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080904 电磁场与微波技术
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08 工学
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       本文利用四阶同轴腔腔体滤波器来验证数据驱动的替代模型优化算法。文中提出的算法与直接优化(PSO)相比,优化结果相近,优化时间减少97%,且具有较好的稳定性。利用基于仿表面等离子激元(SSPP)的阵列天线来验证物理驱动的替代模型优化算法以及多替代模型辅助的优化算法。实验结果显示前者实现了24GHz 下的指定波束方向,后者解决了多设计目标的天线阵列设计问题,如实现了在26-28GHz 时,副瓣水平接近于-20dB 等。

Other Abstract

      With the advancement of modern communication technology, the performance requirements of RF microwave devices such as microwave filters and antennas are increasingly stringent. Due to a large number of design parameters and time-consuming electromagnetic simulation, etc., design optimization of these devices is difficult. Surrogatebased optimization methods are becoming popular in the field of RF microwave device design. These methods exploit low computational cost surrogate models to guide highfidelity electromagnetic models to achieve fast optimization. These methods are essential for solving the complex RF microwave devices design problems.
        The thesis proposes a multi-surrogate model-assisted optimization approach. Firstly, a data-driven surrogate model is introduced to assist the design of microwave filters. After extracting the response features of microwave filters using vector fitting techniques, the surrogate model is established based on neural networks. The optimal design of the filter is obtained using the Harris Hawks optimization algorithm. Secondly, a physics-based surrogate-assisted antenna beam direction design optimization method is introduced. The optimization is carried out using the space mapping technique. The space mapping technique leverages the physics-based surrogate, namely an antenna array factor formula as
a “coarse model”. The electromagnetic simulation model is a “fine model”. Finally, the data-driven and physics-based surrogate model-assisted optimization methods are combined to form the multi-surrogate-assisted optimization algorithm framework.
        A fourth-order coaxial cavity filter is used to validate the data-driven surrogate model optimization algorithm. The results show that the optimization results of the proposed algorithm are similar to direct  optimization (PSO), but with 97% reduction in optimization time and better stability. A spoof surface plasmon polariton (SSPP) array antenna is used to validate the physics-based surrogate model and the multi-surrogate-assisted optimization algorithm. The results show that the former achieves the specified beam direction at 24 GHz, and the latter solves the antenna array design problem with multiple design
objectives, such as achieving a side lobe level close to -20 dB at 26-28 GHz.

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References List

[1] 范国亮. 5G 移动通信发展现状对传输网络的需求[J]. 中国新通信, 2017, 19(20): 1.
[2] CHOWDHURY M Z, SHAHJALAL M, AHMED S, et al. 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions[J]. IEEE Open Journal of the Communications Society, 2020, PP(99): 1-1.
[3] 苏芬芳. 微波通信的主要技术与应用价值探讨[J]. 中国新通信, 2018, 20(17): 1.
[4] WANG X, KONG L, KONG F, et al. Millimeter wave communication: A comprehensive survey[J]. IEEE Communications Surveys Tutorials, 2018, 20(3): 1616-1653.
[5] SÓBESTER A, FORRESTER A I. Geometry parameterization: Philosophy and practice[M]. John Wiley & Sons, 2014: 275-275.
[6] KOZIEL S, OGURTSOV S. Multilevel microwave design optimization with automated model fidelity adjustment[J]. International Journal of RF and Microwave Computer-Aided Engineering, 2014, 24(3): 281-288.
[7] QUEIPO N V, HAFTKA R T, SHYY W, et al. Surrogate-based analysis and optimization[J]. Progress in Aerospace Sciences, 2005.
[8] STYBLINSKI M, OPALSKI L J. Algorithms and software tools for IC yield optimization based on fundamental fabrication parameters[J]. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 1986, 5(1): 79-89.
[9] COUCKUYT I, FORRESTER A, GORISSEN D, et al. Blind Kriging: Implementation and performance analysis[J]. Advances in Engineering Software, 2012, 49: 1-13.
