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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Massive, Position-Resolving, High-Energy-Resolution Detector for Non-Accelerator Cosmic and Intensity Frontier Particle Physics

We proposed to take the first steps in the development of a detector that promises energy resolution of tens of eV FWHM combined with robust nuclear-recoil discrimination and high fidelity, mm-precision position reconstruction for applications in non-accelerator particle physics at the Cosmic and Intensity Frontiers. The detector would obtain these excellent resolutions by sensing athermal phonons produced by particle interactions in crystalline, dielectric targets using a sensitive, highly multiplexable superconducting phonon sensor, the kinetic inductance detector (KID). This detector would be applicable to: the search for low-mass particle dark matter candidates with masses below 5~GeV via direct detection of scattering of dark matter particles with terrestrial nuclei; detection of coherent elastic neutrino-nucleus scattering to test for new physics such as a non-standard value of the weak nuclear charge, non-standard neutrino interactions (perhaps driven by a neutrino magnetic moment), or the existence of sterile neutrinos; and, searches for neutrinoless double-beta decay. During the funding period, we demonstrated scaling up of the detector concept from a 22-mm by 22-mm by 1-mm, 1-g prototype with 0.9~keV FWHM energy resolution to a 75-mm diameter by 1-mm, 9-g prototype while improving the inferred energy resolution to 0.7~keV~FWHM. In the process, we solved many problems associated with scaling device fabrication to large wafers and vastly reduced the fraction of the surface occupied by inactive but energy-absorbing metal. We also demonstrated a new technique that substantially simplifies the process of characterizing a new detector. These results provide a good foundation for future work scaling up the design to 4-mm thickness substrates and improving the resolution to reach 0.035~keV~FWHM, yielding a detector with compelling potential for the above applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Global tuning of hadronic interaction models with accelerator-based and astroparticle data

In high-energy and astroparticle physics, event generators play an essential role, even in the simplest data analyses. As analysis techniques become more sophisticated, e.g. based on deep neural networks, their correct description of the observed event characteristics becomes even more important. Physical processes occurring in hadronic collisions are simulated within a Monte Carlo framework. A major challenge is the modeling of hadron dynamics at low momentum transfer, which includes the initial and final phases of every hadronic collision. QCD-inspired phenomenological models used for these phases cannot guarantee completeness or correctness over the full phase space. These models usually include parameters which must be tuned to suitable experimental data. Until now, event generators have been developed and tuned mainly on the basis of data from high-energy physics experiments at accelerators. The wealth of data available from the latest generation of astroparticle experiments has not yet been fully exploited, and in many cases is not satisfactorily described. Both kinds of data sets are complementary as astroparticle experiments provide sensitivity especially to hadrons produced nearly parallel to the collision axis and cover center-of-mass energies up to several hundred TeV, well beyond those reached at colliders so far. In this report, we provide an overview of state-of-the-art event generators and their tuning, including the most relevant inputs from high-energy accelerator and astroparticle experiments. We present a road map that shows, for the first time, how the unified tuning of event generators with accelerator-based and astroparticle data can be performed.

Albrecht, J. [Ruhr U., Bochum, RAPP Ctr.; Ruhr U.,↗
DOE OSTI2023

An unstructured mesh based neutronics optimization workflow

We have developed a fully automated workflow to optimize the neutronics performance of the Second Target Station (STS) at the Oak Ridge National Laboratory’s Spallation Neutron Source. The optimization workflow starts with the parametrized solid CAD engineering models and converts them into the unstructured mesh (UM) models for the neutronics calculations with MCNP6.2. Calculations are executed and their results are loaded into the Dakota optimization toolkit. Dakota analyzes the results and proposes new geometry parameters for the next design iteration. The cycle repeats until the optimal parameters are found. The automated CAD to MCNP conversion, the use of high-fidelity UM models, and the use of modern optimizer are the key elements that advance the entire optimization workflow in comparison with the original workflow. The original workflow was based on a simplified constructive solid geometry (CSG) modeling with MCNPX, mcnp_pstudy tool, and an in-house optimizer. Herein to demonstrate the new workflow, we present a case of neutronics optimization of the moderator–reflector assembly (MRA). Apart from the MRA, the workflow can optimize other major STS components, such as the spallation target, neutron beamlines, radiation shielding, and various accelerator components. Importantly, the new workflow opens the door to the advanced multi-physics multi-parameter optimization and has the potential for use in other nuclear physics and accelerator applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

