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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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Joint Sensitivity Analysis (JOSA) Exercise Meeting Sept. 21, 2021.
Abstract not provided.
Advanced Use of Tracers in Repository Performance Assessment Modeling.
Abstract not provided.
ADVANCED USE OF TRACERS IN REPOSITORY PERFORMANCE ASSESSMENT MODELING.
Abstract not provided.
Use of Virtual Tracers in Repository Performance Assessment Modeling.
Abstract not provided.
Sensitivity and uncertainty analysis of FMD model choice for a generic crystalline repository .
Abstract not provided.
Understanding and Predicting Stress Corrosion Cracking of SNF Dry Storage Canisters.
Abstract not provided.
Predicting Localized Pitting Corrosion of Austenitic Stainless Steels Utilized in Spent Nuclear Fuel Storage.
Abstract not provided.
Probabilistic Model for Stress Corrosion Cracking of SNF Dry Storage Canisters
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Aging Mechanisms of ~800 nm Broad-Area Laser Diodes
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Understanding Stress Corrosion Cracking of SNF Dry Storage Canisters
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Uncertainty Quantification Implementation for Quantitative Risk Assessments of Hydrogen Systems
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Direct HCN synthesis via plasma-assisted conversion of methane and nitrogen
Hydrogen cyanide (HCN) is synthesized from ammonia (NH 3 ) and methane (CH 4 ) at ~1200°C over a Pt catalyst. Ammonia synthesis entails several complex, highly emitting processes. Plasma-assisted HCN synthesis directly from CH 4 and nitrogen (N 2 ) could be pivotal for on-demand HCN production. Here, we evaluate the potential of dielectric barrier discharge (DBD) N 2 /CH 4 plasma for decentralized catalyst-free selective HCN production. We demonstrate a single-step conversion of methane and nitrogen to HCN with a 72% yield at <300°C. HCN is favored at low CH 4 concentrations with ethane (C 2 H 6 ) as the secondary product. We propose a first-principles microkinetic model with few electron impact reactions. The model accurately predicts primary product yields and elucidates that methyl radical (·CH 3 ) is a common intermediate in HCN and C 2 H 6 synthesis. Compared to current industrial processes, N 2 /CH 4 DBD plasma can achieve minimal CO 2 emissions.
The magnetic gradient scale length explains why certain plasmas require close external magnetic coils
Abstract The separation between the last closed flux surface of a plasma and the external coils that magnetically confine it is a limiting factor in the construction of fusion-capable plasma devices. This plasma-coil separation must be large enough so that components such as a breeding blanket and neutron shielding can fit between the plasma and the coils. Plasma-coil separation affects reactor size, engineering complexity, and particle loss due to field ripple. For some plasmas it can be difficult to produce the desired flux surface shaping with distant coils, and for other plasmas it is infeasible altogether. Here, we seek to understand the underlying physics that limits plasma-coil separation and explain why some configurations require close external coils. In this paper, we explore the hypothesis that the limiting plasma-coil separation is set by the shortest scale length of the magnetic field as expressed by the ∇ B tensor. We tested this hypothesis on a database of > 40 stellarator and tokamak configurations. Within this database, the coil-to-plasma distance compared to the minor radius varies by over an order of magnitude. The magnetic scale length is well correlated to the coil-to-plasma distance of actual coil designs generated using the REGCOIL method (Landreman 2017 Nucl. Fusion 57 046003). Additionally, this correlation reveals a general trend that larger plasma-coil separation is possible with a small number of field periods.
Understanding plasma turbulence through exact coherent structures
Plasma turbulence is a key challenge in understanding transport phenomena in magnetically confined plasmas. This work presents a generalized framework to analyze plasma turbulence that utilizes periodic orbit theory. In periodic orbit theory, doubly periodic solutions (coherent structures) of the governing equation(s) serve as building blocks of the considered turbulent dynamics. To illustrate the concept and method, the particularly simple Kuramoto–Sivashinsky (referred to here as LMRT for the original authors: LaQuey, Mahajan, Rutherford, and Tang) trapped-ion mode toy model is used. By applying numerical optimization techniques to the LMRT equation, we extract coherent spacetime patterns that represent the library of allowable fundamental structures of the equation. These structures provide a framework to systematically describe turbulence as a composition of recurrent solutions, revealing an underlying order within chaotic plasma motion. Although illustrated here using the simplified LMRT model for clarity, this framework provides a general strategy that can be extended to more complex and realistic models of plasma turbulence, including gyrokinetic systems. This offers a new method for predicting and potentially controlling transport processes in fusion plasmas by providing a bridge between nonlinear dynamical systems theory and plasma physics in the form of a generalized framework with which to analyze and understand spatially extended nonlinear partial differential equations.
FENIX: An Open-Source Multiphysics Integrated Framework Enabling Collaborative Development of Plasma Facing Component Modeling Capabilities
Advanced modeling and simulation tools have a crucial role to play in accelerating fusion energy deployment as a sustainable power source. Multiphysics, high-fidelity computational tools can help understand, model, and quantify the complex interactions between materials performance, plasma and neutron exposure, and engineering processes. As a result, they accelerate the design, safety analysis, and performance evaluation of fusion systems. This webinar introduces the Fusion ENergy Integrated multiphys-X (FENIX) framework, an open-source multiphysics tool for plasma facing component modeling. FENIX leverages the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has been developed by the United States Department of Energy Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. FENIX couples various MOOSE capabilities such as heat transfer, thermomechanics, thermal hydraulics, electromagnetics, and plasma kinetics with the MOOSE-based applications Cardinal (neutronics) and TMAP8 (tritium transport). During the webinar, we will present FENIX and discuss how its modularity, openness, software quality assurance processes, and licensing approach supports effective collaborations, including public-private partnerships.
Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models
One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.
Quantification of the effect of uncertainty on impurity migration in PISCES-A simulated with GITR
A Bayesian inference strategy has been used to estimate uncertain inputs to global impurity transport code (GITR) modeling predictions of tungsten erosion and migration in the linear plasma device, PISCES-A. This allows quantification of GITR output uncertainty based on the uncertainties in measured PISCES-A plasma electron density and temperature profiles (n e , T e ) used as inputs to GITR. The technique has been applied for comparison to dedicated experiments performed for high (4 × 10 22 m –2 s –1 ) and low (5 × 10 21 m –2 s –1 ) flux 250 eV He–plasma exposed tungsten (W) targets designed to assess the net and gross erosion of tungsten, and corresponding W impurity transport. The W target design and orientation, impurity collector, and diagnostics, have been designed to eliminate complexities associated with tokamak divertor plasma exposures (inclined target, mixed plasma species, re-erosion, etc) to benchmark results against the trace impurity transport model simulated by GITR. The simulated results of the erosion, migration, and re-deposition of W during the experiment from the GITR code coupled to materials response models are presented. Specifically, the modeled and experimental W I emission spectroscopy data for a 429.4 nm line and net erosion through the target and collector mass difference measurements are compared. Furthermore, the methodology provides predictions of observable quantities of interest with quantified uncertainty, allowing estimation of moments, together with the sensitivities to plasma temperature and density.