SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
For consideration for diagnostic and technology development for a deuterium and tritium (DT) fusion system, we briefly list here the available DT fusion systems, their characteristic information, such as yields, flux, the pulse width, rep rate and approximate spectrum. We also briefly list other neutron sources for comparison. This is not meant to be exhaustive but a sampling of recent and available sources.
The final optic in future inertial fusion energy plants must maintain high performance and survivability under gigashot laser irradiation while exposed to fusion byproducts. Although no practical solution has been demonstrated, a self-healing final turning mirror based on a grazing-incidence liquid metal mirror has been theoretically explored. This work investigates the suitability of a gallium alloy. The experimental system simulates key conditions, including vacuum exposure and 10-Hz, 355-nm, and 6-ns laser pulses. Experiments address reflectivity, damage, and response as a function of pressure. Results highlight the potential of liquid metal gallium alloys for robust final optics in laser-based fusion power plants and inform non-traditional optics for intense laser systems.
With the continued expansion of the private fusion sector, many governments are increasing support for public-private partnerships (PPPs) and positioning these partnerships as central to their plans to deliver commercial fusion. This paper discusses a groundbreaking first-of-its-kind PPP between the United Kingdom–based private company, Tokamak Energy Ltd. and the U.S. public sector researchers from the Princeton Plasma Physics Laboratory, Oak Ridge National Laboratory, and the University of Washington that focused on advancing spherical tokamak physics on the ST40, a high-field spherical tokamak owned and operated by Tokamak Energy. The collaboration was both scientifically productive and supportive of Tokamak Energy in achieving a key company milestone. Key factors that made this a productive and impactful collaboration are outlined from the perspectives of both the public and private participants. These principles can be used to inform future PPPs in fusion, and have already paved the way for new public-private initiatives.
This project addressed a key barrier to advanced fusion and nuclear simulation: the difficulty of performing high-fidelity Monte Carlo neutronics directly on complex, real-world CAD geometry. Traditional workflows require engineers to rebuild CAD models as simplified constructive solid geometry, a time-consuming and error-prone process that limits design iteration and broader adoption of simulation tools. The goal of this Phase I SBIR was to make CAD-based neutronics practical, accessible, and robust for industrial and research users. During the project, Coreform significantly enhanced the Direct Accelerated Geometry Monte Carlo (DAGMC) workflow and fully integrated it into Coreform Cubit as a first-class capability. Major achievements include optimized material assignment and surface meshing workflows, substantial performance improvements to geometry imprinting and preparation, native export of DAGMC models, and new visualization tools to support OpenMC source definition and lost-particle debugging. Coreform also expanded Cubit’s capabilities as a full OpenMC preprocessor, including the ability to convert OpenMC constructive solid geometry models back into CAD for visualization, multiphysics coupling, and debugging. In collaboration with Argonne National Laboratory, the project delivered comprehensive new DAGMC documentation and training materials, transforming DAGMC from a research-oriented tool into a production-ready workflow. Results were disseminated through tutorials, conference training, and multiple well-attended webinars demonstrating integrated CAD-based neutronics and multiphysics workflows. Overall, this project demonstrated that high-fidelity Monte Carlo simulations can be performed directly on complex CAD geometry, reducing setup time, improving usability, and enabling faster, more informed design decisions for fusion and nuclear energy systems.
In this work, we use Jensen–Shannon complexity and permutation entropy to analyze the magnetic field fluctuations of an astrophysically scaled plasma experiment. The experiment was intended to emulate an interplanetary coronal mass ejection event in the lab, recreating the major sections seen in satellite data. We also use a technique called “delay,” in which we use select elements, skipping one or more data points at a time, in our time series data to obtain Jensen–Shannon complexity as a function of frequency and investigate the frequency of maximized complexity. We then compare the delay frequencies to other frequencies in the plasma. We found that the frequencies for maximum complexity do not correspond to the frequencies investigated, implying that other physical mechanisms lead to an increase in complexity at these frequencies.
