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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2025

Evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuously monitoring fence-line sensors

The dependable reporting of methane (CH 4 ) emissions from point sources, such as fugitive leaks from oil and gas infrastructure, is important for profit maximization (retaining more hydrocarbons), evaluating climate impacts, assessing CH 4 fees for regulatory programs, and validating CH 4 intensity in differentiated gas programs. Currently, there are disagreements between emissions reported by different quantification techniques for the same sources. It has been suggested that downwind CH 4 quantification methods using CH 4 measurements on the fence line of production facilities could be used to generate emission estimates from oil and gas operations at the site level, but it is currently unclear how accurate the quantified emissions are. To investigate the accuracy of downwind methods, this study uses fence-line simulated data collected during controlled-release experiments as input for a non-standard closed-path eddy covariance (EC), the Gaussian plume inverse model (GPIM), and the backward Lagrangian stochastic (bLs) model in a range of atmospheric conditions. This study's EC attempt was unsuccessful due to data collection and instrumentation issues, resulting in invalid results characterized by underestimated emissions, large negative fluxes, and cospectra/ogives that deviated from their ideal shapes. Consequently, the EC results could not be compared with the GPIM and bLS model. The bLs model demonstrated the highest accuracy for single-release single-point emissions, though it exhibited greater uncertainty than GPIM under multi-release conditions. Across the GPIM and bLs model, the most reliable quantification was achieved with 15 min averaging and a narrow 5° wind sector range. Although EC was limited in this context, future studies should consider employing a standard EC system and further optimizing GPIM and bLs approaches – particularly for complex multi-source scenarios – to enhance quantification accuracy and reduce uncertainty.

03 NATURAL GAS↗
DOE OSTI2026

Optimization of Processing, Microstructure, and Hardness of an Al–Ce–Ni–Mn–Zr Alloy With Laser Additive Manufacturing

Here, this study examines the processing behavior, microstructure, surface roughness, and hardness properties of an aluminum alloy containing 8.2 Ce, 4.5 Ni, 0.5 Mn, and 0.7 Zr (wt%) fabricated using laser powder bed fusion. Sixty samples were produced across a range of laser powers, scan speeds, and hatch spacings to evaluate their effect on porosity, hardness, and microstructural features. Porosity was measured using X-ray computed tomography, while microstructure and surface roughness were characterized by scanning electron (SEM) and laser confocal microscopy. High dense and cracking-free Al–Ni–Ce alloy was successfully manufactured. Porosity showed a U-shaped dependence on energy input, increasing under both insufficient and excessive melting conditions. Hardness increased with cooling rate due to finer cellular structures and solute redistribution. A general statistical model was developed to capture the relationships between processing parameters and material response. Results identify a narrow processing window defined by laser powers between 350 and 370 W, scan speeds from 1400 to 1800 mm/s, and hatch distances between 0.14 and 0.18 mm. Within this window, porosity is minimized (below 0.01%) and hardness is maximized (up to 160 HV), demonstrating that careful control of these parameters enables dense, high strength aluminum components suitable for demanding structural applications.

Aluminum alloys↗
DOE OSTI2017

Dosimetric comparison of single-beam multi-arc and 2-beam multi-arc VMAT optimization in the Monaco treatment planning system

