profile decreased by nearly 35% in the plasma core, plasma density and electron temperature exhibited an increase of ∼7% and 12% due to enhanced wall fueling and reduced impurity radiation. The lifetime of boronization, as evaluated by the line emissions from boron and other impurity radiation, was about 1700 s of deuterium plasma, with a cumulative injected energy of 2125 MJ on EAST. These findings provide significant insights for evaluating ICWC-boronization applicability in ITER with full W wall structure.

EAST↗
DOE OSTI2026

Enabling On-Demand Aerospace Component Manufacturing: Topology Optimization of GE Engine Bracket and Fabrication Using Metal FFF

Additive Manufacturing (AM) offers advantages over conventional manufacturing processes, particularly by reducing the number of parts produced through multistage combined technologies, but these often result in low manufacturing yields or require post-processing. AM facilitates the production of complex geometries with fine features, overhangs, and lattice structures. For instance, Laser Powder Bed Fusion (LPBF) technology enables the fabrication of intricate parts that can be easily post-processed by removing residual powder. Laser powder bed AM technologies are widely discussed in the literature due to their design freedom in creating complex geometries, with and without the need for support generation. However, rapid solidification due to a thermal gradient in the build direction, which leads to the formation of columnar grains and warpage, is one of the challenges. To address this challenge, we propose layer-by-layer metal FFF technology, followed by the debinding and sintering process, as an alternative to powder- and laser-based approaches. Furthermore, design for additive manufacturing (DfAM) principles are discussed to minimize the need for support generation and enable easy post-processing, thereby improving surface finish to meet high tolerances in fabricating components for aerospace and healthcare applications.

Singh, Abhishek [University of Michigan, Ann Arbor↗
DOE OSTI2025

Stacked-mosaic amplifier and diode delivery concept for kJ-class inertial fusion energy laser driver

We report on a laser amplifier architecture designed as a modular element for scaling to multi-megajoule laser facilities intended for inertial fusion energy (IFE) power plants. This kilojoule-class module features a stacked mosaic of gain media integrated with a diode delivery system for efficient optical pumping of the mosaic-structured gain medium, enabling high-repetition-rate operation at high wall plug efficiencies, necessary for an IFE driver. Details of a diode delivery system capable of pumping the mosaic architecture are presented. Comprehensive numerical modeling demonstrates that the stacked-mosaic approach reduces transverse gain by five orders of magnitude compared to a conventional full-aperture Yb:YAG based amplifier, substantially suppressing transverse amplified spontaneous emission (TASE) and enabling enhanced longitudinal energy extraction. Detailed analysis of thermal management and wavefront distortion in a 4 × 4 mosaic array indicates that temperature gradients and thermally induced aberrations are effectively controlled using gas cooling and commercially available phase-plate and deformable mirror technologies. We further discuss the applicability of a stacked-mosaic architecture to direct-drive IFE schemes, where broad spectral bandwidth is critical, and its compatibility with frequency conversion modules for up-conversion to blue wavelengths. Finally, an example point design for a 10 kJ, 10 Hz Yb:YAG module operating at 175 K with wall-plug efficiency exceeding 10 % is presented, underscoring the feasibility of this approach for next-generation high-energy lasers for IFE drivers. The results establish the stacked-mosaic amplifier as a scalable, robust platform not only for IFE but also for a broad range of advanced scientific and industrial laser applications.

Lasers↗
NASA NTRS2018

Future of Big Earth Data Analytics

The state of the art of Big Earth Data Analytics can be expected to evolve rapidly in the coming years. The forces driving evolution come from both growth in the data and advancement in the field of data analytics. In the data area, advances in sensor instrumentation and platform miniaturization are increasing both data resolution and coverage, resulting in enormous growth in data Volume. Increases in temporal resolution in particular also generate demands for higher data Velocity. At the same time, the proliferation of instruments and the platforms on which they reside is increasing the Variety of datasets. The Variety increase in turn leads to questions about the Veracity of the data. In the algorithm area, powerful machine learning methods are coming to the fore, particularly Deep Neural Networks. These are powerful at detecting interesting features in the data, integrating many different measurements (i.e., data fusion), and classification problems. However, they are still challenging when seeking explanations of how natural or socio-economic phenomena work using Earth Observations. Thus, classical analysis techniques will remain relevant when the emphasis is on forming or testing explanations, as well as to support interactive data exploration.

