Achieving Broadband RCS Reduction on Curved Surfaces Through Gradient Unit Design and Partition Layout Strategy
THIN-WALLED STRUCTURES(2025)
Rocket Force Univ Engn
Abstract
Novel metastructure designs can efficiently manipulate electromagnetic responses. However, achieving exceptional electromagnetic manipulation capabilities in conformal states remains challenging. Herein, we proposed an innovative method that integrates gradient units with the partition layout strategy to facilitate broadband radar cross section (RCS) reduction under different curvatures. The proposed metastructure demonstrates broadband impedance matching characteristics through a gradient multi-layer design and exhibits strong adaptability to complex structures owing to the application of additive manufacturing technology. It demonstrates a remarkable-10 dB RCS reduction across a wide frequency bandwidth of 5.4-18 GHz in its planar state by integrating the micro-material composition and macro-geometry design while maintaining RCS reduction within the central angle range up to 90 degrees. This paper presents a straightforward and innovative approach for constructing conformal metastructures, highlighting their potential applications in both military and civilian domains.
MoreTranslated text
Key words
Gradient metastructure,Conformal curved surface,Partition layout strategy,3D printing
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined