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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗
DOE OSTI2025

MAIA: A new detector concept for a 10 TeV muon collider

Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $\mu\mu$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which enables standalone track reconstruction for high-momentum muons. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background (BIB) simulations generated in FLUKA are overlaid with single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even with BIB, reconstruction efficiencies exceed 95% for energetic tracks, photons, and neutrons in the central region of the detector. This paper outlines promising avenues of future work, including forward region optimization and opportunities for enhanced flavor/boosted object tagging, and addresses the technological assumptions needed to achieve the desired detector performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Simulation study of high-current 7 Li 3+ beam acceleration with laser ion source and RFQ linac using direct plasma injection

Accelerator-based neutron sources (ABNS) utilizing the 7 Li(p,n) 7 Be reaction in inverse kinematics offer strong forward-directed neutron emission, making them attractive for compact and clean neutron source with low background neutrons and unwanted radiation. However, practical use of such systems requires lithium ion beam currents that exceed the capability of conventional ion accelerators by two orders of magnitude. In this study, we numerically designed and evaluated a high-current acceleration system based on a direct plasma injection scheme (DPIS), combining a laser ion source (LIS) and a radio-frequency quadrupole (RFQ) linac. The ion extraction optics and RFQ entrance were optimized using IGUN, OPERA, and GPT, demonstrating that over 1.2 A-class beam current can be injected into the RFQ. The RFQ structure was designed under realistic engineering constraints including surface field strength (Kilpatrick factor) and RF power. GPT simulations showed that a 370 mA 7 Li 3+ beam can be successfully accelerated within a ±10 % energy spread. To validate downstream compatibility, we also designed a simplified medium-energy beam transport (MEBT) section and an interdigital H-type (IH) linac, confirming successful acceleration of a 320 mA beam to the total energy of 14 MeV. These results support the feasibility of the DPIS and RFQ approach as a promising solution for compact neutron sources and other high-intensity ion beam applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Measurement of directional muon beams generated at the Berkeley Lab Laser Accelerator

We present the detection of directional muon beams produced using a PW laser facility at the Lawrence Berkeley National Laboratory. The muon source is a multi-GeV electron beam generated in a laser-plasma accelerator interacting with a high- converter target. The GeV photons resulting from the interaction are converted into a high-flux, directional muon beam via pair production. By employing scintillators to capture delayed events, we were able to identify the produced muons and characterize the source. Using theoretical knowledge of the muon production process combined with simulations that are in excellent agreement with the experiments, we demonstrate that laser-plasma accelerators have the capability of generating electron beams with characteristics suitable to produce GeV-scale muons that offer unique advantages with respect to the cosmic background. Laser-plasma-accelerator-based muon sources can therefore enhance muon imaging applications thanks to their compactness, directionality, and high yields, which reduce the exposure time by orders of magnitude compared to cosmic ray muons. Using the eant4-based simulation code we developed to gain insight into the experimental results, we can design future experiments and applications based on LPA-generated muons.

Terzani, Davide↗
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 OSTI2026

Multi-resolution enhancement for full-spectrum neural representations

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-basedrepresentations increasingly intractable. Implicit neural representations (INRs) offer a promising solutionby encoding signals through coordinate-based neural networks, serving as surrogates of data, withcomputational and storage requirements scaling with network complexity rather than data dimensionality.However, smaller INRs struggle to faithfully represent multiscale structures, high-frequency informationand fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, atheoretically guided hierarchical INR framework that distributes modelling across resolution scales andenables improved representation capacity through a novel enhancement network to recover subtle details.This multiscale architecture allows smaller networks to retain the full spatial-frequency content of thesignal as well as preserve training efficiency and lower storage cost. Evaluated on distinct raw experimentalmeasurements across scales and complexities, WIEN-INR represents a practical step towards a broaderadoption of neural representations in scientific workflows, delivering compact, robust and high-fidelityrepresentations.

Ni, Yuan [SLAC National Accelerator Laboratory (SL↗
DOE OSTI2025

Development of the first 1 GHz niobium-tin quarter-wave cavity

Niobium-Tin (Nb3Sn) is a promising alternative to pure niobium for low-beta ion accelerators due to low RF losses even at high frequencies. At 4.5 K, the expected Bardeen-Cooper-Schrieffer resistance of Nb3Sn is a few nano-Ohms at 1 GHz, with two orders of magnitude improvement compared to pure niobium. The low Nb3Sn RF losses allow the use of higher frequency, much smaller cavities while maintaining 4.5 K operation that is compatible with cryocoolers. This work aims to demonstrate the feasibility of coating a high-frequency 1 GHz, compact quarter-wave cavity. The development of this cavity has the potential to transform low-beta ion accelerators through size reductions and by enabling the replacement of large helium cryoplants with small plug-in cryocoolers. We have designed, built, and tested a 1GHz Nb3Sn coated quarter-wave cavity. Nb3Sn coating has been performed once by vapor diffusion at Fermilab. Cryogenic cold tests show a quality factor of ~1E9 at low accelerating electric fields or ~7 times higher than the theoretical limit for pure niobium at 1GHz. This first coating does not yet meet our quality factor or gradient goals; however, we plan to continue to develop this cavity.

