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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Developing Multiphysics, Integrated, High-Fidelity, Massively Parallel Computational Capabilities for Fusion Applications Using MOOSE

As the need for fusion as a clean, sustainable, and abundant energy source grows internationally, so does the need for multiphysics, computational tools to model, study, and predict the complex interactions between plasma, materials, and engineering processes. These tools have a crucial role to play in solving scientific and engineering challenges and accelerating fusion energy deployment. To address these needs, modeling capabilities should enable massively parallel, multiphysics, fully integrated high-fidelity simulations of fusion systems. Additional attributes, such as being open source and modular while maintaining high software quality assurance standards will maximize impact by ensuring accessibility for all and wide acceptance, rapid expansion and development, as well as reliability, efficiency, and robustness. In this paper, we describe how the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has a track record of success in the fission space thanks to the attributes listed above, can be leveraged in the fusion energy field. We highlight key successes of the MOOSE application in the fission space and describe how MOOSE has been and is being applied to fusion applications in the United States---e.g., Tritium Migration Analysis Program, version 8 (TMAP8), MOOSE Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX)---and the United Kingdom---e.g., AURORA, Achlys, Apollo. These efforts aim to establish a suite of tools that can be further extended to accelerate fusion energy deployment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Kinetic Plasma Simulation in the MOOSE Framework: Verification of Electrostatic Particle In Cell Capabilities

In magnetic confinement nuclear fusion reactors, the interaction between the plasma edge and plasma facing components is extremely important. At the plasma edge, a kinetic representation such as particle-in-cell (rather than a fluid representation) is required to accurately capture the plasma behavior. General purpose particle-in-cell plasma simulation capabilities have been developed in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. This new capability is a part of the development of a new MOOSE-based framework for modeling plasma facing components, the Fusion ENergy Integrated multiphys-X (FENIX) framework. In this work, the verification of foundational particle-in-cell capabilities in FENIX is presented. This new plasma simulation capability has three main components: moving particles in discrete steps on the finite element mesh, mapping charge density from the particle's location to the finite element mesh, and solving for the electrostatic potential based on the charge density mapped from particles to the mesh. In this paper, simple verification problems demonstrating each of these new capabilities are presented, and future work includes electromagnetic capabilities and Monte Carlo collisions with neutral gas particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

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↗
DOE OSTI2025

Comparison of tungsten versus molybdenum for double shell capsules using machine learning design optimization

Double shell targets are an alternative ignition platform for inertial confinement fusion. One design consideration for double shell targets is the choice of inner shell material to help trap radiation emitted by the hot fuel to aid ignition. Materials such as molybdenum and tungsten are of interest for the inner shell layer of the targets. While molybdenum has a lower density that could inhibit instability growth and allow for radiography and code benchmarking, tungsten has a higher density that could provide more compression and confinement. These tradeoffs have been explored using optimized designs for each material. Our previous work [Vazirani et al., “Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 28, 122709 (2021); Vazirani et al., “Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 30, 062704 (2023); and Vazirani et al., “Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design,” Stat. Anal. Data Min. 17, e11698 (2024)] resulted in a multi-fidelity Bayesian optimization framework to find yield-optimized double shell target geometries. By leveraging simulations of varying fidelities (one-dimensional and two-dimensional) to inform one another, the multi-fidelity optimization was able to optimize a design in the highest fidelity with significantly fewer simulations than would be used in a systematic parameter scan. In this work, we apply the multi-fidelity Bayesian optimization to explore the optimized designs of double shell targets with molybdenum and tungsten inner shells as well as the physics producing the high performing implosions. A physics exploration of all the simulations used in this study shows trends in designs that contribute to high yields, ion temperatures, and fuel areal densities. Comparison of molybdenum and tungsten simulations shows that they can produce similar implosion conditions with different geometries, which would be important to study in experiments. Graded density layers produce varying performances with the two materials but continue to be of interest for future studies along with studies of doped inner shell materials and applied surface roughness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
DOE OSTI2024

