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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)

The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Nanostructural features degrading the performance of superconducting radio frequency niobium cavities revealed by transmission electron microscopy and electron energy loss spectroscopy

Nanoscale defect structure within the magnetic penetration depth of ∼100 nm is key to the performance limitations of niobium superconducting radio frequency cavities. Using a unique combination of advanced thermometry during cavity RF measurements, and TEM structural and compositional characterization of the samples extracted from cavity walls, we discover the existence of nanoscale hydrides in electropolished cavities limited by the high field Q slope, and show the decreased hydride formation in the electropolished cavity after 120 °C baking. Furthermore, we demonstrate that adding 800 °C hydrogen degassing followed by light buffered chemical polishing restores the hydride formation to the pre-120 °C bake level. We also show absence of niobium oxides along the grain boundaries and the modifications of the surface oxide upon 120 °C bake.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Steam Reforming of Ethylene Glycol over MgAl₂O₄ Supported Rh, Ni, and Co Catalysts

Steam reforming of ethylene glycol (EG) over MgAl₂O₄ supported metal (15 wt.% Ni, 5 wt.% Rh, and 15 wt.% Co) catalysts were investigated using combined experimental and theoretical methods. Compared to highly active Rh and Ni catalysts with 100% conversion, the steam reforming activity of EG over the Co catalyst is comparatively lower with only 42% conversion under the same reaction conditions (500°C, 1 atm, 119,000 h⁻¹, S/C=3.3 mol). However, CH₄ selectivity over the Co catalyst is remarkably lower. For example, by varying the gas hour space velocity (GHSV) such that complete conversion is achieved for all the catalysts, CH₄ selectivity for the Co catalyst is only 8%, which is much lower than the equilibrium CH₄ selectivity of ~ 24% obtained for both the Rh and Ni catalysts. Further studies show that varying H₂O concentration over the Co catalyst has a negligible effect on activity, thus indicating zero-order dependence on H₂O. These experimental results suggest that the supported Co catalyst is a promising EG steam reforming catalyst for high hydrogen production. To gain mechanistic insight for rationalizing the lower CH₃ selectivity observed for the Co catalyst, the initial decomposition reaction steps of ethylene glycol via C-O, O-H, C-H, and C-C bond scissions on the Rh(111), Ni(111) and Co(0001) surfaces were investigated using density functional theory (DFT) calculations. Despite the fact that the bond scission sequence in the EG decomposition on the three metal surfaces varies, which leads to different reaction intermediates, the lower CH₄ selectivity over the Co catalyst, as compared to the Rh and Ni catalysts, is primarily due to the higher barrier for CH₄ formation. The higher S/C ratio enhances the Co catalyst stability, which can be elucidated by the facile water dissociation and an alternative reaction path to remove the CH species as a coking precursor via the HCOH formation. This work was financially supported by the United States Department of Energy (DOE)’s Bioenergy Technologies Office (BETO) and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for DOE by Battelle Memorial Institute. Computing time and advanced catalyst characterization use was granted by a user proposal at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). EMSL is a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research and located at PNNL.