Zhou, Yang [Argonne]↗
DOE OSTI2024

Proposal for a Proton-Bunch Compression Experiment at IOTA in the Strong Space-Charge Regime

The longitudinal compression of intense proton bunches with strong space-charge force is an essential component of a proton-based muon source for a muon collider. This paper discusses a proton-bunch compression experiment at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab to explore optimal radio frequency (RF) cavity and lattice configurations. IOTA is a compact fixed-energy storage ring that can circulate a 2.5-MeV proton beam with varying beam parameters and lattice configurations. The study will aim to demonstrate a bunch-compression factor of at least 2 in the IOTA ring while examining the impact of intense space-charge effects on the compression process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Proposal for a proton-bunch compression experiment at IOTA in the strong space-charge regime

The longitudinal compression of intense proton bunches with strong space-charge force is an essential component of a proton-based muon source for a muon collider. This paper discusses a proton-bunch compression experiment at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab to explore optimal radio frequency (RF) cavity and lattice configurations. IOTA is a compact fixed-energy storage ring that can circulate a 2.5-MeV proton beam with varying beam parameters and lattice configurations. The study will aim to demonstrate a bunch-compression factor of at least 2 in the IOTA ring while examining the impact of intense space-charge effects on the compression process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

IOTA experiment for proton pulse compression at extreme space-charge

The longitudinal compression of intense proton bunches with strong space-charge force is an essential component of a proton driver for a muon collider. We propose a proton bunch compression experiment at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab to explore optimal radio frequency (RF) cavity and lattice configurations. IOTA is a compact fixed-energy storage ring dedicated to beam physics R&D that can circulate a 2.5-MeV proton beam with extreme space-charge. Using ImpactX and its 3D space-charge solver, simulations indicate that bunch length can be rapidly reduced by a factor of at least two, without appreciable degradation in transverse beam quality, even under strong space-charge conditions. However, longitudinal defocusing presents a large effect in short-pulsed proton beams, and the optimization of bunch compression under such conditions is discussed.

Simons, Benjamin [Northern Illinois U.; Fermilab]↗
DOE OSTI2026

Development of Advanced, Radiation Resistant, Optical-based Detector Technology for Future Experiments.

The primary objective of this project has been to advance the design of high-performance electromagnetic (EM) calorimeters for future particle physics experiments, to identify and measure the timing, position and energy of electrons, positrons and gamma rays, particularly in high-luminosity environments with intense radiation and pileup conditions. To meet such challenges, the proposed research has focused on the development of ultra-compact, radiation-hard calorimeter modules, to provide excellent timing, spatial, and energy resolution. The work aligns with the DOE’s Basic Research Needs (BRN) for High Energy Physics (HEP) Instrumentation and the research team contributes actively to the Coordinating Panel on Advanced Detectors (CPAD) RDC9 calorimetry collaboration in the USA and the European Committee on Future Accelerators (ECFA) DRD-CALO calorimetry collaboration at CERN, the European Laboratory for Particle Physics located in Geneva, Switzerland. The research builds on the RADiCAL (radiation-hard, ultra-compact) modular sampling calorimeter approach, developed by the research team, which employs dense and very bright optical materials such as LYSO:Ce scintillator plates that are interleaved with very dense tungsten plates to minimize detector size while optimizing performance. The modules are comparable in size to a human index finger, dimensionally 14 mm x 14 mm in cross section and 135 mm in length. And despite the small size, the structure is capable of providing excellent timing and energy resolution. This is facilitated through the use of specialized quartz capillaries filled with wavelength-shifting filaments, positioned at various depths along the length of a module, to collect and guide light signals to silicon photomultipliers (SiPMs) which detect and convert the optical signals to electronic signals for analysis. The primary goals of this project have been: (1) Achieve a timing resolution to σ t ≤ 30 ps for high-energy electrons and photons, important for their association with specific events produced in colliding-beam experiments and for the detection of decays-in-flight of long-lived particles. The project has achieved this goal in beam tests of a single RADiCAL module at CERN, during which a timing resolution of σ t = 27 ps was measured for electrons of energy E = 150 GeV. Based upon a mathematical fit to the data measured over a broad energy range from low energy to high energy, a resolution of σ t ≤ 18 ps has been estimated for electrons of very high (TeV) energy. From these measurements and with further expected technical improvements, the timing resolution should reach σ t ≤ 10 ps, important for searches for discovery physics in upcoming and future experiments. (2) Achieve an energy resolution of σ E / E ≤ 10% / $\sqrt{E}$. The project has yet to achieve this goal, but is close to it, having measured a value of σ E / E ≤ 15.9% / $\sqrt{E}$ using a modular array. Ultimately, the resolution goal is expected to be reached by adjustments to material thicknesses within the modules, which will improve the sampling fraction to measure more precisely the shower energy for lower energy particles. The versatility of the modular RADiCAL approach enables the testing of advanced materials, photosensors and electronics, developed in collaboration with CPAD RDC and ECFA DRD-CALO groups. The structure can distinguish electrons, positrons and gamma rays from hadrons and muons and beam-induced backgrounds, making it a valuable tool in a variety of detector environments, including future circular colliders (FCC-ee, FCC-hh) proposed for the European Laboratory for Particle Physics (CERN), the muon-collider proposed for Fermi National Accelerator Laboratory (Fermilab), and searches for new physics in beam-dump, fixed target and forward-physics experiments. And, while designed with particle physics applications in mind, the technologies developed in this project have the potential for application more broadly in particle and nuclear physics, materials science, and medical physics, underscoring the far-reaching potential of this line of instrumentation research and development.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