An extraction scheme for future CEBAF FFA based energy upgrade

Jefferson lab is considering an energy increase from current 12 GeV to 22 GeV for its CEBAF accelerator. This will be accomplished by recirculating 5-6 additional turns through two parallel CEBAF LINACs using an FFA arc at each end of the racetrack. The total recirculation turns would be 10 times, the first four turns use present conventional arcs to make the 180-degree bends from one LINAC to the other. However, the last 5-6 turns will all share a single beam line inside two FFA arcs. This reduces the footprint and the cost of the project significantly. On the other hand, having the trajectories of last 5-6 recirculating beams close to each other makes it challenging to extract beams from different passes with different energies. In this paper we will explain our present extraction system for 12 GeV, our challenges and limitations, and a possible extraction solution for the 22 GeV upgrade with the goal of extracting beam at different turns/energies to different experimental halls.

Accelerator Physics↗
DOE OSTI2026

Design of a microbunched electron cooler energy recovery linac

Microbunched electron Cooling (MBEC), a type of Coherent electron Cooling (CeC), is a possible way to cool high energy protons; such an electron cooler can be driven by an energy recovery linac (ERL). The beam parameters of this design are based on cooling 275 and 100 GeV protons at the Electron-Ion Collider (EIC), requiring 150 and 55 MeV electrons, respectively. If implemented, a high energy cooler would serve to increase the average luminosity of the collider by mitigating the emittance growth caused by various processes. This ERL is designed to deliver a bunch charge of 1 nC, an average current of 100 mA, and strict requirements on the transverse emittance, slice energy spread, and longitudinal distribution profile. This paper covers the current state of the design.

Accelerator Physics↗
DOE OSTI2026

Multi-GeV FFA beam transport test at CEBAF

Jefferson National Lab plans an upgrade project to reach 22 GeV high polarization electron beam by using Fixed Field Alternating-gradient (FFA) magnets. The utilization of the FFA magnets for 10-22 GeV beam energy range is unexampled, therefore those magnets need an experimental validation before their full installation to form an arc in the Continuous Electron Beam Accelerator Facility (CEBAF). For this reason, JLAB is also considering the design of an FFA magnet test bench, i.e. a half or full FFA cell, that would be deployed in the current CEBAF in order to serve as the highest energy demonstration for the FFA field uniformity, permanent magnet resiliency with the beam as well as enabling beam optics measurements with the 5-11 GeV range highly polarized beams which closely resembles the full energy range of the 22 GeV upgrade. In this report, we present the status of the planned beamline for the FFA beam transport test at CEBAF.

Accelerator Physics↗
DOE OSTI2026

Simulations of positron injector for Ce+BAF

A baseline concept for a continuous wave (CW) polarized positron injector was developed for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. This concept is based on the generation of CW longitudinally polarized positrons by a high-current, polarized electron beam (1 mA, 130‑370 MeV, and 90% longitudinal polarization) that passes through a rotating, water-cooled, tungsten target. The simulation results for the Ce+BAF injector at the Low Energy Recirculator Facility (LERF) are presented, including positron beam generation, capture, energy selection, and acceleration to 123 MeV. The positron yield (or positron current) and longitudinal polarization are calculated considering the longitudinal and transverse CEBAF acceptances (<1% energy spread, <1 mm bunch length and normalized emittance of <100 mm mrad). The impact of target thickness, drive electron beam energy, and transverse size on positron yield within the required emittance limit is evaluated.

Accelerator Physics↗
DOE OSTI2021

Tunable Permanent Magnet Quadrupole Operating at Cryogenic Temperatures for Accelerators

The project objective is to develop a compact and tunable quadrupole using Electron Energy Corporation (EEC)’s PM materials for cryogenic accelerator applications. To achieve this objective, EEC and SLAC National Accelerator Laboratory (SLAC) will collaborate to develop a cost-effective quadrupole and optimize the conventional manufacturing process. Based on previous experience, EEC designed an innovative field adjustment capability using tuning magnets which is able to achieve all the requirements for SLAC’s cold copper collider (C3 ) application. These novel ideas enable us to develop a compact and tunable quadrupole magnet superior to the current designs. Our approach is to leverage EEC's over 50 years of experience in magnet manufacturing and SLAC’s beamline test capabilities.