Design and modeling of indirectly driven magnetized implosions on the NIF

The use of magnetic fields to improve the performance of hohlraum-driven implosions on the National Ignition Facility (NIF) is discussed. The focus is on magnetically insulated inertial confinement fusion, where the primary field effect is to reduce electron-thermal and alpha-particle loss from the compressed hotspot (magnetic pressure is of secondary importance). We summarize the requirements to achieve this state. The design of recent NIF magnetized hohlraum experiments is presented. These are close to earlier shots in the three-shock, high-adiabat (BigFoot) campaign, subject to the constraints that magnetized NIF targets must be fielded at room-temperature, and use ≲1 MJ of laser energy to avoid the risk of optics damage from stimulated Brillouin scattering. We present results from the original magnetized hohlraum platform, as well as a later variant that gives a higher hotspot temperature. In both platforms, imposed fields (at the capsule center) of up to 28 T increase the fusion yield and hotspot temperature. Integrated radiation-magneto-hydrodynamic modeling with the Lasnex code of these shots is shown, where laser power multipliers and a saturation clamp on cross-beam energy transfer are developed to match the time of peak capsule emission and the P2 Legendre moment of the hotspot x-ray image. The resulting fusion yield and ion temperature agree decently with the measured relative effects of the field, although the absolute simulated yields are higher than the data by 2.0−2.7×. The tuned parameters and yield discrepancy are comparable for experiments with and without an imposed field, indicating the model adequately captures the field effects. Self-generated and imposed fields are added sequentially to simulations of one BigFoot NIF shot to understand how they alter target dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Informing ARC divertor design and plasma facing material selection through integrated modeling

This INFUSE 2023 project between UCLA and Commonwealth Fusion Systems used computer modeling to test whether tungsten materials can survive in CFS's ARC fusion reactor divertor. The team simulated plasma conditions and material responses, finding that tungsten-rhenium alloys resist grain growth better than pure tungsten, and that hydrogen buildup depends more on particle flux than temperature. The work helps CFS design durable plasma-facing components for their fusion power plant.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Extracting Topological Orders of Generalized Pauli Stabilizer Codes in Two Dimensions

In this paper, we introduce an algorithm for extracting topological data from translation invariant generalized Pauli stabilizer codes in two-dimensional systems, focusing on the analysis of anyon excitations and string operators. The algorithm applies to Z d qudits, including instances where d is a nonprime number. This capability allows the identification of topological orders that differ from the Z d toric codes. It extends our understanding beyond the established theorem that Pauli stabilizer codes for Z p qudits (with p being a prime) are equivalent to finite copies of Z p toric codes and trivial stabilizers. The algorithm is designed to determine all anyons and their string operators, enabling the computation of their fusion rules, topological spins, and braiding statistics. The method converts the identification of topological orders into computational tasks, including Gaussian elimination, the Hermite normal form, and the Smith normal form of truncated Laurent polynomials. Furthermore, the algorithm provides a systematic approach for studying quantum error-correcting codes. We apply it to various codes, such as self-dual CSS quantum codes modified from the two-dimensional honeycomb color code and non-CSS quantum codes that contain the double semion topological order or the six-semion topological order. Published by the American Physical Society 2024

Physics↗
DOE OSTI2025

Computational Study of a Cryopump Design for Fusion Exhaust Gas Purification Using Direct Simulation Monte Carlo

Cryopump-based direct internal recycling (DIR) of fusion fuel is an attractive prospect because it provides pumping of helium ash from a reactor along with separation of helium and other impurities from the fuel. Previous studies have demonstrated a continuously regenerating cryopump, referred to as the Snail pump, that separates helium ash from fusion fuel for reactor-relevant flow rates. Here, in this study, a conceptual cryopump was designed as a proof-of-principle study to target other impurities, besides helium ash, from the fusion exhaust that would complement the Snail pump. The impurity-removal cryopump consists of four sets of chevron fins, two sets operating at 80 K and the other two at 30 K. A computational study using direct simulation Monte Carlo (DSMC) was performed with a gas mixture of 96.5% D2, 2.0% He, and 0.5% each of CO2, N2, and CH4. In this configuration, 80 K chevron fin sets are capable of capturing CO2 and 30 K sets capable of capturing CO2, N2, and CH4. The computational study showed that the cryopump is capable of reducing the impurity content by more than two orders of magnitude from the flow.

