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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Post-DTL Beam Delivery

Ensuring that the beam delivered from the upgraded Front-End (FE) meets the Key Performance Parameters (KPPs) at each user facility is critical to the success of the LANSCE Accelerator Modernization Project (LAMP). For a high-intensity, multi-user facility like LANSCE, compliance with beam loss and radiation thresholds is as important as the charge delivered to each target. While early LAMPF/LANSCE operations relied on iterative tuning to minimize losses from beam halo and tail particles, the new FE may introduce different beam distributions and loss modes—making predictive modeling essential. To manage this, the F2E (Front-End to End) effort is developing detailed particle-tracking models that reflect realistic beamline conditions, including halo formation and expected diagnostic readings. These "snapshot" simulations aim to benchmark live machine performance at a given moment. This will help quantify how beam quality from the new FE will propagate downstream through the facility. Only by validating these models can we confidently assess and mitigate the potential impacts of the LAMP FE on beam delivery. Post-DTL, the beam splits to serve five major user facilities. Historically, low-energy beam transport has been modeled using TRACE, and higher-energy sections with TRANSPORT. These have now been unified into MAD-X format and validated with codes such as Elegant, pyOrbit, XSuite, Impact-Z, and HPSim. The primary focus now is on accurate modeling of full particle distributions (including beam halo) as they traverse the accelerator and beamlines to each experimental station. All models are at various stages of validation with empirical data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Precision beam diagnostics at the NuMI facility using muon monitor observations

The Neutrinos at the Main Injector (NuMI) facility at Fermilab delivers an intense neutrino beam for multiple experiments by producing pions that decay into neutrinos, muons, and other particles. Magnetic horns—the primary pion focusing elements in the NuMI beamline—exhibit predominantly linear optics, enabling a predictable relationship between the proton beam and the resulting pion and muon phase spaces. This study has two primary objectives: first, to evaluate and confirm the linearity of the horn focusing mechanism using analytical models and numerical simulations; and second, to demonstrate that key beam parameters—such as proton beam intensity, beam position on target, and horn current—can be extracted from muon monitor observations within this linear optics framework. Using a machine learning model trained on spill-by-spill muon monitor data, we infer the horn current with a precision of ±0.05%, the beam intensity with ±0.1%, and the beam position on target with ±0.018⁢ mm horizontally and ±0.013⁢ mm vertically. This approach provides a reliable cross-check of beam parameters, helping to reduce systematic uncertainties that are critical for future experiments such as the Deep Underground Neutrino Experiment, which will rely on the neutrino beam produced by the Long-Baseline Neutrino Facility.

Beam control↗
DOE OSTI2024

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evidence for electron energization accompanying spontaneous formation of ion acceleration regions in expanding plasmas

We report experiments conducted in an expanding argon plasma generated in the inductive mode of a helicon source in the Hot hELIcon eXperiment–Large Experiment on Instabilities and Anisotropies facility. As the neutral gas pressure increases, the supersonic ion acceleration weakens. Increasing neutral pressure also alters the radial profile of electron temperature, density, and plasma potential upstream of the plasma expansion region. Langmuir probe measurements of the electron energy probability function (EEPF) show that heating of electrons at the plasma edge by RF fields diminishes with increasing gas pressure, yielding a plasma with a centrally peaked electron temperature, and flat potential profiles at higher neutral pressures. For neutral pressures at which ion acceleration regions develop in the expanding plasma plume, EEPFs reveal electrons with two temperature components.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Characterizing the Mechanical Response of the Saturn Accelerator

The Saturn Particle Accelerator is a hot X-ray source used by the NNSA for creating conditions similar to that of a nuclear weapon. When Saturn fires some of it's energy is released in the form of mechanical shock and vibration. This mechanical output has not been characterized or understood, making design of components and diagnostics more difficult. Thus it will be helpful to understand this mechanical shock. This poster presents the beginnings of a project to do just that.