Lynnes, Christopher↗
NASA NTRSDate not supplied

Integral Channel Nozzles and Heat Exchangers using Additive Manufacturing Directed Energy Deposition NASA HR-1 Alloy

Heat exchangers for use in propulsion applications are very critical components because they must be efficient, compact and light and often operate with working fluids at extreme temperatures or pressures or both. Various components and systems use heat exchangers such as combustion chambers of gas turbines and internal combustion engines, fuel cells (air supply and thermal management), electric batteries (thermal management), evaporators and recuperators of waste-heat-to-power systems, and rocket engines. Even if the results are more generally applicable, the heat exchangers applications to which this study is more closely related are regeneratively cooled rocket nozzles and chambers, and repressurization systems for the launch vehicles. These components are often thin-walled and contain pressurized fluids, like propellants at cryogenic or elevated temperatures. Given that the environments that these propulsion components must endure are challenging, the manufacturing to meet these specifications often require long lead times due to specialty processes and unique tooling associated with the combined thin-wall integral channel and large-scale structures. Additive manufacturing (AM) offers programmatic advantages for reduction in processing time and cost in addition to various technical advantages, including the possibility to achieve enhanced hardware complexity targeted to superior performance, part consolidation, and the capability of processing of novel alloys. While AM is already being utilized for heat exchanger components in propulsion applications, almost all these AM components are made by means of Laser Powder Bed Fusion (L-PBF). L-PBF allows for fine features but is rather limited with respect to the overall size of the components that can be manufactured. Recent developments are maturing the Laser Powder Directed Energy Deposition (LP-DED) process which may be used, for example, to make integral channel thin-wall regeneratively-cooled rocket nozzles with diameters greater than 1 m. This paper highlights some integral channel heat exchanger demonstrator hardware applications of LP-DED, as well as the characterization of this process in combination with the use of the NASA HR-1 alloy. To properly utilize LP-DED for heat exchanger manufacturing, various aspects are being characterized such as geometry limitations, measurement of surface texture and geometric angled surfaces, surface enhancements for internal channels, and material evaluation. NASA HR-1 (FeNi-Cr) is a high strength hydrogen resistant superalloy developed for use in aerospace applications, such as heat exchangers. Some aspects and considerations about the design of heat exchangers are summarized together with data relevant to LP-DED manufacturing in combination with the NASA HR-1 alloy. Microchannels were successful deposited down to 2.54 mm and 1 mm wall thickness, wall angles of 30°, both with high reproducibility. It was also found that the areal surface roughness is highly dependent on the size of the powder feedstock used for deposition. The characterization of these LP-DED features is critical for fluid flow and heat transfer predictions as it can be exploited to enhance heat transfer at the cost of increased pressure drop.

Additive Manufacturing↗
NASA NTRSDate not supplied

Integral Channel Nozzles and Heat Exchangers using Additive Manufacturing Directed Energy Deposition NASA HR-1 Alloy

Heat exchangers for use in propulsion applications are very critical components because they must be efficient, compact and light and often operate with working fluids at extreme temperatures or pressures or both. Various components and systems use heat exchangers such as combustion chambers of gas turbines and internal combustion engines, fuel cells (air supply and thermal management), electric batteries (thermal management), evaporators and recuperators of waste-heat-to-power systems, and rocket engines. Even if the results are more generally applicable, the heat exchangers applications to which this study is more closely related are regeneratively cooled rocket nozzles and chambers, and repressurization systems for the launch vehicles. These components are often thin-walled and contain pressurized fluids, like propellants at cryogenic or elevated temperatures. Given that the environments that these propulsion components must endure are challenging, the manufacturing to meet these specifications often require long lead times due to specialty processes and unique tooling associated with the combined thin-wall integral channel and large-scale structures. Additive manufacturing (AM) offers programmatic advantages for reduction in processing time and cost in addition to various technical advantages, including the possibility to achieve enhanced hardware complexity targeted to superior performance, part consolidation, and the capability of processing of novel alloys. While AM is already being utilized for heat exchanger components in propulsion applications, almost all these AM components are made by means of Laser Powder Bed Fusion (L-PBF). L-PBF allows for fine features but is rather limited with respect to the overall size of the components that can be manufactured. Recent developments are maturing the Laser Powder Directed Energy Deposition (LP-DED) process which may be used, for example, to make integral channel thin-wall regeneratively-cooled rocket nozzles with diameters greater than 1 m. This paper highlights some integral channel heat exchanger demonstrator hardware applications of LP-DED, as well as the characterization of this process in combination with the use of the NASA HR-1 alloy. To properly utilize LP-DED for heat exchanger manufacturing, various aspects are being characterized such as geometry limitations, measurement of surface texture and geometric angled surfaces, surface enhancements for internal channels, and material evaluation. NASA HR-1 (FeNi-Cr) is a high strength hydrogen resistant superalloy developed for use in aerospace applications, such as heat exchangers. Some aspects and considerations about the design of heat exchangers are summarized together with data relevant to LP-DED manufacturing in combination with the NASA HR-1 alloy. Microchannels were successful deposited down to 2.54 mm and 1 mm wall thickness, wall angles of 30°, both with high reproducibility. It was also found that the areal surface roughness is highly dependent on the size of the powder feedstock used for deposition. The characterization of these LP-DED features is critical for fluid flow and heat transfer predictions as it can be exploited to enhance heat transfer at the cost of increased pressure drop.