Adhikari, Nirajan [Oak Ridge National Laboratory (↗
DOE OSTI2024

Fusion Energy Research at Idaho National Laboratory: Experimentation and Simulation to Support Safety and Rapid Technology Development

Research into fusion energy is growing rapidly, responding to a call for sustainable sources of energy to replace fossil fuels and mitigate climate change. Within the United States, at least, researchers are also responding to the “Bold Decadal Vision” proposed by the White House, seeking to have a commercially relevant fusion pilot plant deployed within a decade. Before this can become a reality, many Fusion Science & Technology (FS&T) gaps remain. For over 45 years, Idaho National Laboratory has been at the forefront of addressing these FS&T gaps in the context of fusion safety and technology via the operation of world-leading experimental facilities within the Safety and Tritium Applied Research (STAR) Facility. Here, INL focuses on the tritium fuel cycle, conceptual system design studies, risk assessment, waste management, and materials safety. Modeling and Simulation (M&S) has also been a component of this portfolio of research, but, early on, focused on individual systems. Since 2019, active development and research on integrated whole device modeling tools based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework has been undertaken. This has culminated in a MOOSE-based version of the Tritium Migration and Analysis Program (TMAP), an INL code historically focused on tritium permeation and trapping within fusion systems. More recently, INL Laboratory Directed Research and Development funds have been used to create the Fusion ENergy Integrated multiphys-X (FENIX) code focused on scrape-off layer plasma physics and the first wall of a magnetically confined fusion device. This talk will focus on an overview of INL activities in the FS&T research area, with a particular focus on recent M&S activities and results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗
DOE OSTI2025

In situ Visible Light and Thermal Imaging Data from a Laser Powder Bed Fusion Additive Manufacturing Process Co-Registered to X-ray Computed Tomography and Fatigue Data

This dataset is comprised of in situ sensing data collected during a laser-based powder bed fusion additive manufacturing process, as well as rasterized scan path information, post-build X-ray computed tomography (XCT), and fatigue test results. A total of 64 cylinders, approximately 15 mm in diameter and 102 mm tall, were printed out of stainless steel 316H on a Colibrium Additive Concept Laser M2 Series 5 machine. Parameters known to produce dense material were used to construct 56 of these cylinders, while the remaining 8 cylinders were printed with relatively high energy density parameters prone to producing keyhole pores. In addition, two spatter generation blocks were constructed upstream of the 64 cylinders such that ejecta produced during the melting of the spatter generators were stochastically seeded onto the 64 cylinders. Based on previous experiments, these spatter particles were theorized to produce stochastic lack-of-fusion pores. During the construction of the build, high-resolution images of reflected light in the visible spectrum were captured both before and after recoating for each print layer. Additionally, temporally integrated thermal imaging in the near infrared spectrum produced integrated sum and max images on a layerwise basis. The multimodal in situ data has been co-registered to the build plate coordinate system, allowing for identification of process anomalies (e.g., spatter particles) apparent in the two sensors. Following construction of the build, the cylinders were subjected to XCT to identify internal flaws, and the resulting data have also been registered to the build plate coordinate system. Finally, 60 of the 64 cylinders were machined into fatigue coupons conforming to ASTM E466 and subsequently subjected to either high- or -low-cycle fatigue testing. The results of the fatigue tests have also been included in the dataset, and the XCT data corresponded to the approximate location of the gauge sections of the machine fatigue specimen geometry.

42 ENGINEERING↗
DOE OSTI2026

Improving the stability and performance of MagLIF implosions by applying dielectric coatings and increasing applied B z , fuel preheat, and load current

We report two magnetized liner inertial fusion (MagLIF) experiments that produced record thermonuclear D–D neutron yields of 2.11×10 13 and 2.33×10 13 . These yields are about a factor of two higher than previous MagLIF results. The experiments achieved ion temperatures of 3.0 and 3.3 keV and stagnation pressures of 1.6 and 1.3 Gbar. The inferred Lawson parameters were χ=0.2 and 0.1, which are the largest reported for MagLIF. The performance increase used a high-aspect-ratio beryllium liner with a dielectric coating and modest increases in preheat energy (∼2.2 kJ), peak current (18.5 MA), and axial magnetic field (15 T). Three-dimensional HYDRA simulations are consistent with the measured liner dynamics and fusion outputs. These results indicate a pathway to higher-yield MagLIF designs using coated, high-aspect-ratio liners and improved input parameters. Simulations further suggest that adding an ice fuel layer could increase yield by up to a factor of 2.5 by reducing liner convergence, instability feedthrough, and mix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

The Development of Kinetic and Radiation Hydrodynamics Modeling of Thermonuclear Burn Propagation in Isochoric p - 11 B Through the Support of the INFUSE Program