42 ENGINEERING↗
DOE OSTI2022

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

EUV FEL light source based on energy recovery linac with on-orbit laser plasma injection

We report on a week-long study of a conceptual design of EUV FEL light source based on an energy recovery linac with on-orbit laser plasma accelerator injection scheme. We carried out this study during USPAS Summer 2023 session of Unifying Physics of Accelerators, Lasers and Plasma applying the art of inventiveness TRIZ. An ultrashort Ti-sapphire laser accelerates electron beams from a gas target with mean energy of 20 MeV, which are then ramped up to 1 GeV in a five-turn scheme with a series of fixed field alternating magnets and two superconducting RF cavities (100 MeV per cavity per turn). The electron beam is then bypassed to an undulator line optimized to generate EUV light of 13.5 nm at kW level in a single pass.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Physics successfully implements Lagrange multiplier optimization

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip–flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Frequency dependence of BCS and residual resistance using multi-mode measurement of nitrogen-doped single-cell elliptical cavities

Various cavity surface treatments have been found to significantly improve cavity quality factor, Q0, with one such treatment being nitrogen-doping (N-doping). N-doped 1.3 GHz cavities were the first found to exhibit anti-Q slope, the increase of Q0 with accelerating field, Eacc. However, even with the use of the same N-doping recipes, this anti-Q slope behavior has not been realized in sub-GHz frequency cavities. In this study, we measured the Q0 and surface resistance, RS, of our N-doped, single-cell, elliptical cavity for both the 644 MHz fundamental mode (FM) and 1.45 GHz higher-order mode (HOM). As a result of multi-mode measurements, the BCS and residual resistances, the temperature-dependent and temperature-in-dependent RF surface resistances, could be determined without the influence of cavity-to-cavity surface treatment variations. We will discuss the frequency-dependent behaviors of the BCS and residual resistances.

Accelerator Physics↗
DOE OSTI2023

Thermodynamic stability of xenon-doped liquid argon detectors

Liquid argon detectors are employed in a wide variety of nuclear and particle physics experiments. The addition of small quantities of xenon to argon modifies its scintillation, ionization, and electroluminescence properties and can improve its performance as a detection medium. However, a liquid argon-xenon mixture can develop instabilities, especially in systems that require phase transitions or that utilize high xenon concentrations. In this work, we analyze the causes of these instabilities and describe a small (liter-scale) apparatus with a unique cryogenic circuit specifically designed to handle argon-xenon mixtures. The system is capable of condensing argon gas mixed with $\mathscr{O}$ (1%) xenon by volume and maintains a stable liquid mixture near the xenon saturation limit while actively circulating it in the gas phase. We also demonstrate control over instabilities that develop when the detector condition is allowed to deviate from optimized settings. This progress enables future liquid argon detectors to benefit from the effects of high concentrations of xenon doping, such as more efficient detection of low-energy ionization signals. In conclusion, this work also develops tools to study and mitigate instabilities in large argon detectors that use low concentration xenon doping.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Plasma processing of SRF cavities at Jefferson Lab: Experiment results and simulation insight

Plasma processing of superconducting radio frequency (SRF) cavities has been an active research effort at Jefferson Lab (JLab) since 2019, aimed at enhancing cavity performance by removing hydrocarbon contaminants and reducing field emission. In this experiment, processing using argon-oxygen and helium-oxygen gas mixtures to find minimum ignition power at different cavity pressure was investigated. Ongoing simulations are contributing to a better understanding of the plasma surface interactions and the fundamental physics behind the process. These simulations, combined with experimental studies, guide the optimization of key parameters such as gas type, RF power, and pressure to ignite plasma using selected higher-order mode (HOM) frequencies. This paper presents experimental data from argon-oxygen and helium-oxygen gas mixture C75 and C100 cavity plasma ignition studies, as well as simulation results for the C100-type cavity based on the COMSOL model previously applied to the C75 cavity.

Accelerator Physics↗
DOE OSTI2026

Explainable physics-based constraints on reinforcement learning for accelerator optimization

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments.

explainability↗