additive manufacturing↗
DOE OSTI2024

Preliminary Design of Ironless Linear Induction Motors for ITER MSE Shutter Actuators

In this article, the shutters need to be closed or opened during the operation and calibration period time for the fusion diagnostic systems, such as the International Thermonuclear Experimental Reactor (ITER) motional stark effect (MSE). Standard electric motors and actuators will not work in a strong magnetic field environment due to the presence of the magnetic field of fusion reactors. The innovative linear induction motor (LIM) with an ironless feature overcomes this kind of challenge and can be used for any application that requires controlled motion with a large stroke in the magnetic field environment. It consists of a high-electrical conductivity plate like copper and three-phase motor windings with nonferrous or stainless-steel stators. The conductor plate attached with the shutter is simply driven by the underneath three-phase linear windings to achieve linear motion. The motor drive can be controlled remotely by a controller using the electrical connection, so no sensitive electronic components are located in the harsh environment where the motor itself is located. The design requirements and test facility have been described. Several 3-D transient Maxwell electromagnetic (EM) models with different locations of three-phase linear motor windings and shutter stators have been analyzed and evolved to meet any applications allowed in the harsh environment inside the vacuum vessel of fusion reactors. The preliminary design results are presented in this article.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

LLNL FESP Theory Highlights: August 2024

The 2024 ABOUND SciDAC and BOUT++ combined workshop was held August 5-9 th 2024 at the University of California Livermore Collaboration Center (UCLC) in Livermore. Bringing together leading scientists and researchers from across the globe, this pivotal event focused on advancing plasma physics and boundary plasma dynamics within the context of fusion energy research. Key discussions throughout the meeting highlighted significant advancements in the BOUT++ framework, including enhanced simulations of small Edge Localized Modes (ELMs) and the initiation of integrating the integration of the 5D GEM gyrokinetic turbulence core code with the 2D SOLPS-ITER boundary transport code. These developments are crucial for managing heat loads in fusion reactors and supporting the longevity of plasma-facing components. The event also featured a session on Inter-SciDAC Collaborations, where principal investigators from multiple U.S. FES SciDAC tokamak projects explored opportunities for cross-collaboration. Additionally, the meeting showcased cutting-edge advancements in GPU acceleration and AI/ML technologies, poised to drive the next generation of fusion research. In his closing remarks, Dr. Xueqiao Xu emphasized the importance of the collaborative efforts and discussions that took place, noting their potential to shape future breakthroughs in fusion energy. The event underscored the global nature of the BOUT++ collaboration, with contributions from over 57 institutions worldwide. The 2024 BOUT++ and ABOUND Joint Hybrid Meeting continues to drive forward the research and innovations needed to achieve fusion energy, setting the stage for future collaboration and discovery.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Development and validation of fully open-source R2S shutdown dose rate capabilities in OpenMC *

We present the first fully open-source capabilities for shutdown dose rate (SDR) calculations of fusion energy facilities based on the Rigorous 2-Step (R2S) methodology. These capabilities have been implemented in the OpenMC Monte Carlo particle transport code, building on its existing capabilities while also leveraging new features that have been added to the code to support SDR calculations, such as decay photon source generation. Each of the individual physics components in the R2S workflow—neutron transport, activation, decay photon source generation, and photon transport—have been verified through code-to-code comparisons with MCNP6.2 and FISPACT-II 4.0. These comparisons generally demonstrate excellent agreement between codes for each of the physics components. The full cell-based R2S workflow was validated by performing a simulation of the first experimental campaign from the Frascati Neutron Generator (FNG) ITER dose rate benchmark problem from the Shielding INtegral Benchmark Archive and Database (SINBAD). For short cooling times, the dose calculated by OpenMC agrees with the experimental measurements within the stated experimental uncertainties. For longer cooling times, an overprediction of the shutdown dose was observed relative to experiment, which is consistent with previous studies in the literature. Altogether, these features constitute a combination of capabilities in a single, open-source codebase to provide the fusion community with a readily-accessible option for SDR calculations and a platform for rapidly analyzing the performance of fusion technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Latent code-based fusion: A Volterra neural network approach

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces. The so-called self-representation embedding of the latent codes leads to a simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter architecture achieved reduction in parameter complexity is primarily due to controlled non-linearities being introduced by the higher-order convolutions in lieu of generalized activation functions. Experimental results on two different datasets have shown a significant improvement in the clustering performance for VNNs auto-encoder over conventional Convolutional Neural Networks (CNNs) auto-encoder. In addition, we also show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a robust classification performance.

97 MATHEMATICS AND COMPUTING↗