Cuneo, Nicolas Francis [Sandia National Laboratori↗
DOE OSTI2024

Source size of x rays from self-modulated laser wakefield accelerators

A comparative study of x-ray sources generated with different mechanisms from self-modulated laser wakefield acceleration (SM-LWFA) electrons was performed to compare the source size or spatial resolution for use in high energy density science applications. We examine the source size of betatron, inverse Compton scattering, and bremsstrahlung radiation with a Fresnel diffraction based formalism and a modified x-ray ray tracing model. We observe the dependence of source size on the radiation generation process, laser parameters, and compare to what is possible in other regimes of LWFA, as well as current methods. This information is significant as we begin to explore the use of light sources driven by SM-LWFA for use as a diagnostic at large-scale laser facilities where blowout regime LWFA is not possible.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Systematic feature design for cycle life prediction of lithium-ion batteries during formation

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid-electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature-design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. By only using two simple Q (V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, we achieved an average of 9.87% error for cycle life prediction. Here, the physics-based investigation guided by the two designed features shows that the voltage ranges identified by our framework capture the effects of formation temperature and microscopic-particle resistance heterogeneity. By designing highly predictive, robust, and interpretable features, our approach can accelerate industrial battery formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.

25 ENERGY STORAGE↗
DOE OSTI2021

Analytic solutions for Asay foil trajectories with implications for ejecta source models and mass measurements

We consider the trajectory of an Asay foil ejecta diagnostic for scenarios where ejecta are produced at a singly shocked planar surface and fly ballistically through a perfect vacuum to the sensor. We do so by building upon a previously established mathematical framework derived for the analytic study of stationary sensors. First, we derive the momentum conservation equation for the problem, in a form amenable to accelerating sensors, in terms of a generic ejecta source model. The result is an integrodifferential equation of motion for the foil trajectory. This equation yields an easily calculable closed-form implicit solution for the foil trajectory in instant-production scenarios. From there, we derive a boundary condition that particle velocity distributions must satisfy if their associated foil trajectories are to exhibit a smooth initial acceleration, as occurs in some experiments. This condition is identical to one derived previously from a consideration of piezoelectric voltage data obtained in similar experiments. We also compare techniques for inferring accumulated ejecta masses from foil trajectories, first by deriving the exact solution, and then by quantifying the error imposed by a frequently used approximate solution (both subject to the assumption of instantaneous ejecta production). Finally, we examine the common practice of presenting inferred cumulative ejecta masses as a function of implied ejecta velocity, establishing the conditions under which this methodology is most meaningful.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Experimental and theoretical comparison of ion properties from nanosecond laser-produced plasmas of metal targets

The ion emission properties of laser-produced plasmas as a function of laser intensities between 4–50 GW cm –2 and varying angles with respect to the target normal were investigated. The plasmas were produced by focusing 1064 nm, 6 ns pulses from an Nd:YAG laser on various metal targets. The targets used for this study include Ti, Mo, and Gd (Z = 22, 42, 64). It is noted that all ion profiles are composed of multiple peaks—a prompt emission peak trailed by three ion peaks (ultrafast, fast, and thermal). Experimentally, it is shown that each of these ion peaks follows a unique trend as a function of laser intensity, angle, and distance away from the target. Theoretically, it is shown that simple analytical models can be used to explain the properties of the ions. The variations in the ion velocity and density as a function of laser intensity are found to be in good agreement with theoretical models of sheath acceleration, isothermal self-similar expansion, and ablative plasma flow for various ion peaks.

