Search NASA⌕ Search

SEARCH · Search NASA

Results for “LASER OUTPUT”

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.

70 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-speed fiber-coupled terahertz time-domain spectroscopy system using a bias-free plasmonic source

We demonstrate a high-speed fiber-coupled terahertz time-domain spectroscopy (THz-TDS) system using a bias-free plasmonic terahertz source. The system is powered by a compact, low-power femtosecond fiber laser oscillator that drives a dual-branch fiber amplifier to pump and probe a plasmonic terahertz source and detector, respectively. A polarization-maintaining, fiber-coupled optical delay line is employed to precisely control the temporal offset between the pump and probe beams through a compact, robust platform. Operating in the self-similar amplification regime, the fiber amplifiers are engineered to mitigate nonlinear pulse distortion and dispersion while delivering robust, high-energy pulses at a 1560-nm wavelength, with an output power exceeding 326 mW. In reflection mode, the THz-TDS system achieves a peak dynamic range of 81 dB and a bandwidth greater than 3.5 THz at a scan rate of 3.75 Hz. The utility of the system for rapid sensing is further demonstrated by quantifying the porosity and density of pharmaceutical tablets through measurements of their physical thickness and effective refractive index.

Jiang, Xinghe [University of California, Los Angel↗
DOE OSTI2026

Nonlinear post-compression to sub-20 fs using a single-stage multipass cell filled with ambient air

Noble gas-filled multipass cells have proven to be very effective in compressing high-energy, hundreds-of-femtoseconds pulses down to tens of femtoseconds. Molecular gases can be an attractive alternative to noble gases since they provide additional Raman nonlinearities that can be much stronger than the electronic Kerr nonlinearity and can therefore enable more spectral broadening and shorter compressed pulses. Air at atmospheric pressure offers molecular gases in their simplest format, with both easy access and no costs for implementing a gas chamber. Here we demonstrate a single-stage, air-filled multipass cell that spectrally broadens 145- μ J, 185-fs pulses with >90% throughput, with a small fraction of the output compressed to sub-20 fs using a prism-pair compressor. The multipass cell uses standard broadband mirrors without dispersion engineering and ambient air as the nonlinear medium, making it the simplest and most cost-effective solution for generating few-cycle femtosecond pulses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Intrinsic optical bistability of photon avalanching nanocrystals

Optically bistable materials respond to a single input with two possible optical outputs, contingent on excitation history. Such materials would be ideal for optical switching and memory, but the limited understanding of intrinsic optical bistability (IOB) prevents the development of nanoscale IOB materials suitable for devices. Here we demonstrate IOB in Nd 3+ -doped KPb 2 Cl 5 avalanching nanoparticles, which switch with high contrast between luminescent and non-luminescent states, with hysteresis characteristic of bistability. Here we elucidate a non-thermal mechanism in which IOB originates from suppressed non-radiative relaxation in Nd 3+ ions and from the positive feedback of photon avalanching, resulting in extreme, >200th-order optical nonlinearities. The modulation of laser pulsing tunes the hysteresis widths, and dual-laser excitation enables transistor-like optical switching. This control over nanoscale IOB establishes avalanching nanoparticles for photonic devices in which light is used to manipulate light.

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

Yb:Lu 2 O 3 single-crystal fiber: spectroscopy, amplification, and lasing

For the first time, to our knowledge, a lutetium oxide (Lu 2 O 3 ) single-crystal fiber (SCF) laser is demonstrated. The laser heated pedestal growth (LHPG) technique was used to pull Yb-doped Lu 2 O 3 SCFs between 10 and 50 mm long and with diameters between 150 and 225 μm. Spectroscopic properties are first reported in detail, as the two-site nature of the host demands careful attention. Short 10 mm long, unclad fibers were used as amplifier media in a single pass copropagating configuration. Then, a 50 mm long 0.1%Yb:Lu 2 O 3 SCF with a 180 μm diameter was configured to lase by butt-coupling mirrors on the ends and pumping at 976 nm. Lasing occurred at the 1033 nm peak of Yb, and a maximum output of around 300 mW is reported. Finally, the results indicate there is no, at least obvious, fundamental reason that should deter future interest in Lu 2 O 3 as a SCF platform, which has been considered to have high potential for power scaling based on its beneficial intrinsic properties.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Fully plasma-based electron injector for a linear collider or XFEL

We demonstrate through high-fidelity particle-in-cell (PIC) simulations a simple approach for efficiently generating 20 + GeV electron beams with the necessary charge, energy spread, and emittance for use as an injector in a future linear collider or a next generation XFEL. A high quality injected bunch is generated by self-focusing an unmatched electron driver in a nonlinear plasma wakefield. Over pump depletion distances, the drive beam dynamics and self-loading effects lead to high energy, low-energy spread output beams. For plasma densities of 10 18 c⁢m −3 , PIC simulation results indicate that self-injected beams with 0.52 n⁢C charge can be accelerated to 20 GeV with projected core energy spreads of ≲ 1%, normalized slice emittances of 110n⁢m, peak normalized brightness of ≳ 10 19 A/m 2 /rad 2 , and transfer efficiencies of ≳ 44%.