[10] HOSDER S. Stochastic response surfaces based on non-intrusive polynomial chaos for uncertainty quantification[J]. International Journal of Mathematical Modelling and Numerical Optimisation, 2012, 3(1-2): 117-139.
[11] YANG X S. Engineering optimization: An introduction with metaheuristic applications[M]. John Wiley & Sons, 2010.
[12] JORGE N, STEPHEN J W. Numerical Optimization[M]. Spinger, 2006.
[13] KOLDA T G, LEWIS R M, TORCZON V. Optimization by direct search: New perspectives on some classical and modern methods[J]. SIAM review, 2003, 45(3): 385-482.
[14] SASTRY K, GOLDBERG D, KENDALL G. Genetic algorithms[M]. Springer, 2005: 97-125.
[15] BäCK T, FOGEL D B, MICHALEWICZ Z. Evolutionary computation 1: Basic algorithms and operators[M]. CRC Press, 2018.
[16] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66.
[17] KENNEDY J. The particle swarm: social adaptation of knowledge[C]//Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC’97). IEEE, 1997: 303-308.
[18] DIRECTOR S, ROHRER R. The generalized adjoint network and network sensitivities[J]. IEEE Transactions on Circuit Theory, 1969, 16(3): 318-323.
[19] PIRONNEAU O. Optimal shape design for elliptic systems[M]. Springer, 1982: 42-66.
[20] JAMESON A. Aerodynamic design via control theory[J]. Journal of Scientific Computing, 1988, 3(3): 233-260.
[21] PAPADIMITRIOU D I, GIANNAKOGLOU K C. Aerodynamic shape optimization using first and second order adjoint and direct approaches[J]. Archives of Computational Methods in Engineering, 2008, 15(4): 447-488.
[22] PALACIOS F, COLONNO M R, ARANAKE A C, et al. Stanford University Unstructured (SU2): An open-source integrated computational environment for multi-physics simulation and design[J]. AIAA paper, 2013, 287: 2013.
[23] FORRESTER A I, KEANE A J. Recent advances in surrogate-based optimization[J]. Progress in Aerospace Sciences, 2009, 45(1-3): 50-79.
[24] SIMPSON T W, POPLINSKI J, KOCH P N, et al. Metamodels for computer-based engineering design: survey and recommendations[J]. Engineering with Computers, 2001, 17(2): 129-150.
[25] PIETRENKO-DABROWSKA A, KOZIEL S. Accelerated antenna optimization using design database and Kriging surrogates[C]//2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting. IEEE, 2020: 2061-2062.
[26] JACOBS J P, DU PLESSIS W P. Efficient modeling of missile RCS magnitude responses by Gaussian processes[J]. IEEE Antennas and Wireless Propagation Letters, 2017, 16: 3228-3231.
[27] ZHANG Z, CHENG Q S, CHEN H, et al. An efficient hybrid sampling method for neural network-based microwave component modeling and optimization[J]. IEEE Microwave and Wireless Components Letters, 2020, 30(7): 625-628.
[28] ZHANG J W, YE S B, LIU H, et al. Filtering out antenna effects from GPR data by an RBF neural network[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9): 1378-1382.
[29] ZHANG Z, CHEN H, YU Y, et al. Yield-constrained optimization design using polynomial chaos for microwave filters[J]. IEEE Access, 2021, 9: 22408-22416.
[30] 刘璨. 微波滤波器的建模与调试策略设计[D]. 武汉: 中国地质大学, 2019.
[31] DING X, DEVABHAKTUNI V, CHATTARAJ B, et al. Neural-network approachesto electromagnetic-based modeling of passive components and their applications to highfrequency and high-speed nonlinear circuit optimization[J]. IEEE Transactions on Microwave Theory and Techniques, 2004, 52(1): 436-449.
[32] MACCHIARELLA G, TRAINA D. A formulation of the Cauchy method suitable for the synthesis of lossless circuit models of microwave filters from lossy measurements[J]. IEEE Microwave and Wireless Components Letters, 2006, 16(5): 243-245.