The purpose of this study was to evaluate the dosimetric and practical effects of the Monaco treatment planning system “max arcs-per-beam” optimization parameter in pelvic radiotherapy treatments. We selected for this study a total of 17 previously treated patients with a range of pelvic disease sites including prostate (9), bladder (1), uterus (3), rectum (3), and cervix (1). For each patient, 2 plans were generated, one using an arc-per-beam setting of “1” and another with an arc-per-beam setting of “2” using the volumes and constraints established from the initial clinical treatments. All constraints and dose coverage objects were kept the same between plans, and all plans were normalized to 99.7% to ensure 100% of the planning target volume (PTV) received 95% of the prescription dose. Plans were evaluated for PTV conformity, homogeneity, number of monitor units, number of control points, and overall plan acceptability. Treatment delivery time, patient-specific quality assurance procedures, and the impact on clinical workflow were also assessed. We found that for complex-shaped target volumes (small central volumes with extending arms to cover nodal regions), the use of 2 arc-per-beam (2APB) parameter setting achieved significantly lower average dose-volume histogram values for the rectum V{sub 20} (p = 0.0012) and bladder V{sub 30} (p = 0.0036) while meeting the high dose target constraints. For simple PTV shapes, we found reduced monitor units (13.47%, p = 0.0009) and control points (8.77%, p = 0.0004) using 2APB planning. In addition, we found a beam delivery time reduction of approximately 25%. In summary, the dosimetric benefit, although moderate, was improved over a 1APB setting for complex PTV, and equivalent in other cases. The overall reduced delivery time suggests that the use of mulitple arcs per beam could lead to reduced patient-on-table time, increased clinical throughput, and reduced medical physics quality assurance effort.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosol properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptive basis sets for practical quantum computing

Electronic structure calculations on small systems such as H 2 , H 2 O, LiH, and BeH 2 with chemical accuracy are still a challenge for the current generation of noisy intermediate-scale quantum (NISQ) devices. One of the reasons is that due to the device limitations, only minimal basis sets are commonly applied in quantum chemical calculations, which allows one to keep the number of qubits employed in the calculations at a minimum. However, the use of minimal basis sets leads to very large errors in the computed molecular energies as well as potential energy surface shapes. One way to increase the accuracy of electronic structure calculations is through the development of small basis sets better suited for quantum computing. In this work, we show that the use of adaptive basis sets, in which exponents and contraction coefficients depend on molecular structure, provides an easy way to dramatically improve the accuracy of quantum chemical calculations without the need to increase the basis set size and thus the number of qubits utilized in quantum circuits. As a proof of principle, we optimize an adaptive minimal basis set for quantum computing calculations on an H 2 molecule, in which exponents and contraction coefficients depend on the H—H distance, and apply it to the generation of H 2 potential energy surface on IBM-Q quantum devices. The adaptive minimal basis set reaches the accuracy of the double-zeta basis sets, thus allowing one to perform double-zeta quality calculations on quantum devices without the need to utilize twice as many qubits in simulations. This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Rapid quantification of whole seed fatty acid amount, composition, and shape phenotypes from diverse oilseed species with large differences in seed size

Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops. High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured. New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗
DOE OSTI2017

A near-stationary subspace for ridge approximation

Response surfaces are common surrogates for expensive computer simulations in engineering analysis. However, the cost of fitting an accurate response surface increases exponentially as the number of model inputs increases, which leaves response surface construction intractable for high-dimensional, nonlinear models. Here we describe ridge approximation for fitting response surfaces in several variables. A ridge function is constant along several directions in its domain, so fitting occurs on the coordinates of a low-dimensional subspace of the input space. We review essential theory for ridge approximation – e.g., the best mean-squared approximation and an optimal low-dimensional subspace – and we prove that the gradient-based active subspace is near-stationary for the least-squares problem that defines an optimal subspace. Motivated by the theory, we propose a computational heuristic that uses an estimated active subspace as an initial guess for a ridge approximation fitting problem. We show a simple example where the heuristic fails, which reveals a type of function for which the proposed approach is inappropriate. We then propose a simple alternating heuristic for fitting a ridge function, and we demonstrate the effectiveness of the active subspace initial guess applied to an airfoil model of drag as a function of its 18 shape parameters.

