SHAO Jingxuan, TIAN Ge, HAN Jing, CHANG Liang, CAO Yi, LUO Yanlin. Visualization and Analysis of the Volume Dataset for Time-Varing Electromagnetic Simulation[J]. Chinese Journal of Electronics, 2020, 29(5): 973-981. doi: 10.1049/cje.2020.08.014
Citation: SHAO Jingxuan, TIAN Ge, HAN Jing, CHANG Liang, CAO Yi, LUO Yanlin. Visualization and Analysis of the Volume Dataset for Time-Varing Electromagnetic Simulation[J]. Chinese Journal of Electronics, 2020, 29(5): 973-981. doi: 10.1049/cje.2020.08.014

Visualization and Analysis of the Volume Dataset for Time-Varing Electromagnetic Simulation

doi: 10.1049/cje.2020.08.014
Funds:  This work is supported by the National Natural Science Foundation of China (No.61977063).
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  • Corresponding author: LUO Yanlin (corresponding author) received the Ph.D. degree in applied mathematics from Zhejiang University in 1997. Now she is an associate professor in the College of Artificial Intelligence at Beijing Normal University. Her major fields included data visualization, virtual reality and computer graphics. (Email:luoyl@bnu.edu.cn)
  • Received Date: 2020-01-12
  • Rev Recd Date: 2020-06-27
  • Publish Date: 2020-09-10
  • The three Dimensional (3D) visualization of time-varying Electromagnetic (EM) data in antenna devices can be used to evaluate various parameters, such as the quality of beams and the power performance of antennas, for optimizing the antenna design. Compared with the single volume data, the time-varying data has characteristics of strong continuity, large scale and multi-variable. It is challenging. We proposed a real-time dynamic rendering method using Compute unified device architecture (CUDA)-based volume rendering algorithm. We designed the opacity transfer function as multiple trapezoid, which represents each energy band, and provided a fusion strategy for a horn-like surface and a EM volume model that considers the contribution of both intersection points of surface with viewing ray and sample points of the volume. We also proposed a framework for time-varying 3D EM datasets. Two assessment tests were conducted by both experts and students. Results showed that our 3D visualization method were effective.
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