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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrodynamic modeling of plasma channel systems for laser plasma accelerators

Structured plasma channels are an essential technology for driving high-gradient, plasma-based acceleration and control of electron and positron beams for advanced concepts accelerators. Laser and gas technologies can permit the generation of long plasma columns known as hydrodynamic, optically-field-ionized (HOFI) channels, which feature low on-axis densities and steep walls. By carefully selecting the background gas and laser properties, one can generate narrow, tunable plasma channels for guiding high intensity laser pulses. Here, we present on the development of simulations of HOFI channels using the FLASH code, a publicly available radiation hydrodynamics code. We explore sensitivities of the channel evolution to laser profile, intensity, and background gas conditions, and identify relevant scalings with laser intensity through a range of practical channel delays.

Accelerators↗
DOE OSTI2022

On possibility of low-emittance high-energy muon source based on plasma wakefield acceleration

We report plasma wakefield acceleration (PWA) channels are characterized by very high accelerating gradients and very strong focusing fields. We propose to employ these properties for effective production of low emittance high energy muon beams, consider muon beam dynamics in the PWFA cell and analyze various options and potential of the PWA-based muon sources.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Tau tridents at accelerator neutrino facilities

We present the first detailed study of Standard Model neutrino tridents involving tau leptons at the near detectors of accelerator neutrino facilities. The rates of these processes were previously thought to be negligible, even at future facilities. Our full 2 → 4 calculation, including both coherent and incoherent scatterings, reveals that the DUNE near detector could observe a considerable number of tau tridents—an important background to new physics searches. We identify promising kinematic features that may allow distinction of tau tridents from the usual neutrino charged-current background at DUNE, and thus establish the observation of tau tridents for the first time. We also comment on the detection prospects at other accelerator and collider neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Nanosecond rf-Power Switch for Gyrotron-Driven Millimeter-Wave Accelerators

The development of alternative mm-wave high-gradient, >200 MV/m, accelerating structures offers a promising path to reduce the cost and footprint of future TeV-scale linear colliders, as well as linacs for industrial, medical, and security applications. The major factor limiting accelerating gradient is vacuum rf breakdown. The probability of such breakdowns increases with pulse length. For reliable operation, millimeter-wave structures require nanoseconds-long pulses at the megawatt level. This power is available from gyrotrons, which have a minimum pulse length on the order of microseconds. To create shorter pulses and to reliably detect rf breakdowns, we developed the following devices: a laser-based rf switch capable of selecting 10 ns long pulses out of the microseconds long gyrotron pulses, thus enabling the use of the gyrotrons as power sources for mm-wave high-gradient linacs, and a shot-to-shot sub-THz spectrometer with high-frequency resolution, capable of detecting pulse shortening due to rf breakdowns. We will describe the principle of operation of these devices and their achieved parameters. Here, we also report on the experimental demonstration of these devices with the high-power gyrotron at the Massachusetts Institute of Technology. In the experiments, we demonstrate nanosecond rf power modulation, shot-to-shot measurements of the pulse spectra, and detection of rf breakdowns.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Stable Positron Acceleration in Thin, Warm, Hollow Plasma Channels

Hollow plasma channels are attractive for lepton acceleration for providing intrinsic emittance preservation regimes. However, beam breakup instabilities dominate the dynamics. Here, we show that thin, warm hollow channels can sustain large-amplitude plasma waves ready for high-quality positron acceleration. We verify that the combination of warm electrons and thin hollow channels enables positron focusing structures. Such focusing wakefields unlock beam breakup damping mechanisms. We demonstrate that such channels emerge self-consistently during the long-term plasma dynamics in the blowout's regime aftermath, allowing for experimental demonstration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Ultrabroad-band x-ray source using a picosecond, laser-driven plasma accelerator

An ultrabroad-band x-ray source, with photon energies from 10 keV to >1 MeV, based on a picosecond laser-driven plasma accelerator, is characterized and used to radiograph high-energy-density-science relevant targets. The measured yield of 10 12 photons/shot is reaching the necessary photon yields to radiograph, in a single shot, high areal density objects and matter under extreme conditions. By focusing a short laser pulse (120 J, 1 ps) into a gas jet, a < 100 mrad electron beam with energies up to 350 MeV and up to 70 nC of charge was produced by a combination of laser self-modulation instability and direct laser acceleration. A foil placed at the exit of the gas jet is used to convert part of the electron beam energy into x rays through inverse bremsstrahlung and/or inverse Compton scattering, generating a bright, broad-band, high-photon-energy beam. This beam is used to radiograph a gold half hohlraum with a high-density sphere inside with relevant characteristics for high-energy-density science and inertial confinement fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Towards continual machine learning for particle accelerators

This talk covers our work on errant beam prognostics at the Spallation Neutron Source (SNS), focusing on the end-to-end process from data collection to the development and deployment of predictive models in specific. A short overview of AIML work done for accelerators and current trends will be presented. We will walk through key steps involved in creating robust Machine Learning (ML) models, including model training, validation, and deployment in an operational setting. In addition to presenting our technical approach, we will share valuable lessons learned, emphasizing the importance of infrastructure to support the continuous adaptation of models to evolving data and system behaviors. This talk will provide insights into the challenges and solutions involved in applying ML to real-world operational environments, with a particular focus on managing data drift and changes in accelerator setup while ensuring model resilience over time.

Accelerator Physics↗
DOE OSTI2017

Notes on the design of experiments and beam diagnostics with synchrotron light detected by a gated photomultiplier for the Fermilab superconducting electron linac and for the Integrable Optics Test Accelerator (IOTA)

We outline the design of beam experiments for the electron linac at the Fermilab Accelerator Science and Technology (FAST) facility and for the Integrable Optics Test Accelerator (IOTA), based on synchrotron light emitted by the electrons in bend dipoles, detected with gated microchannel-plate photomultipliers (MCP-PMTs). The system can be used both for beam diagnostics (e.g., beam intensity with full dynamic range, turn-by-turn beam vibrations, etc.) and for scientific experiments, such as the direct observation of the time structure of the radiation emitted by single electrons in a storage ring. The similarity between photon pulses and spectrum at the downstream end of the electron linac and in the IOTA ring allows one to test the apparatus during commissioning of the linac.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Laboratory demonstration of slow rise to fast acceleration of arched magnetic flux ropes

In this paper, we demonstrate the slow rise to fast acceleration of an arched plasma-filled magnetic flux rope. The flux rope expansion is inhibited by an externally applied customizable strapping field. When the strapping field is not too strong and not too weak, expansion forces build up while the flux rope is in the strapping field region. When the flux rope moves to a critical height beyond the peak strapping field region, the plasma accelerates quickly corresponding to the observed slow rise to fast acceleration of solar eruptions. Finally, this behavior is in agreement with the predictions of the torus instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Heating and acceleration of ions with Kappa distribution functions by low‐frequency Alfvén wave

Abstract Heating and acceleration of ions with Kappa distribution functions (with parameter ) in low‐beta plasmas, by a low‐frequency Alfvén wave, is investigated using test‐particle simulations, yielding interesting new results. As long as the Alfvén wave amplitude is sufficiently large, the computed net heating energy of ions becomes independent of the wave frequency and amplitude, always approaching the same value of . The eventual energy of ions is dictated only by the initial ion energy and the ratio of the magnetic field energy density to the plasma density. The heating effect of the Kappa ions increases with . During the heating process, the ions are picked up by the Alfvén wave and pitch angle scattered, forming a quasi‐isotropic spherical shell velocity distribution. The Kappa ions are accelerated in the parallel direction, reaching a bulk flow speed roughly equal to the local Alfvén speed. Higher ‐value in the initial Kappa distribution leads to faster saturation. The above results may explain certain features of the ion heating and acceleration in the solar wind and corona.

Li, Kehua↗
DOE OSTI2020

Application of radiochromic gel dosimetry to commissioning of a megavoltage research linear accelerator for small–field animal irradiation studies

To develop and implement an efficient and accurate commissioning procedure for small-field static beam animal irradiation studies on an MV research linear accelerator (Linatron-M9) using radiochromic gel dosimetry. The research linear accelerator (Linatron-M9) is a 9 MV linac with a static fixed collimator opening of 5.08 cm diameter. Lead collimators were manually placed to create smaller fields of 2 × 2 cm 2 , 1 × 1 cm 2 , and 0.5 × 0.5 cm 2 . Relative dosimetry measurements were performed, including profiles, percent depth dose (PDD) curves, beam divergence, and relative output factors using various dosimetry tools, including a small volume ionization chamber (A14), GAFCHROMIC™ EBT3 film, and Clearview gel dosimeters. The gel dosimeter was used to provide a 3D volumetric reference of the irradiated fields. The Linatron profiles and relative output factors were extracted at a reference depth of 2 cm with the output factor measured relative to the 2 × 2 cm 2 reference field. Absolute dosimetry was performed using A14 ionization chamber measurements, which were verified using a national standards laboratory remote dosimetry service. Absolute dosimetry measurements were confirmed within 1.4% (k = 2, 95% confidence = 5%). The relative output factor of the small fields measured with films and gels agreed with a maximum relative percent error difference between the two methods of 1.1 % for the 1 × 1 cm 2 field and 4.3 % for the 0.5 × 0.5 cm 2 field. These relative errors were primarily due to the variability in the collimator positioning. The measured beam profiles demonstrated excellent agreement for beam size (measured as FWHM), within approximately 0.8 mm (or less). Film measurements were more accurate in the penumbra region due to the film’s finer resolution compared with the gel dosimeter. Following the van Dyk criteria, the PDD values of the film and gel measurements agree within 11% in the buildup region starting from 0.5 cm depth and within 2.6 % beyond maximum dose and into the fall-off region for depths up to 5 cm. The 2D beam profile isodose lines agree within 0.5 mm in all regions for the 0.5 × 0.5 cm 2 and the 1 × 1 cm 2 fields and within 1 mm for the larger field of 2 × 2 cm 2 . The 2D PDD curves agree within approximately 2% of the maximum in the typical therapy region (1–4 cm) for the 1 × 1 cm 2 and 2 × 2 cm 2 and within 5% for the 0.5 × 0.5 cm 2 field. This work provides a commissioning process to measure the beam characteristics of a fixed beam MV accelerator with detailed dosimetric evaluation for its implementation in megavoltage small animal irradiation studies. Radiochromic gel dosimeters are efficient small-field relative dosimetry tools providing 3D dose measurements allowing for full representation of dose, dosimeter misalignment corrections and high reproducibility with low inter-dosimeter variability. Altogether, radiochromic gels are valuable for fast, full relative dosimetry commissioning in comparison to films for application in high-energy small-field animal irradiation studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗
DOE OSTI2018

Active Quenching Technique for YBCO Tapes: Quench Acceleration and Protection

The application of resistive-type superconducting fault current limiters (RSFCLs) in electrical networks is very attractive due to their relative compactness, light weight, and good performance. However, this technology still has drawbacks: asymmetrical quench, uncertain limiting velocity, passive action and incapability of enlarging capacity. Here, we present an active quenching technique which can potentially solve these problems. The quenching process is triggered using high-frequency (HF) AC fields, which are generated by two coupled copper coils attached on both sides of the YBCO sample. Our experiments show that this approach is effective in guaranteeing uniform quench and therefore is expected to be able to significantly extend the service life of the device. Moreover, the quench speed can be considerably increased by the HF field. We find that the performance of acceleration is positively correlated to the transport current, intensity and frequency of the AC field. In addition, a DC magnetic field is added around the sample holder simultaneously with the AC field, to study the field-angular-frequency dependence of the quench time t(B{sub AC}f{sub AC}B{sub DC}𝜃{sub DC}). Experimental results prove that the DC magnetic field can cooperate with the HF AC field to accelerate quench, which means better performance can be produced with lower costs with the two fields acting together. In all, this technique showed outstanding performance regarding quench acceleration and tape protection. We believe the HF-assisted quenching technology has a promising future in current-limiting devices and hope our findings could be helpful for its potential applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600–700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Suppressing NLRP2 expression accelerates hepatic steatosis: A mechanism involving inflammation and oxidative stress

