arXiv subjects

Shengduo Liu

Publications and source records attributed to Shengduo Liu.

4 recordsLinked to original sources

arXiv2026

Optimized design of a Penning ion source for sealed neutron tube

Sealed neutron tubes have a wide range of applications, and the ion source is their core component. Penning ion sources commonly suffer from issues such as uneven magnetic field distribution and a low proportion of monoatomic ions. Improving the performance of the ion source can effectively address the problems of low neutron flux and short operational lifespan. This study aims to optimise the magnetic field configuration and discharge parameters of the ion source, thereby increasing the proportion of monoatomic and enhancing discharge stability, and to provide a design basis for high-performance sealed neutron tubes. Develop a magnetic field-plasma coupling model to compare and analyze the magnetic field distribution patterns of traditional magnetic block structures and soft iron-reinforced structures, and investigate the mechanisms by which operating pressure and anode voltage affect plasma density and ion composition using COMSOL multiphysics simulation methods. Simulation results indicate that the soft iron structure significantly enhances the axial magnetic field strength and uniformity within the discharge region; under conditions of 0.06 Pa gas pressure and 1500 V anode voltage, the proportion of monoatomic ions increased from the conventional 9% to 30%.

physics.plasm-ph↗

arXiv2026

A Method for Neutron-Gamma Pulse Shape Discrimination of CLYC Detector Based on a Gated Residual-Linear Attention Network

The discrimination of neutron and gamma pulse shapes is a key technology in fields such as nuclear safety monitoring and radiation assessment. An enhanced recursive gated cyclic residual-sparse linear attention network is developed on the CLYC detector experimental platform to overcome weak noise resistance, limited feature extraction and inferior real-time performance of conventional algorithms. The experimental dataset comprises 19,971 samples, which were pre-processed and stratified for model training and testing. Results indicate that the proposed algorithm achieves a quality factor of 2.2, with a classification accuracy of 98.7% and a recall rate of 99.4%. It achieves an accuracy of 95.1% under the 20 dB low signal-to-noise ratio condition, exhibiting excellent anti-noise ability.With around 2.8 million parameters, the model takes merely 0.05 ms to process a single pulse on GPU, satisfying real-time monitoring and embedded deployment demands.

physics.ins-det↗

arXiv2026

Compatibility and Accuracy Verification of CADmesh-Based Complex Geometry Modeling in Geant4

Geant4 Monte Carlo simulation relies on the Constructive Solid Geometry (CSG) method for complex geometric modeling. This method has low efficiency and a high application threshold. Importing triangular facet formats such as STL/OBJ via CADmesh is a promising alternative, but systematic evaluations of format compatibility, geometric accuracy, and physical simulation deviations are lacking. Construct open-source experimental environment based on Geant4 11.0, CADmesh 1.3.0 and FreeCAD 1.0. We design high and low precision gradient test cases using simple geometric bodies and complex engineering models, and systematically evaluate the import success rate, facet loss rate, volume error, and particle transport dose deviation for STL and OBJ formats.The results show a 100% import success rate for both formats; the volume error rate is <= 0.018% for high-precision models and <= 0.288% for low-precision models. The two formats share the same vertex facet data structure. This study designs a general adaptive interface. The interface reduces the number of parsing code lines by about 70% and maintains geometric accuracy.Furthermore, the tetrahedral mesh loading takes 3.1 times longer than tessellated solids, but the simulation time can be reduced from 15194.3 s to 77.28 s.

cs.GR↗

arXiv2025

Learning a potential formulation for rate-and-state friction

Empirical rate-and-state friction laws are widely used in geophysics and engineering to simulate interface slip. They postulate that the friction coefficient depends on the local slip rate and a state variable that reflects the history of slip. Depending on the parameters, rate-and-state friction can be either rate-strengthening, leading to steady slip, or rate-weakening, leading to unsteady stick-slip behavior modeling earthquakes. Rate-and-state friction does not have a potential or variational formulation, making implicit solution approaches difficult and implementation numerically expensive. In this work, we propose a potential formulation for the rate-and-state friction. We formulate the potentials as neural networks and train them so that the resulting behavior emulates the empirical rate-and-state friction. We show that this potential formulation enables implicit time discretization leading to efficient numerical implementation.

cond-mat.mes-hall↗