42 ENGINEERING↗
DOE OSTI2025

Neutron-Friendly Li-Ion Battery Coin Cell for In Situ 3D Visualization of Li Plating

Advanced battery characterization using in situ/operando neutron imaging is critical for uncovering degradation modes such as lithium (Li) plating in Li-ion batteries (LIBs). However, conventional LIBs hinder operando neutron radiography (NR) and in situ neutron micro-computed tomography (N-μCT) for visualizing Li plating near the graphite-separator interface due to strong attenuation from hydrogen-rich components like PP–PE–PP separators, electrolyte, and Fe-based spacers. In this work, we designed and tested a neutron-friendly battery (NFB) optimized for in situ Li detection during extreme fast charging (XFC). Guided by neutron attenuation cross-sections and material transmission, the NFB enables clear visualization at the graphite–separator interface, which is typically opaque in standard LIBs. Electrochemical tests show the NFB exhibits voltage/current responses like standard cells for up to 50 XFC cycles. However, its lower reversibility and capacity are likely due to Cu-coated Al spacer degradation from delamination or corrosion. We propose titanium spacers as a more stable alternative, albeit requiring custom machining. Using this optimized cell, we achieved simultaneous neutron tomography of multiple cells, capturing in situ 3D images of dead Li accumulation, particularly near graphite edges. These heterogeneous deposits and disconnected Li clusters suggest localized current density hotspots during XFC.

25 ENERGY STORAGE↗
DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗
DOE OSTI2022

The MTA primary beamline description and performance [Slides]

The Muon Test Area (2003) was initially constructed to develop, test, and verify muon ionization apparatus using the 400-MeV proton beam from the Fermilab Linac. Since muon facilities involve the capture, collection and cooling of ~10 13 muons at a repetition conclusive tests require ~10 13 protons/pulse (full Linac beam): the original concept/request was for a full Linac capability (~10 14 p/sec). The beamline designed for MTA experiments, however, includes specialized insertions for linac beam diagnostics and beam measurements, greatly enhancing the functionality and complexity of the beamline. Installation of the beamline was complete fall, 2008. It represents the only facility in which experiments have direct access to a primary beam.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Evidence of electron microbunching in laser-driven modulated downramp injection and prospects for beam-driven implementation

Plasma accelerators can generate high-energy, high-brightness electron beams over centimeter-scale distances, offering novel pathways to compact x-ray free-electron lasers. Generating beams pre-bunched at the desired radiation wavelength would significantly enhance longitudinal coherence and reduce saturation length. Plasma density-modulated downramp injection offers an in-situ way to generate such beams with nanometer-scale bunching. Here we report the first experimental evidence of this mechanism in a laser-driven wakefield accelerator, showing that modulated density downramps generate modulated electron energy spectra absent in unmodulated cases. Particle-in-cell simulations reproduce these observations and reveal bunching factors of 0.05 at 0.4 μm, with a compression factor of approximately 7. Building on this demonstration, we propose a beam-driven implementation for FACET-II to generate multi-GeV beams pre-bunched at hundreds of nanometers wavelength, with sub-micrometer emittance, kiloampere peak current, and sub-percent slice energy spread. Two-stage magnetic compression enables tunable bunching from optical to extreme ultraviolet wavelengths while achieving peak currents exceeding 100 kA. Coherent transition radiation calculations confirm diagnostic feasibility. This approach offers a promising path towards compact, high-energy pre-bunched electron sources for advanced photon science applications.

