arXiv subjects

Zhijun Zhou

Publications and source records attributed to Zhijun Zhou.

6 recordsLinked to original sources

arXiv2026

AlgoPlasma: Open Algorithms for Plasma Modeling

AlgoPlasma is an open-source library in which core numerical algorithms for plasma modeling are implemented as modular, well-documented, and independently testable components. Rather than offering a complete simulation code, it allows researchers to select, adapt, and assemble the required components into application-specific workflows. The current release is centered on particle-based simulation, while AlgoPlasma is designed to encompass a broader range of approaches to plasma modeling. It provides components for particle initialization and advancement, particle--grid coupling, field solution, collision modeling, parallel data exchange, input/output, and selected fluid updates. Documentation links mathematical formulations to source implementations, interfaces, and usage, while verification and validation cases evaluate numerical accuracy and physical behavior. AlgoPlasma thus establishes a shared algorithmic foundation for plasma modeling, transforming repeatedly reimplemented numerical methods into open, reusable, tested, and explainable components for research, verification, education, and collaborative development.

physics.plasm-ph↗

arXiv2026

Conservative Charge and Current Deposition on Nonuniform 3D Cylindrical PIC Meshes with Residual Self-Field Diagnostics

Particle-in-cell simulations on nonuniform cylindrical meshes require deposition schemes that respect cylindrical metrics while controlling numerical self-fields. This work develops cylindrical-volume-weighted charge and current deposition using nodal control volumes and swept-volume factors on logically structured stretched grids. Uniform-density and controlled-transport tests demonstrate accurate charge recovery, well-controlled current-density errors, and continuity residuals substantially lower than the corresponding current-density errors. Single-particle diagnostics show that charge-transport consistency alone does not ensure self-field cancellation; face-centered electric fields give the smallest residual, whereas cell-centered and shifted cell-centered layouts produce larger residuals.

physics.plasm-ph↗

arXiv2025

The effect of ion rotational flow on Hall thruster azimuthal instability via two dimensional PIC simulations

Previous experimental studies have found that the neutral gas rotational flow in the opposite direction of electron Hall drift can lead to better experimental results comparing to the same direction. In Hall thrusters, the core factor influencing operational states is the electron cross field transport, where the azimuthal instability serves as a key mechanism. The rotational flow of neutral gas may affect instability by altering initial azimuthal velocity of ions, which has not been investigated before. Therefore, to study the effects of ion rotational flow of varying magnitudes and directions on azimuthal instability, simulations are conducted in this work based on two benchmark particle-in-cell (PIC) cases: the azimuthal-axial and the azimuthal-radial. The results indicate that the ion rotational flow velocity can potentially complicate the coupling characteristics of the electron cyclotron drifting instability and the modified two stream instability, particularly when a reverse rotational flow velocity is added. In general, both co-directional and reverse ion rotational flow have been observed to inhibit azimuthal instability, which results in a decrease in axial electron mobility. A 1% addition of the ion rotational flow (compared to the electron drift) would result in a 10% change of the electron mobility due to varied azimuthal instability, and the decrease in electron mobility of the reverse ion rotational flow is greater than that of co-directional. In addition, detailed spectral analyses are carried out to study the relation between ECDI, MTSI, and resonant wave-wave interactions.

physics.plasm-ph↗

arXiv2025

Impact of Azimuthal Magnetic Field Inhomogeneity on Hall Thruster high-frequency azimuthal instability via 2D radial-azimuthal PIC simulations

For the SPT-type Hall thrusters, the magnetic structure with magnetic conductive columns leads inherent to azimuthally inhomogeneous magnetic configurations. This azimuthal magnetic inhomogeneity may impact electron azimuthal closed-drift motion and cross-field transport characteristics. This study systematically investigates the effects of azimuthal magnetic field gradient on high frequency azimuthal instability and associated anomalous electron transport through 2D radial-azimuthal Particle-in-Cell (PIC) simulations. The results reveal dual mechanisms of magnetic inhomogeneity on electron cyclotron drift instability (ECDI) characteristics: (1) The azimuthal drift velocity distribution becomes modulated by the magnetic field inhomogeneity, with increased average drift velocity enhancing ECDI intensity under stronger inhomogeneity; (2) Simultaneously, the ECDI wavenumber spectrum broadens with elevated magnetic inhomogeneity, reducing discrete ECDI spectral peaks. Under the dual influence of magnetic field inhomogeneity, when the inhomogeneity level is below 5%, the ECDI saturation amplitude and electron cross field mobility remains largely unchanged. However, a notable reduction of 13.4% in ECDI saturation intensity and a 15.7% decrease in electron mobility are observed when magnetic field inhomogeneity reaches 10%.

physics.plasm-ph↗

arXiv2020

Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis

During training phase, more connections (e.g. channel concatenation in last layer of DenseNet) means more occupied GPU memory and lower GPU utilization, requiring more training time. The increase of training time is also not conducive to launch application of SR algorithms. This's why we abandoned DenseNet as basic network. Futhermore, we abandoned this paper due to its limitation only applied on medical images. Please view our lastest work applied on general images at arXiv:1911.03464.

eess.IV↗

arXiv2019

Medical image super-resolution method based on dense blended attention network

In order to address the issue that medical image would suffer from severe blurring caused by the lack of high-frequency details in the process of image super-resolution reconstruction, a novel medical image super-resolution method based on dense neural network and blended attention mechanism is proposed. The proposed method adds blended attention blocks to dense neural network(DenseNet), so that the neural network can concentrate more attention to the regions and channels with sufficient high-frequency details. Batch normalization layers are removed to avoid loss of high-frequency texture details. Final obtained high resolution medical image are obtained using deconvolutional layers at the very end of the network as up-sampling operators. Experimental results show that the proposed method has an improvement of 0.05db to 11.25dB and 0.6% to 14.04% on the peak signal-to-noise ratio(PSNR) metric and structural similarity index(SSIM) metric, respectively, compared with the mainstream image super-resolution methods. This work provides a new idea for theoretical studies of medical image super-resolution reconstruction.

cs.CV↗