[33] FENG F, ZHANG C, MA J, et al. Parametric modeling of EM behavior of microwave components using combined neural networks and pole-residue-based transfer functions[J]. IEEE Transactions on Microwave Theory and Techniques, 2016, 64(1): 60-77.
[34] ZHAO P, WU K L. Model-based vector-fitting method for circuit model extraction of coupledresonator diplexers[J]. IEEE Transactions on Microwave Theory and Techniques, 2016, 64(6): 1787-1797.
[35] HE Y, WANG G, SONG X, et al. A coupling matrix and admittance function synthesis for mixed topology filters[J]. IEEE Transactions on Microwave Theory and Techniques, 2016, 64 (12): 4444-4454.
[36] BANDLER J W, CHENG Q S, DAKROURY S A, et al. Space mapping: the state of the art[J]. IEEE Transactions on Microwave Theory and Techniques, 2004, 52(1): 337-361.
[37] KOZIEL S, OGURTSOV S. Antenna design by simulation-driven optimization[M]. Cham: Springer International Publishing, 2014: 1-4.
[38] LEIFSSON L, KOZIEL S, KURGAN P. Automated low-fidelity model setup for surrogatebased aerodynamic optimization[M]//Solving Computationally Expensive Engineering Problems. Springer, 2014: 87-111.
[39] LEIFSSON L T, KOZIEL S, HOSDER S, et al. Physics-based multi-fidelity surrogate modeling with entropy-based availability methods[C]//AIAA Modeling and Simulation Technologies Conference. 2014: 0473.
[40] KOZIEL S, OGURTSOV S, COUCKUYT I, et al. Variable-fidelity electromagnetic simulations and co-kriging for accurate modeling of antennas[J]. IEEE Transactions on Antennas and Propagation, 2012, 61(3): 1301-1308.
[41] ALEXANDROV N M, LEWIS R M. An overview of first-order model management for engineering optimization[J]. Optimization and Engineering, 2001, 2(4): 413-430.
[42] TOROPOV V V. Simulation approach to structural optimization[J]. Structural Optimization, 1989, 1(1): 37-46.
[43] ECHEVERRĭA D, HEMKER P W. Space mapping and defect correction[J]. Computational Methods in Applied Mathematics, 2005, 5(2): 107-136.
[44] KOZIEL S, BANDLER J W, MADSEN K. Space mapping with adaptive response correction for microwave design optimization[J]. IEEE Transactions on Microwave Theory and Techniques, 2009, 57(2): 478-486.
[45] KOZIEL S. Shape-preserving response prediction for microwave design optimization[J]. IEEE Transactions on Microwave Theory and Techniques, 2010, 58(11): 2829-2837.
[46] WU K L, ZHANG R, EHLERT M, et al. An explicit knowledge-embedded space mapping technique and its application to optimization of LTCC RF passive circuits[J]. IEEE Transactions on Components and Packaging Technologies, 2003, 26(2): 399-406.
[47] SONG Y, CHENG Q S, KOZIEL S. Multi-fidelity local surrogate model for computationally efficient microwave component design optimization[J]. Sensors, 2019, 19(13): 3023.
[48] XIAO L Y, SHAO W, JIN F L, et al. Multiparameter modeling with ANN for antenna design [J]. IEEE Transactions on Antennas and Propagation, 2018, 66(7): 3718-3723.
[49] LIU B, AKINSOLU M O, SONG C, et al. An efficient method for complex antenna design based on a self adaptive surrogate model-assisted optimization technique[J]. IEEE Transactions on Antennas and Propagation, 2021, 69(4): 2302-2315.
[50] 宋怡然. 基于替代模型的微波器件优化方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.
[51] CHENG Q S, KOZIEL S, BANDLER J W. Simplified space-mapping approach to enhancement of microwave device models[J]. International Journal of RF and Microwave Computer-Aided Engineering, 2006, 16(5): 518-535.
[52] MCKAY M D, BECKMAN R J, CONOVER W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics, 2000, 42(1): 55-61.
[53] GIUNTA A, WOJTKIEWICZ S, ELDRED M. Overview of modern design of experiments methods for computational simulations[C]//41st Aerospace Sciences Meeting and Exhibit. 2003: 649.
