Automated Author ProfileV. O. Poluboyartsev
V. O. Poluboyartsev
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
"This paper explores the application of the VPython library for simulating klystrons microwave devices that utilize dynamic electron beam control. The study investigates VPython\u2019s capabilities in visualizing electron motion under the influence of both microwave and static electric fields. Key processes examined include velocity modulation, electron bunching, and induced current generation. A virtual laboratory setup for studying klystrons was developed, incorporating interactive controls such as sliders, buttons, and checkboxes for adjusting voltages, drift length, and particle count. This approach enhances the understanding of the physical processes occurring within a klystron. Realtime plotting of velocity, voltage, and current illustrates the system\u2019s dynamics. The advantages of VPython, such as ease of physical modeling and trajectory visualization, are analyzed. Limitations, including simplified spacecharge effects and scaling discrepancies, are discussed. Future improvements could incorporate Coulomb interactions and resonator losses while optimizing performance for large particle counts. The study concludes that VPython is effective for educational purposes and preliminary klystron analysis. However, high-precision simulations require specialized tools like CST Studio or COMSOL."
Authors
- A. I. Nesterenko ;
- V. O. Poluboyartsev ;
- A. A. Eskov ;
- A. V. Lukyanchikov