electron microbunching↗
DOE OSTI2022

Purification and Concentration of 99 Mo from a UREX raffinate

The medical isotope 99m Tc ( t ½ = 6.0 h) accounts for over 80% of isotopes used in diagnostic nuclear medicine today and is normally derived from its transient equilibrium parent 99 Mo ( t ½ = 66 h). To reduce U.S. dependence on 99 Mo derived from HEU by foreign suppliers, the NNSA/M3 program—under the American Medical Isotope Production Act—has been tasked with facilitating the work of domestic 99 Mo suppliers that do not utilize HEU. Superconducting electron linear accelerators with high-Z converter targets can generate bremsstrahlung photons and neutron fluxes that can induce photonuclear reactions and LEU fission. After sufficient production intervals, targets can be rotated out and processed while another batch is irradiated. The process flow chemistry is shown in Figure 1. The irradiated U 3 O 8 targets are retrieved and dissolved in HNO 3 ; the volatile fission products are expelled and captured during this step. The HNO 3 liquor bearing the uranyl, fission and activation products is injected into the UREX suite of liquid-liquid extraction banks. The uranyl is partitioned in typical fashion using tri-$\textit{n}$-butyl phosphate (TBP) in a hydrocarbon diluent. The raffinate—comprising mainly 99 Mo and fission products—is then injected into the MoLLE (Molybdenum Liquid-Liquid Extraction) flowsheet, where the Mo is selectively extracted by an organophosphorous acid extractant such as di-(2-ethylhexyl phosphoric acid) (HDEHP). Trace amounts of Nb, Zr, Np, I, and Te are co-extracted. The Mo is stripped using acetohydroxamic acid (AHA) and fed onto an anion exchange column. Following a series of hydroxide, HCl, and oxalic acid wash steps, the Mo can be recovered in NaOH/NaCl. A manuscript describing this process chemistry in more detail was recently published.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Power Module Precursors and Prognostics

This project focuses on finding and utilizing electrical properties (precursors) of power electronic devices and modules whose changes have strong correlations with different modes and levels of degradations in order to realize online and lightweight state of health evaluation and fault diagnosis. Results from this project will contribute to higher safety, reliability, and power density of power electronic components. The approaches mainly include reviewing academic literature and industry practices for state of the art precursor identification and extraction, accelerated life cycles (ALC) of various power modules to create different degradations in-house, and developing techniques for gate-driver-integratable precursor detection circuit. In FY24, the results includes two sets of definitively detected aging precursors (on-state resistance and gate threshold voltage) for two sets of power modules respectively, controlled accelerated lifetime cycling of multiple silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) modules, and the development and validation of a gate voltage detection system.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Nuclear Excitation and Fission Studies with Short Pulsed Laser-Driven High Energy Gamma Rays

We propose the development of a platform to study photon excitation of high Z nuclei utilizing a high energy (70 J) short pulsed (500 fs) laser to drive > 5 MeV γ-radiation. The laser beam will be focused onto a specially prepared foam or corrugated conveying target, where it generates relativistic MeV electrons via ponderomotive and wake-field acceleration mechanisms. γ-emission is produced in the form of bremsstrahlung in the same target or in a separated high-Z γ-production target hit by the accelerated MeV electrons. The high energy γ-rays will be utilized to provide nuclear state excitation in the short time domain. The γ platform will allow to investigate photo fission of high Z target material positioned in an auxiliary target chamber in the γ-ray beam path via the established gas transport scheme. The photo fission yield will provide furthermore diagnostic information on the produced γ-yield. In addition, γ-spectroscopy will be used to identify γ-lines for the diagnostic of the excited state population of high Z target nuclei. This research capability is relevant to the understanding of how nuclear excitation effects fission, in relevance to recent astrophysical observations on element nucleosynthesis. These processes involve neutron-rich fragments and processes such as fission recycling (e. g. in neutron stars outer crusts).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Variational neural network approach to QFT in the field basis

We present a variational neural network approach for solving quantum field theories in the field basis, focusing on the free Klein-Gordon model formulated in momentum space. While recent studies have explored neural-network-based variational methods for scalar field theory in position space, a systematic benchmark of the analytically solvable Klein-Gordon ground state—particularly in the momentum-space field basis—has been lacking. In this work, we represent the ground-state wavefunctional as a neural network defined on a discretized set of field configurations and train it by minimizing the Hamiltonian expectation value. This framework enables direct comparison to exact analytic results for a range of key observables, including the ground-state energy, two-point correlators, expectation value of the field, and the structure of the learned wavefunctional itself. Our results provide quantitative diagnostics of accuracy and establish a validated foundation for extending neural-network wavefunctional methods to interacting field theories and position-space formulations.

Klein-Gordon model↗