Particle acceleration in plasmas↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ElementLIBS User's Guide: An operational aid for use and development

Laser-Induced Breakdown Spectroscopy or LIBS is a rapid, in-situ analytical technique where a laser of known energy is pulsed at the surface of an analyte. The laser pulse rapidly heats a localized area to many thousand degrees Kelvin, ablating part of the analyte and turning it into a plasma. As the plasma cools, excited atoms return to a ground state with known emission energies. This emitted energy is captured by various spectrometers and provides a spectrum of the emitted energies and intensities. This spectrum can be analyzed to provide the elemental composition of a sample by using known emission lines and relative abundance. ElementLIBS was developed for the SciAps hand-held LIBS model Z300 but will work with any model that provides a LIBS spectrum of similar resolution. The Z300 has an integrated resolution of 1/30 nm with a range of approximately 180nm – 960nm, providing an output spectrum of 23431 pixels across three spectrometers. These criteria are only provided as a reference, as the software was designed to work with any size spectrum, provided the input file is of the correct format and the models were developed using the same framework. If spectra of varying dimensions are used, the software will fail without warning and unusual events could occur.

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

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Spectrograph stabilization using a single-delay interferometer on the Hale Telescope

We describe a technique for spectrograph stabilization useful when conventional mitigation techniques of vacuum tanks, thermal insulation, and laser frequency comb may be impractical, expensive, heavy, or bulky. This includes spectrographs on airborne platforms or mounted on telescopes where they suffer a changing gravity vector or other drifts. Placing a fixed-delay interferometer in series with a spectrograph forms an externally dispersed interferometer (EDI). This produces a uniform sinusoidal comb multiplying input spectrum, creating (through heterodyning) beats (moiré patterns). In Fourier space for low frequencies up to the comb frequency, the moiré generated signal counter-rotates to ordinary spectra under an unknown disperser wavenumber drift Δx. This generates a large negative feedback signal useful in a conceptual control loop, to converge rapidly to a stable spectrum and yield Δx. A modified EDI data analysis algorithm (“crossfading”) combines frequency-weighted moiré with conventional spectrum to cancel net output spectrum reaction to Δx. Needing only a single-delay, this is a practical improvement over prior crossfading analyses requiring multiple delays. We test crossfading on ThAr data near 4850 cm−1 taken on Hale telescope in an earlier project. In a single pass, we reduce drift 20 times. Using seven iterations, we reduce 0.5 cm−1 (31 km/s Doppler equivalent) drift to 4×10−7 cm−1 (2.5 cm/s). The interferometer delay can wander, because linearity of phase versus wavenumber interpolates science features between bracketing calibrating spectral references. Second, mathematically reversing the heterodyning effect doubles effective spectral resolution without changing disperser slit.

Erskine, David J [Lawrence Livermore National Labo↗
DOE OSTI2025

Novel Concepts for High Gradient Acceleration (Final Technical Report)

We have conducted an intensive, pioneering program to demonstrate novel concepts for achieving high gradient acceleration at frequencies from the conventional microwave bands up to the millimeter wave /THz bands. High gradient accelerators hold the promise of smaller and less costly accelerators for applications ranging from the largest scale accelerators used for discovery science down to the smallest accelerators used for industrial, homeland security and medical applications. The research consisted of two major research thrusts: 1.) Structure-based wakefield accelerator (SWFA) research in collaboration with the Argonne Wakefield Accelerator research group and 2.) Millimeter Wave / THz high gradient acceleration in collaboration with SLAC. The specific goals of the research program were: Design novel metallic metamaterial structures that increase the beam-wave coupling for the accelerator mode and reduce the effect of high order modes; test novel metamaterial structures to achieve higher output power, > 1 GW at X-Band (11.7 GHz), in test at the Argonne Wakefield Accelerator (AWA); determine the experimental breakdown threshold for nanosecond-scale pulses at X-Band in testing at the AWA; test a 110 GHz accelerator structure with a field emission electron gun, built at SLAC, using pulses from a 1 MW, 110 GHz gyrotron; design, build and test a 110 GHz quasi-optical, resonant-ring pulse compressor to compress microsecond pulses from the 1 MW gyrotron into > 20 MW, 5 ns output pulses for accelerator structure testing. The proposed research program built on our successes in our research program including: Generation of 510 MW, 2.1 ns (FWHM) pulses at 11.7 GHz from a metallic metamaterial structure in test at the Argonne Wakefield Accelerator using a train of eight 65 MeV electron bunches spaced at 1.3 GHz with a total charge of 280 nC. The metamaterial structure consisted of 100 copper unit cells each consisting of a “wagon-wheel” plate and a spacer plate with a total structure length of 0.2 m. The 510 MW pulse generated an on-axis wakefield of 130 MV/m that could be used to accelerate a trailing witness bunch. Demonstration of coupling of an unprecedented rf power level of 575kW into a 110 GHz accelerator structure using a quasi-optical setup. The standing structure consisted of a central copper cavity located between two matching cavities fed by a TM01 mode. The 6 ns input pulses were sliced from 3 microsecond pulses from the gyrotron using a laser-driven silicon switch. We obtained an unprecedented high gradient up to 230MV/m corresponding to a peak surface electric field of more than 520 MV/m.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Spatiotemporal Automatic Calibration of Infrastructure Lidar, Radar, and Camera with a Global Navigation Satellite System

Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2024

Precision Polishing of Ablator Capsules via in situ Process Monitoring and Machine Learning–Based Optimization

In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing the hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High-density carbon capsules made for ICF experiments at the National Ignition Facility are precision polished to achieve surface smoothness on the order of a few nanometers as well as to minimize isolated defects in the form of pits. Given the critical role of this process, we are developing smart manufacturing techniques with the goal of elevating the efficiency of this process. Our approach is to use MEMS (micro-electromechanical systems)–based sensors to capture the fine vibration signals generated during the polishing process and combine them with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, in this work, we describe the multiple fronts we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used to ensure the progress of the run as well as a means for faster optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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↗