The report summarizes DOE INFUSE-supported work between HB11 Energy and the University of Rochester’s TriForce Institute to improve computational modeling of advanced fusion fuels, especially proton–boron-11 (p- 11 B). The project extended the TriForce particle-in-cell/Monte Carlo collision code to include physics needed for dense, high-temperature p- 11 B burn studies, including p- 11 B fusion reactions, three-alpha-particle reaction products, relativistic Coulomb collisions, large-angle nuclear scattering, bremsstrahlung radiation, inverse bremsstrahlung absorption, and photon transport. The upgraded models were verified against focused physics tests and against known deuterium–tritium burn behavior. The study then used one-dimensional spherical simulations to estimate the conditions required for thermonuclear burn propagation in isochoric p- 11 B fuel. The calculations found that burn propagation is possible in the model, but only under very extreme hot-spot conditions, such as about 7000 g/cm 3 at 500 keV or 9000 g/cm 3 at 300 keV for a 20-micron hot spot. These conditions are much more demanding than current demonstrated inertial confinement fusion hot spots. The report concludes that the INFUSE collaboration successfully advanced kinetic and radiation modeling capabilities for p- 11 B fusion and provided useful estimates of ignition requirements. However, the simulated fuel gains remain below what would be needed for practical inertial fusion energy, and further work is needed to reconcile differences among kinetic, radiation-hydrodynamic, and analytic models and to identify more achievable target designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Development, Verification, and Validation of an OpenFOAM-Based Solver for Modeling Inertial Fusion Energy Chambers

Our work seeks to introduce a computational tool tailored to the physics of inertial fusion energy chambers, in particular, those concepts based on thick liquid walls. In this approach, the structural materials are protected by several neutron mean-free-paths of renewable liquid and thus will be able to survive much longer than un-shielded walls, with virtually all structures lasting for the life of the plant and enabling the use of commercially available and qualified materials. The OpenFOAM-based solver named rhoCentralFoam has been used as a starting point. rhoCentralFoam belongs to the standard OpenFOAM solver toolset. It is a high-speed, explicit compressible flow solver with shock-capturing capability. While the main features have been retained, the solver had to be restructured to make use of tabular data for equations of states, a necessary addition to model the complex thermo-physical properties of ionized gasses. This entailed the need to change the independent state variables used by the solver, resulting in a new thermodynamic library and slightly different solution algorithm. Moreover, a radiation heat transfer model based on the P-1 approximation was added to the solver. The solver is verified against an analytical solution from the Sedov-Taylor-Neumann test problem to showcase the ability of the hydrodynamic solvers to handle strong shocks, whereas the P-1 model was verified using a simple one-dimensional problem with an analytical solution. Additionally, a validation case involving shock-wave propagation through jet array is presented, and the results are compared with experimental data from the open literature. Lastly, in order to showcase the utility of the solver for practical cases, we applied the refined solver to two representative scenarios: gas venting within the HYLIFE-II chamber and the compression of the gas following the partial ablation of the liquid wall.

Chamber dynamics↗
DOE OSTI2026

Full-wave modeling of RF waves in fusion plasmas with finite element method: Progress in past decades and its future role

This paper reviews the progress in computing radio frequency (RF) wave fields in fusion plasmas, specifically focusing on simulations utilizing the finite element method (FEM) over the past few decades. Computing RF wave fields in fusion plasmas presents unique challenges due to large simulation domains, complex antenna structures, non-local dielectric properties, and wide ranges of spatial scales. It highlights key developments and outlines future directions, primarily addressing waves in the ion cyclotron (IC) to lower hybrid (LH) frequency range. We begin with briefly revisiting earlier developments before the widespread availability of modern computer-aided engineering (CAE) software based on FEM. This historical perspective illuminates early progress and the physics difficulties that motivated ongoing work within the community. Modern wave simulations for RF antennas based on FEM are characterized by the use of detailed 3D antenna model geometry generated from engineering CAD software and localized wave dielectric model. Significant advancements have also been made in improving physics models to include phenomena such as RF sheath rectification and wave scattering. FEM-based RF simulations have also been applied to compute wave propagation in the core region, where the inclusion of non-local dielectric response is crucial. This is a challenging goal, and several promising approaches have been proposed in this area. Additionally, RF simulation development initiatives based on open-source libraries have gained popularity, demonstrating scalability and flexibility in extending physics models. This paper will discuss the advantages and disadvantages of using such a publicly available FEM library.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