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西安电子科技大学电子工程学院,陕西西安 710071
Received:12 May 2022,
Revised:2022-08-12,
Published:25 January 2024
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何瑶,徐乐,李蕊,等.基于BLT计算复杂腔体结构屏蔽效能的拓展方法[J].电子学报,2024,52(01):258-263.
HE Yao, XU Le, LI Rui, et al.An Extended Method for Calculating Shielding Effectiveness of Complex Cavity Structures Based on BLT[J].Acta Electronica Sinica, 2024, 52(01): 258-263.
何瑶,徐乐,李蕊,等.基于BLT计算复杂腔体结构屏蔽效能的拓展方法[J].电子学报,2024,52(01):258-263. DOI:10.12263/DZXB.20220535
HE Yao, XU Le, LI Rui, et al.An Extended Method for Calculating Shielding Effectiveness of Complex Cavity Structures Based on BLT[J].Acta Electronica Sinica, 2024, 52(01): 258-263. DOI:10.12263/DZXB.20220535
本文通过拓展BLT(Baum-Liu-Tesche)方法,计算了复杂结构腔体的屏蔽效能(Shielding Effectiveness,SE).BLT方法最初被用于分析传输线模型,后被拓展到计算腔体屏蔽效能,然而其局限于计算矩形腔体和圆柱腔体的屏蔽效能,无法分析更加复杂的腔体结构.本文将BLT方法与数值方法相结合,同时利用机器学习的回归方法,将其拓展到复杂腔体结构的屏蔽效能计算中.本文首先计算了腔体二维截面的特征模;然后,基于电磁波传播规律建立了等效电路网络结构,结合BLT方法获得腔体屏蔽效能的频域响应分布;最后,利用机器学习方法训练支持向量回归模型(Support Vector Regression,SVR),修正频域响应幅值,得到腔体目标的屏蔽效能.本文采用了3种不同结构的腔体对提出的方法进行了验证,频率范围为0.1~3 GHz.以商业软件仿真得到的结果为标准,本文提出的方法可以在85%的置信度条件下,实现均方根误差小于3,同时计算速度相比商业软件提升80倍以上.
In this article
by extending the BLT (Baum-Liu-Tesche) method
the shielding effectiveness (SE) of cavities with complex structures is calculated. The BLT method was originally used to analyze the transmission line model and was later extended to calculate the shielding effectiveness of the cavity. However
it is limited to calculating the shielding effectiveness of the rectangular cavity or cylindrical cavity
and cannot analyze more complex cavity structures. Combined with the numerical method
and using the regression method of machine learning
it is extended to the shielding effectiveness calculation of complex cavity structures. The eigenmodes of the two-dimensional section of the cavity are firstly calculated; then an equivalent circuit network structure is established based on the law of electromagnetic wave propagation
and the frequency domain response distribution of the shielding effectiveness of the cavity is obtained by combining the BLT method; finally
the machine learning method is used to train the SVR (Support Vector Regression) model
modify the frequency domain response amplitude
and obtain the shielding effectiveness of the cavity target. In this paper
three cavities with different structures are used to verify the method proposed
and the frequency range is 0.1~3 GHz. Taking the results obtained by commercial software simulation as the standard
the method proposed in this paper can achieve a root mean square error of less than 3 under the condition of 85% confidence
and the calculation speed is over 80 times higher than that of commercial software.
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