cobalt↗
DOE OSTI2017

Aqueous-Phase Acetic Acid Ketonization over Monoclinic Zirconia

The effect of aqueous phase on the acetic acid ketonization over monoclinic zirconia has been investigated using first-principles based density functional theory (DFT) calculations. To capture the aqueous phase chemistry over the solid zirconia catalyst surface, the aqueous phase is represented by 111 explicit water molecules with a liquid water density of 0.93 g/cm3 and the monoclinic zirconia is modeled by the most stable surface structure . The dynamic nature of aqueous phase/ interface was studied using ab initio molecular dynamics simulation, indicating that nearly half of the surface Zr sites are occupied by either adsorbed water molecules or hydroxyl groups at 550 K. DFT calculations show that the adsorption process of acetic acid from the liquid water phase to the surface is nearly thermodynamically neutral with a Gibbs free energy of -2.3 kJ/mol although the adsorption strength of acetic acid on the surface in aqueous phase is much stronger than in vapor phase. Therefore it is expected that the adsorption of acetic acid will dramatically affects aqueous phase ketonization reactivity over the monoclinic zirconia catalyst. Using the same ketonization mechanism via the β-keto acid intermediate, we have compared acetic acid ketonization to acetone in both vapor and aqueous phases. Our DFT calculation results show although the rate-determining step of the β-keto acid formation via the C-C coupling is not pronouncedly affected, the presence of liquid water molecules will dramatically affect dehydrogenation and hydrogenation steps via proton transfer mechanism. This work was financially supported by the United States Department of Energy (DOE)’s Bioenergy Technologies Office (BETO) and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for DOE by Battelle Memorial Institute. Computing time and advanced catalyst characterization use was granted by a user proposal at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). EMSL is a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research and located at PNNL.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Openpronghorn

OpenPronghorn is a simulation tool specifically tailored for modeling thermal-hydraulic phenomena in advanced nuclear reactors. It is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source platform that facilitates the development of high-performance scientific computing applications. OpenPronghorn solves the Navier-Stokes equations, which describe the conservation of mass, momentum, and energy in fluid flows, using the finite volume numerical method. The code supports a wide range of fluid flow conditions that are applicable to nuclear reactors, including incompressible and weakly compressible flows, as well as single-phase and multiphase flows. It is capable of modeling diverse flow regimes, including laminar and turbulent flows, using various turbulence models such as the standard k-epsilon models, the v2f model, and the mixing length model. For multiphase flows, OpenPronghorn employs a mixture a Eulerian modeling approach with mixture, drift-flux, and full Eulerian models, and includes open-sourced interfacial transfer correlations for drag, exchange, and heat transfer coming from the scientific literature. OpenPronghorn's modular design allows it to handle multiscale simulations, ranging from detailed Reynolds-Averaged Navier Stokes (RANS) simulations to coarse-mesh and lumped parameter models. This flexibility enables users to perform high-fidelity simulations of specific reactor components as well as system-level analyses of entire reactor circuits. The code can be coupled with other MOOSE-based tools using the MultiApp system, allowing for the transfer of coupling quantities such as mass flow rates, heat fluxes, and boundary conditions between different simulation scales. One of the main features of OpenPronghorn is the it includes built-in validation cases from the open-source scientific literature and supports the implementation of user-defined models and correlations through MOOSE's FunctorMaterial system. OpenPronghorn is designed to be computationally efficient, leveraging the SIMPLE projection method for large-scale problems, and can be run on high-performance computing systems to handle the extensive computational demands of detailed reactor simulations. Overall, OpenPronghorn is a versatile and robust tool that provides critical insights into the thermal-hydraulic behavior of advanced nuclear reactors, supporting the design, safety, and optimization of next-generation nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
DOE OSTI2025

Application of Accelerator Technology to Quantum Information Science

The intersection of accelerator and quantum information science (QIS) offers a unique platform to advance both fields through shared technology and infrastructure. This talk will discuss the synergies which exist between these two vastly different but complementary domains. We demonstrate how we leverage pre-existing infrastructure and knowledge to perform research and development which helps to realize dramatic improvements in both 10 km long accelerators and 10 cm large quantum processors. We will explore niobium superconducting radio-frequency (SRF) cavities, a highly advanced technology that excels in efficiently storing electromagnetic energy, enabling ultra-long photon lifetimes critical for quantum processors and facilitating the characterization of quantum materials with parts-per-billion precision. We will also discuss how advancements in superconducting materials, cryogenic systems, and control techniques help to reduce cost and improve performance for both quantum systems and particle accelerators. Moreover, we will discuss cross-disciplinary applications such as dark-matter searches and demonstrate the convergence of these fields in addressing fundamental scientific questions.

Bafia, Daniel [Fermilab]↗
DOE OSTI2025

Fluoride-Cooled High-Temperature Pebble-Bed Reactor Reference Plant Model Updates

This work presents the latest improvements to, and investigations performed with, the Fluoride-Cooled High-Temperature Pebble-Bed Reactor reference plant models for the United States Nuclear Regulatory Commission. These models, developed with the Comprehensive Reactor Analysis Bundle, or BlueCRAB, serve as the foundation for the future development of detailed design evaluation models based on license applications. BlueCRAB is the code suite proposed for non-light-water reactor systems safety analyses, and it incorporates various simulation tools developed by the Nuclear Energy Advance Modeling and Simulation program, including the Griffin code for reactor physics, the Pronghorn and SAM codes for core thermal fluids, the BISON code for solid conduction and fuel performance, and the SAM code for system analysis. The primary objective of this work is to assess the level of readiness of BlueCRAB for modeling fluoride-cooled high-temperature pebble-bed reactors. We first developed numerical models in BlueCRAB that include the key physics for this technology, ensuring an adequate level of fidelity for modeling the core performance during accident scenarios. This was followed by simulation of transient scenarios, two loss-of-forced-cooling events (one protected and one unprotected), and two control rod withdrawal events (one delayed and one prompt supercritical reactivity insertion). The analysis includes comparisons between the 2-D thermal fluid porous media models in Pronghorn and SAM, comparisons between coupled Pronghorn-Griffin and coupled SAM-Griffin models for two loss-of-forced cooling events and one control rod withdrawal event, and comparisons between SAM single-solve and domain-overlapping approaches for multi-scale thermal fluid coupling. In addition, we performed comparisons between 3-D, 2-D, and 0-D neutronic models for the two control rod withdrawal scenarios with Pronghorn-Griffin. The results show that the BlueCRAB models led to physically intuitive solutions for the scenarios examined. The changes in the various scalar and vector fields such as the neutron flux, power, temperatures, densities, pressures, and velocities are all within the expected ranges, and their distributions can be explained from the system response of the transients and the geometric and material variations. Several comparisons suggest that the porous media models in Pronghorn and SAM can lead to similar solutions, even though they are based on different methodologies. The simulations demonstrate that there are differences between the various levels of fidelity, and it is advisable to have flexible tools that can cover the breadth and depth of needs that may arise in future technical evaluations. We believe that BlueCRAB’s capabilities represent a significant asset for confirmatory analyses aimed at resolving important safety questions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Fuel Performance Experiments and Modeling: Fission Gas Bubble Nucleation and Growth in Alloy Nuclear Fuels

Advanced fast reactor systems being developed under the DOE's Advanced Fuel Cycle Initiative are designed to destroy TRU isotopes generated in existing and future nuclear energy systems. Over the past 40 years, multiple experiments and demonstrations have been completed using U-Zr, U-Pu-Zr, U-Mo and other metal alloys. As a result, multiple empirical and semi-empirical relationships have been established to develop empirical performance modeling codes. Many mechanistic questions about fission as mobility, bubble coalescience, and gas release have been answered through industrial experience, research, and empirical understanding. The advent of modern computational materials science, however, opens new doors of development such that physics-based multi-scale models may be developed to enable a new generation of predictive fuel performance codes that are not limited by empiricism.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Lithiation-Induced Defect Engineering to Promote Oxygen Evolution Reaction

Exploring efficient electrocatalysts for oxygen evolution reaction (OER) is an urgent need to advance the development of sustainable energy conversion. Though defect engineering is considered an effective strategy to regulate catalyst activity for enhanced OER performance, the controllable synthesis of defective oxides electrocatalysts remains challenging. Here, oxygen defects are introduced into NiCo 2 O 4 nanorods by an electrochemical lithiation strategy. By tuning in situ lithiation potentials, the concentration of oxygen defects and the corresponding catalytic activity can be feasibly regulated. In addition, the relationship between the changes in the defect density and electronic structure and the lithiation cut-off voltages is revealed. The results show that NiCo 2 O 4 nanorods undertook intercalation and two-step conversion reaction, in which the lithiation-induced conversion reaction gives rise to a CoO@NiO-based structure with higher defect density and lower oxidation states. As a result, the defective CoO@NiO-based catalyst exhibits exceptional OER activity with an overpotential of 270 mV at 10 mA cm -2 , which is about 74 mV below the pristine nanomaterials. In conclusion, this research proposes a novel strategy to explore high-performance catalysts with structural stability and defect control.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Equivariant, safe and sensitive — graph networks for new physics

This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data for Beyond the Standard Model (BSM) discoveries. By integrating equivariance in the rapidity-azimuth plane with IRC-safe principles, our model significantly reduces computational overhead while ensuring theoretical consistency in identifying BSM scenarios amidst Quantum Chromodynamics backgrounds. The proposed GNN architecture demonstrates superior performance in tagging semi-visible jets, highlighting its potential as a robust tool for advancing BSM search strategies at high-energy colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Structure-guided utilization of lignocellulose for catalysis, energy, and biomaterials

As a complex composite of cellulose, hemicellulose, and lignin, plant lignocellulose has long served as a major resource for biomass conversion, materials engineering, and bio-based product development. High-resolution structural insights enabled by solid-state nuclear magnetic resonance (ssNMR) now allow the mapping of polymer interfaces, identification of functional group accessibility, and tracking of molecular organization during processing, all of which are critical factors for optimizing catalytic strategies. These insights could drive transformative progress in lignocellulose-based applications, including selective depolymerization, improved pretreatment design, and efficient upcycling of lignin into resins, plastics, and biomedical materials. In industry-relevant contexts, such as biofuel generation and renewable material manufacturing, understanding the hydration dynamics, cross-linking patterns, and structural heterogeneity is also essential. The ability to visualize these features in native biomass presents a unique opportunity to develop new strategies for sustainability and performance. As the structural toolbox continues to expand, it is becoming a central enabler for innovations in renewable energy, green chemistry, and advanced bioproducts.

bioproduct↗
DOE OSTI2025

X-ray Micro-Computed Tomography for Structural Analysis of All-Solid-State Battery at Pouch Cell Level

Characterizing the microstructure of all-solid-state batteries (ASSBs) during fabrication and operation is vital for their advancement, particularly as scaling to pouch cell levels introduces challenges in probing large-scale microstructural evolution. This work highlights the potential of synchrotron X-ray micro-computed tomography (sXCT) as a nondestructive, rapid (<30 min), and high-resolution technique for visualizing and quantifying key microstructural features, including overhang, porosity, contact loss, active surface area, and tortuosity, in all-solid-state pouch cells. The large field of view (up to millimeters) of sXCT enables detailed analysis at an industry-relevant scale, bridging the gap between laboratory research and commercial applications. Furthermore, integrating realistic sXCT-derived 3D models into multiphysics simulations could provide insights into chemo-mechanical degradation, particularly at the edges of the pouch cells, offering a pathway for designing robust, high-performance ASSBs. This perspective establishes sXCT as an indispensable tool for advancing both the understanding and the engineering of next-generation energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Time-resolved chemically-selective spectroscopic investigation of the redox reaction between hematite and aluminium

Thermite reactions –highly energetic redox processes between a metal and an oxide—are used in welding, propulsion, and the fabrication of advanced materials. When reduced to the nanoscale, these reactions exhibit enhanced energetic performance, but their ultrafast dynamics remain poorly understood. Gaining insight into charge transfer during these processes is essential for advancing applications in energy conversion and materials design. Here we show that the reaction between aluminium and hematite, a common iron oxide, can be tracked with femtosecond resolution using extreme ultraviolet (EUV) time-resolved absorption spectroscopy at the Fe M 2,3 and Al L 2,3 edges. By exciting the system with an ultrashort optical pulse and probing element-specific absorption changes, we observe an early spectral shift that reveals the formation of localized charge carriers (polarons). Comparing samples with different supporting substrates highlights ultrafast electron transfer from aluminium to hematite. These results demonstrate an approach to investigating charge flow in energetic materials and provide a basis for studying fast chemical reactions with chemical specificity.