42 ENGINEERING↗
DOE OSTI2018

Wetting behavior of four polar organic solvents containing one of three lithium salts on a lithium-ion-battery separator

Hypothesis: The wetting behavior of an electrolyte solution on the separator, determined by contact-angle measurements, has a significant effect on the internal resistance of the battery and on its cycle life. The solvent, the lithium-salt type and its concentration may affect the wettability. Yet, few systematic studies address the effect of salt concentration on surface tension and contact angle. Experiments: Surface tensions and advancing contact angles were measured for dimethyl sulfoxide (DMSO), propylene carbonate (PC), dimethyl carbonate (DMC), and a PC/DMC mixture (1:1 mass ratio) with various concentrations of a lithium salt (LiClO 4 , LiPF 6 , and LiTFSI) at 23 °C. Measurements were made by a Krüss Drop Shape Analyzer 100, with a video camera mounted on a microscope to record the drop image. Findings: For DMSO, PC and PC/DMC, surface tensions increase by adding LiClO 4 or LiPF 6 but decrease upon addition of LiTFSI. For DMC, the lithium salts have little impact on the surface tensions. Furthermore, for each solvent, contact angles and adhesion energies follow the same trend as those for surface tensions. The TFSI- anion reduces the surface tension of the solvent, favoring good wettability of the separator. The optimal surface tension for wettability of Celgard 2500 is at or below 26.1 mN/m.

25 ENERGY STORAGE↗
DOE OSTI2025

Design and Implementation of Surface Eroding Thermocouples for Stage-One Modular Divertor Integration at the DIII-D Tokamak

The DIII-D has upgraded its upper divertor to a modular system using copper alloy pedestals to alter the divertor geometry without changing the vessel structure. Six new graphite tiles were designed for the shape and volume rise (SVR) divertor. Here, these new SVR tiles facilitated the formation of a poloidal array of 27 surface eroding thermocouples (SETCs) in the upper divertor region. At one location, a specialized recessed SETC, paired with a standard flush SETC, was installed in one of the ceiling tiles to provide comprehensive heat flux measurements, distinguishing between charged and noncharged particle contributions. Upgrades were made to the SETC system in the small-angle slot (SAS) divertor to improve overall performance. These upgrades included optimizing the feedthrough system to double the thermocouple cable capacity and reallocating cables from the SAS area to the SVR divertor. A compact isolation amplifier system with a fixed gain of 41 was employed to improve the signal level and minimize interference. Additionally, two analog-to-analog fiber systems were implemented for transmitting thermocouple signals over a single fiber, significantly reducing both noise levels and costs. The newly installed SETCs in the SVR divertor successfully completed initial commissioning testing. The SETCs captured the in-out asymmetry in the power distribution between the inner and outer strike points and demonstrated the dependency of the heat flux profile on the outer strike point location. During divertor detachment, heat flux mitigation was noted at the outer strike point location, while significant heat flux contributions from neutral particles were measured in the SVR divertor.

Divertor↗
DOE OSTI2025

Including the vacuum energy in stellarator coil design

Being three-dimensional, stellarators have the advantage that plasma currents are not essential for creating rotational-transform; however, the external current-carrying coils in stellarators can have strong geometrical shaping, which can complicate the construction. Reducing the inter-coil electromagnetic forces acting on strongly shaped 3D coils and the stress on the support structure while preserving the favorable properties of the magnetic field is a design challenge. In this work, we recognize that the inter-coil forces are the gradient of the vacuum magnetic energy. We introduce an objective functional built on the usual quadratic flux on a prescribed target surface together with a weighed penalty on the vacuum energy. The Euler–Lagrange equation for stationary states is derived, and numerical illustrations are computed using a modern stellarator optimization framework. A study of the effect of the energy functional on the inter-coil forces is conducted and the energy is shown to be a promising quantity in producing coils with low forces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Synergy and antagonism in a genome-scale model of metabolic hijacking by bacteriophages

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions, but previous studies mostly inferred their roles from annotations, ignoring system-wide impacts and phage production. Here we integrate AMGs and phage assembly into a genome-scale metabolic model of Prochloroccocus marinus MED4 infected by P-HM2. We show that 17 directly hijacked reactions substantially affect more than 30% of the reactions in MED4 metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis, distinguishing these AMGs as either phage aligned—shifting feasible reaction velocities in accordance with maximal phage production—or phage antialigned. Pareto optimization reveals that phage-aligned reactions alter phage-host growth trade-offs, while phage-antialigned reactions do not. We experimentally validate our predictions of system-level AMG impacts by measuring the N-dependent effect of P-HM2 cp12 expression on growth in a model relative of the genetically intractable MED4, Synechococcus elongatus. We also show that AMGs’ indirect impacts are synergistically and antagonistically coupled, providing systems-level insight into AMG perturbations and highlighting how nontrivial cascading effects shape host metabolism.