IOTA Experiment for Proton Pulse Compression at Extreme Space-Charge

The longitudinal compression of high-intensity, space-charge-dominated proton bunches is a critical requirement for future proton-driven muon colliders. We propose a proton bunch compression experiment at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab to investigate optimal radio-frequency (RF) cavity parameters and lattice configurations. IOTA is a compact, fixed-energy storage ring dedicated to beam physics Research and Development and capable of circulating a 2.5 MeV proton beam under extreme space-charge conditions. Using the ImpactX code with its 3D space-charge solver, simulations indicate that the bunch length can be rapidly reduced by at least a factor of two without appreciable degradation of transverse beam quality--even in the strong space-charge regime. However, longitudinal defocusing due to the space-charge remains a significant challenge in short-pulsed intense proton bunches, and the optimization of compression under these conditions is discussed.

Simons, B. D. [Northern Illinois U.]↗
DOE OSTI2025

The Muon Collider

Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose an R\&D project that can make the muon collider a reality.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2025

Additive Manufactured Compact Microwave Absorbers

A high-performance, compact microwave absorber was created using Fused Deposition Modeling (FDM) 3D printing. Both a narrowband and a broadband absorber were created. The narrowband absorber was designed at 4.9 GHz, mid-band in WR-187 waveguide. The broadband absorber tried to achieve the best attenuation across the entire 3.95 to 5.85 GHz band. Two types of carbon loaded polylactic acid (PLA) plastic and one type of unloaded PLA were 3D printed with variable percentages of air to achieve different values of effective dielectric constant and loss tangent. The absorber comprised five or six rectangular pieces of these plastic materials. The thickness and fill factor values for each piece were optimized to minimize reflection through fast analytic modeling in MATLAB®. The results were then verified by HFSS® simulation as well. The stack progressed from the lowest loss and lowest dielectric constant to the highest at the shorting end. The final narrowband load had simulated return loss of 87 dB at 4.9 GHz with an analytic solution in MATLAB. The measured return loss of the 3D printed attenuator was 73 dB at 4.929 GHz. The total length of the absorber was 2.44 inches. A commercial absorber for WR-187 with return loss of 40 dB has length of 13 inches. The experiment proves that an effective and compact microwave absorber can be created using 3D printing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles: Preprint

In this work, we present a proof of concept of a physics-based digital twin for a monopile structure (with overhead inertia) subjected to wave loading. The digital twin is formulated using reduced-order models derived from first principles and combined with a Kalman filter for state estimation. The proposed framework estimates the monopile top motion, the wave elevation, and the section forces and moments along the pile using primarily acceleration measurements at the monopile top. Key innovations include the use of a hydrodynamic shape function to represent distributed wave loading in a compact and computationally efficient manner, and the introduction of a shaping filter to augment the state-space with wave kinematics. Synthetic measurement data are generated using OpenFAST and used as a reference to assess the performance of the digital twin. Results demonstrate that the wave elevation can be accurately reconstructed without direct sea-state measurements as long as the wave regime is inertia-dominated. Under the ideal tested conditions, the total hydrodynamic force and sea-bed bending moment are estimated with relative errors on the order of 1% and correlation coefficients exceeding 96%. Future work will evaluate the estimator's performance under operational uncertainties and more complex loading conditions.

17 WIND ENERGY↗