Lawrence Livermore National Laboratory (LLNL) is proposing a new Laser Driven Fusion Integration Research and Science Test Facility (LD-FIRST) with the goal of providing an experimental testbed for future Inertial Fusion Energy (IFE) systems. However, IFE systems require detailed and accurate multiphysics modeling to quantify material damage, thermal loading, and tritium breeding within complex chamber environments. This article presents the first step in an integrated multiphysics framework that couples meshed CAD-based geometry within Monte Carlo neutronic simulations to enable high-fidelity analysis of IFE chamber concepts, with future coupling to external codes. The neutronics workflow utilizes OpenMC and its third-party capability to use CAD-based geometries through DAGMC and tally on unstructured meshes with Libmesh to evaluate neutron transport behavior, geometric fidelity, and material performance under reactor-relevant conditions. The use of tailored tallies on unstructured meshes in this framework allows direct transfer without interpolating to CFD simulation tools. Two IFE chambers were evaluated, both conceived by LLNL: HYLIFE-II and Laser IFE (LIFE). This work produced high-fidelity conformal surface and volumetric meshes of the HYLIFE-II and LIFE chambers with mapped spatial insight into material damage, thermal loading, and tritium breeding. The HYLIFE-II model was built utilizing available resources and used as a test case to verify that the neutronics framework can handle complex geometries. The LIFE chamber CAD was provided by LLNL and was the main focus of this work. This work analyzes multiple ternary alloy breeding materials for the LIFE chamber, across different 6 Li enrichments to produce data relevant to the LD-FIRST project. This work also investigates the level of model fidelity for the LIFE chamber, and results show that inclusion of detailed first wall and coolant structures increased the predicted tritium breeding ratio (TBR) by ~30%, highlighting the sensitivity of tritium breeding and the need for a high-fidelity simulation framework for IFE chambers. These developments provide a scalable toolset for the design and optimization of next-generation IFE chambers, forming a solid foundation for future coupled multiphysics analysis.
Accomplishing a task with telerobotics typically involves a combination of operator control/supervision and a 'script' of preprogrammed commands. These commands usually assume that the location of various objects in the task space conform to some internal representation (database) of that task space. The ability to quickly and accurately verify the task environment against the internal database would improve the robustness of these preprogrammed commands. In addition, the on-line initialization and maintenance of a task space database is difficult for operators using Cartesian coordinates alone. This paper describes the Interactive Scene' Analysis Module (ISAM) developed to provide taskspace database initialization and verification utilizing 3-D graphic overlay modelling, video imaging, and laser radar based range imaging. Through the fusion of taskspace database information and image sensor data, a verifiable taskspace model is generated providing location and orientation data for objects in a task space. This paper also describes applications of the ISAM in the Intelligent Systems Research Laboratory (ISRL) at NASA Langley Research Center, and discusses its performance relative to representation accuracy and operator interface efficiency.
Experimentally measuring the magnetic-field vector topology in low-cost fusion-concept devices will provide critical information required to optimize and accelerate the development of those concepts for economical fusion energy. The magnetic-field measurement techniques most commonly used in modern tokamaks, i.e., beam-emission spectroscopy and optical emission spectroscopy, are not suitable below 5000 Gauss due to a lack of sensitivity. To diagnose magnetic field relevant to some of the ARPA-E-supported fusion concepts (50 to 1000 Gauss), a novel approach is required. Doppler-free saturation spectroscopy (DFSS) is a pump/probe laser-based technique and has been successfully demonstrated in the laboratory to provide a magnetic-field measurement accuracy of <10 Gauss. DFSS is implemented to obtain ultra-high-resolution spectra at a localized point in space. The spectral data is then fit to the Schrödinger equation for determination of the magnetic-field vector. To obtain the magnetic-field topology, the measurement spatial location is scanned over the desired region of space by steering the DFSS pump laser beam. The goal of this project is to assemble and commission a portable DFSS diagnostic capable of 2-D magnetic-field vector measurements utilizing the Hα spectral line profile.
The climate is changing so rapidly that climatologies based on historical statistics cannot reliably capture the current risk of extreme weather events hazardous to society. Decision relevant projections of weather extreme probability over the next 10–15 years are needed to enable adaptation and resilience in the face of this evolving risk. Current weather forecasts/predictions and long-term climate projections for decades into the future are inadequate for providing this information to stakeholders that need it, targeting forecast horizons either too short or too far into the future. We argue that a new approach is needed: climate nowcasting. Climate nowcasting would focus on user-inspired extreme metrics over the next 10–15 year time frame, targeting specific impacts and locations down to a local scale, by engaging with stakeholders to understand their needs and provide information in a format relevant for decision making. Importantly, climate nowcasting will not consist of a single approach or data set, involving rather data fusion from different sources of information: simulations, observations and data driven methods, likely through different weights depending on the metric of interest. Predictions must be accompanied by serious engagement with stakeholders facing climate risks, and clearly present uncertainties and limitations of any prediction. Such a vision is very different from how typical climate or weather forecasts are applied today.