Highlights: • NLRP2 knockout enhances HFD-elicited metabolic syndrome and insulin resistance. • NLRP2 deletion accelerates inflammation and oxidative stress in liver of HFD-fed mice. • NLRP2 regulates Nrf2 to meditate inflammation and oxidative stress in primary hepatocytes treated with palmitate. Nonalcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation and inflammation in the liver, contributing to a broad spectrum of severe pathologies, such as metabolic syndrome and hepatocellular carcinoma. Presently, the pathogenesis that attributes to NAFLD has not been fully understood. NLRP2 has been shown to inhibit the NF-κB signaling, and thus may contribute to regulate the inflammatory response. However, its role in NAFLD is largely unclear. In the study, we found that NLRP2 was markedly decreased in liver tissues of individuals with severe steatosis, or in a genetic deficiency (ob/ob) mice. High fat diet (HFD) feeding also led to a significant reduction of NLRP2 in liver of mice. Then, the wild type (WT) and NLRP2 knockout (KO) mice were used to further explore the role of NLRP2 in the NAFLD progression. NLRP2 knockout mice exhibited severer metabolic syndrome and hepatic steatosis after HFD administration, as evidenced by the increased body weight, liver histological changes and lipid accumulation. Moreover, HFD feeding-induced inflammation was significantly accelerated by the loss of NLRP2, as evidenced by the increased expression of pro-inflammatory cytokines and activation of nuclear factor κB (NF-κB) pathway. In addition, oxidative stress triggered by HFD was further promoted by NLRP2 deletion through repressing NF-E2-related factor 2 (Nrf2) pathway. In vitro, we surprisingly found that promoting Nrf2 activation could attenuate NLRP2 knockout-accelerated inflammation and reactive oxygen species (ROS) generation. Therefore, our study indicated that NLRP2 might be a potential target for developing effective therapeutic strategy to prevent NAFLD progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for efficient surface wave inversion using MPI and GPUs

Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer 1D layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics processing units (GPUs). We propose new parallel and GPU accelerated algorithms for computing 1D MASW inversion, and provide software implementations in C using Message Passing Interface (MPI) and CUDA. These algorithms take advantage of sparsity that arises in the problem, and the work balance between processes considers typical data trends. We compare our methods to an existing open source Matlab MASW tool. Our serial C implementation achieves a 2x speedup over the Matlab software, and we continue to see improvements by parallelizing the problem with MPI. Here we see nearly perfect strong and weak scaling for uniform data, and improve strong scaling for realistic data by repartitioning the problem to process mapping. By utilizing GPUs available on most modern workstations, we observe an additional 1.3x speedup over the serial C implementation on the first use of the method. We typically repeatedly evaluate theoretical dispersion curves as part of an optimization procedure, and on the GPU the kernel can be cached for faster reuse on later runs. We observe a 3.2x speedup on the cached GPU runs compared to the serial C runs. This work is the first open-source parallel or GPU-accelerated software tool for MASW imaging, and should enable geotechnical engineers to fully utilize all computer hardware at their disposal.

58 GEOSCIENCES↗
DOE OSTI2021

Machine learning accelerated discrete element modeling of granular flows

Granular flows are widely encountered in many industrial processes and natural phenomena. Discrete Element Modeling (DEM) is a useful tool for understanding and troubleshooting devices, which handle granular materials. However, its applicability is significantly limited by the huge computational cost associated with detecting and computing collisions. In this research, the computation speed of DEM was accelerated by orders of magnitude using a convolutional neural network to replace the direct calculation of particle-particle and particle-boundary collisions. The MFiX software was used to generate the training and testing dataset. Additionally, a GPU accelerated TensorFlow model was used to train the neural network and test the results. The model fluctuations caused by different training steps were reduced with a multi-scale loss function. The accuracy was improved with more frames within one training step. The modeling of a rotating drum and a hopper demonstrated the accuracy and efficiency of this machine learning accelerated DEM in the simulation of granular flows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

GPU-acceleration of the ELPA2 distributed eigensolver for dense symmetric and hermitian eigenproblems

The solution of eigenproblems is often a key computational bottleneck that limits the tractable system size of numerical algorithms, among them electronic structure theory in chemistry and in condensed matter physics. Large eigenproblems can easily exceed the capacity of a single compute node, thus must be solved on distributed-memory parallel computers. We here present GPU-oriented optimizations of the ELPA two-stage tridiagonalization eigensolver (ELPA2). On top of cuBLAS-based GPU offloading, we add a CUDA kernel to speed up the back-transformation of eigenvectors, which can be the computationally most expensive part of the two-stage tridiagonalization algorithm. Furthermore, we benchmark the performance of this GPU-accelerated eigensolver on two hybrid CPU–GPU architectures, namely a compute cluster based on Intel Xeon Gold CPUs and NVIDIA Volta GPUs, and the Summit supercomputer based on IBM POWER9 CPUs and NVIDIA Volta GPUs. Consistent with previous benchmarks on CPU-only architectures, the GPU-accelerated two-stage solver exhibits a parallel performance superior to the one-stage counterpart. Finally, we demonstrate the performance of the GPU-accelerated eigensolver developed in this work for routine semi-local KS-DFT calculations comprising thousands of atoms.

97 MATHEMATICS AND COMPUTING↗