Rozum, Jordan C. [Pacific Northwest National Labor↗
DOE OSTI2018

Light charged ion measurements and Monte Carlo calculations from thick targets bombarded by protons and heavy ions - 25435

The data presented in this work are from experiments performed in November 2016 and November 2017 at the National Aeronautics and Space Administration (NASA) Space Radiation Laboratory at Brookhaven National Laboratory (BNL). The secondary light charged ions (Z=1 and 2 ions) and neutrons produced by 400-, 800-, and 2500-AMeV protons and 400-, 800-, and 1500-AMeV He, C, Si, Fe ions bombarding thick targets (20, 40, and 60 g/cm{sup 2} aluminum and high-density polyethylene (HDPE), and 20 and 60 g/cm{sup 2} combination of aluminum and HDPE) were measured with organic liquid scintillators and sodium iodide (NaI) detectors. The organic liquid scintillators were positioned at 10 deg., 30 deg., 45 deg., 60 deg., 80 deg., and 135 deg. with respect to the beam direction, while NaI detectors were placed at 10 deg. and 30 deg. off the beam axis. The double-differential thick-target secondary light charged ion yields measured by organic liquid scintillators are of main interest here. In the analysis, after applying pulse shape discrimination to separate protons, deuterons, tritons, helium-3, and helium-4 particles, the time of flight technique was used to produce the secondary charged particle angular-energy spectra. Additionally, the spectra were compared with simulation results using the Monte Carlo simulation code, PHITS version 2.88. To obtain favorable statistics in PHITS calculations, ring-type detectors were modeled, which have the same distances and equivalent angles with the experimental setup. These experimental data will be integrated into the uncertainty analysis for NASA to optimize future GCR shielding design. (authors)

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Metasurface-based multi-harmonic free-electron light source

Metasurfaces are subwavelength spatial variations in geometry and material where the structures are of negligible thickness compared to the wavelength of light and are optimized for far-field applications, such as controlling the wavefronts of electromagnetic waves. Here, we investigate the potential of the metasurface near-field profile, generated by an incident few-cycle pulse laser, to facilitate the generation of high-frequency light from free electrons. In particular, the metasurface near-field contains higher-order spatial harmonics that can be leveraged to generate multiple higher-harmonic X-ray frequency peaks. We show that the X-ray spectral profile can be arbitrarily shaped by controlling the metasurface geometry, the electron energy, and the incidence angle of the laser input. Using ab initio simulations, we predict bright and monoenergetic X-rays, achieving energies of 30 keV (with harmonics spaced by 3 keV) from 5-MeV electrons using 3.4-eV plasmon polaritons on a metasurface with a period of 85 nm. As an example, we present the design of a four-color X-ray source, a potential candidate for tabletop multicolor hard X-ray spectroscopy. Our developments could help pave the way for compact multi-harmonic sources of high-energy photons, which have potential applications in industry, medicine, and the fundamental sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Control of the diocotron instability of a hollow electron beam with periodic dipole magnets

A method to control the diocotron instability of a hollow electron beam with peri-odic dipole magnetic fields has been investigated by a two-dimensional particle-in-cell simulation. At first, relations between the diocotron instability and several physical parameters such as the electron number density, current and shape of the electron beam, and the solenoidal field strength are theoretically analyzed without periodic dipole magnetic fields. Then, we study the effects of the periodic dipole magnetic fields on the diocotron instability using the two-dimensional particle-in-cell simulation. In the simulation, we considered the periodic dipole magnetic field applied along the propagation direction of the beam, as a temporally varying magnetic field in the beam frame. Lastly, a stabilizing effect is observed when the oscillating frequency of the dipole magnetic field is optimally chosen, which increases with the increasing amplitude of the dipole magnetic field.

43 PARTICLE ACCELERATORS↗