[54] GUSTAVSEN B, SEMLYEN A. Rational approximation of frequency domain responses by vector fitting[J]. IEEE Transactions on Power Delivery, 1999, 14(3): 1052-1061.
[55] 向珈林. 广角AVO 有理函数表征方法研究[D]. 北京: 中国石油大学, 2019.
[56] KOZIEL S, LEIFSSON L. Simulation-driven design by knowledge-based response correction techniques[M]. Springer, 2016.
[57] STORN R, PRICE K. Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
[58] 汤安迪, 韩统, 徐登武, 等. 混沌精英哈里斯鹰优化算法[J]. 计算机应用, 2021, 41(8): 8.
[59] BANDLER J W, BIERNACKI R M, CHEN S H, et al. Space mapping technique for electromagnetic optimization[J]. IEEE Transactions on Microwave Theory and Techniques, 1994, 42 (12): 2536-2544.
[60] 吴生彪. 微波腔体滤波器建模与计算机调试策略[D]. 武汉: 中国地质大学, 2020.
[61] VALLOZZI L, HERTLEER C, ROGIER H. Latest developments in the field of textile antennas[M]. Elsevier, 2016: 599-626.
[62] 克劳斯. 天线: 第三版[M]. 北京: 电子工业出版社, 2011.
[63] WANG X, KONG L, KONG F, et al. Millimeter wave communication: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 1616-1653.
[64] COMITE D, PODILCHAK S K, BACCARELLI P, et al. Analysis and design of a compact leaky-wave antenna for wide-band broadside radiation[J]. Scientific Reports, 2018, 8(1): 1-14.
[65] BUI C D, NGUYEN-TRONG N, NGUYEN T K. A planar dual-band and dual-sense circularly polarized microstrip patch leaky-wave antenna[J]. IEEE Antennas and Wireless Propagation Letters, 2020, 19(12): 2162-2166.
[66] LIU J, JACKSON D R, LONG Y. Substrate integrated waveguide (SIW) leaky-wave antenna with transverse slots[J]. IEEE Transactions on Antennas and Propagation, 2011, 60(1): 20-29.
[67] SÁNCHEZ-ESCUDEROS D, FERRANDO-BATALLER M, HERRANZ J I, et al. Periodicleaky-wave antenna on planar Goubau line at millimeter-wave frequencies[J]. IEEE Antennas and Wireless Propagation Letters, 2013, 12: 1006-1009.
[68] MINATTI G, CAMINITA F, CASALETTI M, et al. Spiral leaky-wave antennas based on modulated surface impedance[J]. IEEE Transactions on Antennas and Propagation, 2011, 59(12): 4436-4444.
[69] ZHANG H C, HE P H, TANG W X, et al. Planar spoof SPP transmission lines: Applications in microwave circuits[J]. IEEE Microwave Magazine, 2019, 20(11): 73-91.
[70] WEI D, LI J, YANG J, et al. Wide-scanning-angle leaky-wave array antenna based on microstrip SSPPs-TL[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17(8): 1566-1570.
[71] YIN J Y, REN J, ZHANG Q, et al. Frequency-controlled broad-angle beam scanning of patch array fed by spoof surface plasmon polaritons[J]. IEEE Transactions on Antennas and Propagation, 2016, 64(12): 5181-5189.
[72] YU H W, JIAO Y C, ZHANG C, et al. Dual-linearly polarized leaky-wave patch array with low cross-polarization levels using symmetrical spoof surface plasmon polariton lines[J]. IEEE Transactions on Antennas and Propagation, 2020, 69(3): 1781-1786.
[73] ABDEL-WAHAB W M, BUSUIOC D, SAFAVI-NAEINI S. Millimeter-wave high radiation efficiency planar waveguide series-fed dielectric resonator antenna (DRA) array: analysis, design, and measurements[J]. IEEE Transactions on Antennas and Propagation, 2011, 59(8): 2834-2843.

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焦亚茜. 多替代模型辅助的微波器件优化方法研究[D]. 深圳. 南方科技大学,2022.
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