Radio Frequency (RF) signals are found throughout our world, carrying over-the-air information for both digital and analog uses with applications ranging from WiFi to the radio. One area of focus in RF signal analysis is determining the modulation schemes employed in these signals which is crucial in many RF signal processing domains from secure communication to spectrum monitoring. This work investigates the accuracy and noise robustness of novel Topological Data Analysis (TDA) and dynamic representation based approaches paired with a small convolution neural network for RF signal modulation classification with a comparison to state-of-the-art deep neural network approaches. We show that using TDA tools, like Vietoris-Rips and lower star filtrations, and the Takens' embedding in conjunction with a standard shallow neural network we can capture the intrinsic dynamical, geometric, and topological features of the underlying signal's manifold, offering informative representations of the RF signals. Our approach is effective in handling the modulation classification task and is notably noise robust, outperforming the commonly used deep neural network approaches in mode classification. Moreover, our fusion of dynamical and topological information is able to attain similar performance to deep neural network architectures with significantly smaller training datasets.
Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.
Explore the source record for details and available documents.
Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor. IIS reduce the need for camera imaging, image processing, and LIDAR use and point cloud processing. We show that IIS, when combined with traditional sensors, results in more accurate perception and localization outcomes and a reduced AV compute load.
Outline: Describe principle of magnetically-confined nuclear fusion; Explain the role of Poincaré maps in fusion reactor design; Describe a new method for rapidly computing Poincaré maps using physics-informed machine learning
X-ray radiography is a technique frequently used to diagnose convergent high-energy-density (HED) systems, such as inertial confinement fusion implosion, and provides unique information that is not available through self-emission measurements. In this work, we investigate the scope and limits of that information using a radiography simulation combined with the Bayesian inference workflow. The accuracy of density reconstruction from simulated radiographs of spherical implosions driven with 27 kJ laser energy is assessed, including the increase or decrease in accuracy due to the addition of Lagrangian marker layers, Poisson noise, and improved prior information. This work is the first to present the full uncertainty distributions inferred from radiography analysis in HED systems and demonstrates the importance of constructing the full posterior probability density, as opposed to a point estimate, due to the modal structure of the likelihood surface introduced by typical experimental noise sources. This general methodology can be used both for robust analysis of radiographic data and for an improved design of radiography experiments by modeling the full experimental system.
Over the past several decades, the United States (U.S.) and other countries have done important research and development in advanced nuclear energy technologies, both fission and fusion. The urgency of our current climate, energy, and security needs have prompted a concerted effort by innovators, investors, and the public sector to transition from research and development (R&D) to demonstration and commercialization—to cross the proverbial “valley of death.” The stakes are high: humanity needs clean energy options, and the failure of any promising alternative will lower our odds of success. The process of moving an innovation into use has often been described in phases that begin with early research and end with deployment at scale. Between those two phases lies an area often referred to as the “valley of death” because it can require a long period of time, large investments, and often includes significant challenges related to regulation, standardization, and entering markets already structured around incumbent technologies. What can we learn from recent experience with fission to inform our path forward with fusion? At least five key lessons rise to the top: 1) Set bold targets; 2) Empower the private sector; 3) Engage with regulators and the public; 4) Address costs and markets; and 5) Deliver results. Here, this editorial will consider each of these in turn.
Neutrons generated in Inertial Confinement Fusion (ICF) experiments provide valuable information to interpret the conditions reached in the plasma. The neutron time-of-flight (nToF) technique is well suited for measuring the neutron energy spectrum due to the short time (100 ps) over which neutrons are typically emitted in ICF experiments. By locating detectors 10s of meters from the source, the neutron energy spectrum can be measured to high precision. Here, we present a contextual review of the current state of the art in nToF detectors at ICF facilities in the United States, outlining the physics that can be measured, the detector technologies currently deployed and analysis techniques used.
This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).