Electron transfer↗
DOE OSTI2016

Photoionization of tungsten ions: experiment and theory for ${{\rm{W}}}^{2+}$ and ${{\rm{W}}}^{3+}$

Experimental and theoretical results are reported for single-photon single ionization of W 2+ and W 3+ tungsten ions. Experiments were performed at the photon-ion merged-beam setup of the Advanced Light Source in Berkeley. Absolute cross sections and detailed energy scans were measured over an energy range 20-90 eV at a bandwidth of 100 meV. Broad peak features with widths typically around 5 eV have been observed with almost no narrow resonances present in the investigated energy range. Theoretical results were obtained from a Dirac-Coulomb R-matrix approach. The calculations were carried out for the lowest-energy terms of the investigated tungsten ions with levels 5s 2 5p 6 5d 4 5 D J J = 0, 1, 2, 3, 4 for W 2+ and 5s 2 5p 6 5d 3 4 F J ' J ' = 3/2, 5/2, 7/2, 9/2 for W 3+ . Assuming a statistically weighted distribution of ions in the initial ground-term levels there is good agreement of theory and experiment for W 3+ ions. However, for W 2+ ions at higher energies there is a factor of approximately two difference between experimental and theoretical cross sections.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Overview of Wendelstein 7-X high-performance operation

The Wendelstein 7-X (W7-X) stellarator has completed two consecutive experimental campaigns OP 2.2 (Sep.-Dec. 2024) and OP 2.3 (Feb.-May 2025) under a new operational strategy enabling more than one year of uninterrupted device availability. This approach, supported by exceptionally high subsystem reliability, allowed sustained high-efficiency plasma operations with up to 80–100 discharges per day across a broad range of magnetic configurations. Several key technical upgrades-most notably the first operation of a 1.5 MW class steady-state gyrotron, a new steady-state pellet injector, and advanced real-time feedback control systems significantly enhanced heating, fueling, and plasma control capabilities. Together, these improvements enabled major advances in long-pulse performance, high-β operation, and confinement optimization. Long-pulse discharges achieved 1.8 GJ of injected energy under fully detached divertor conditions, while reduced-field scenarios facilitated record volume-averaged β values approaching 3%. High-performance plasmas with centrally peaked density profiles, created via neutral beam injection (NBI) or sustained pellet fueling, demonstrated strongly reduced turbulent transport and stellarator-record fusion triple products. Complementary studies of power exhaust and divertor heat loads revealed the role of scrape-off-layer drift physics in shaping strike-line patterns under attached conditions. Together, the results from OP 2.2 and OP 2.3 significantly expand the operational space of W7-X and strengthen its role as a leading platform for steady-state stellarator research and reactor-relevant plasma scenarios.

Wendelstein 7-X↗
DOE OSTI2012

NEAMS VLTS project :

The objective of the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) Very Long Term Storage (VLTS) Project is to develop a simple, benchmark model that describes the performance of Zry4 d-hydrides in cladding, under conditions of long-term storage of used fuel. This model will be used to further explore the requirements of hydride modeling for used fuel storage and transport. It is expected that this model will be further developed as its weaknesses are understood, and as a basis of comparison as the Used Fuel Disposition (UFD) Campaign explores more comprehensive, multiscale approaches. Cladding hydride processes, a thermal model, a hydride model API, and the initial implementation of the J2Fiber hydride model is documented in this report.

Hansen, Glen A.↗