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复合形法在PDP计算机仿真中的应用 被引量:1

Application of Complex Method in Computer Simulation of Plasma Display Panel
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摘要 采用复合形算法建立数学模型,以PDP三维模拟仿真软件为基础,以PDP放电效率为目标函数,以扫描电极宽度、氙气比例、气体压强和驱动电压为搜索变量,进行最优化设计来提高单元的放电效率。模拟结果表明:搜索变量坐标为(80,15,498,230)时,即扫描电极宽度、氙气比例、气体压强及维持电压分别为80μm、15%、6.6×104Pa和230 V时,对应的放电效率取得最大值11.0%,比搜索变量坐标为(160,5,310,210)初始复合形时的放电效率提高一倍多,且真空紫外辐射能量也比初始复合形时增加79%。模拟结果表明采用复合形算法可以得出影响PDP放电性能的各因素的最优化组合,使PDP单元的放电效率和放电强度都大幅提高。 Due to complex function relation between discharge characteristics and relative parameters,the complex method is used to optimize the performance of PDP.Numerical model based on complex method is set in the article,where the discharge characteristics are computed by the three-dimensional PDP simulation software.The discharge efficiency is the objective function.The scan electrode width,the xenon content,the gas pressure and the sustain voltage are the search variables in the model.The simulation results indicate that the maximum discharge efficiency is 11% when the search variables are(80,15,498,230),which means that the scan electrode width,the xenon content,the gas pressure and the sustain voltage are 80 μm、15%、6.6×10^4 Pa and 230 V.The discharge efficiency in optimized parameters is about two times larger than that in initial parameters.And the vacuum energy increases 79% compared to the initial parameters.So the optimized parameters can be obtained to increase the discharge efficiency and the discharge intensity.
出处 《光电子技术》 CAS 北大核心 2009年第3期149-153,160,共6页 Optoelectronic Technology
基金 国家自然科学基金资助项目(60571033 60871015) 高等学校学科创新引智计划资助(B07027)
关键词 等离子体平板显示 复合形法 放电效率 plasma display panel complex method discharge efficiency
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参考文献7

  • 1Tu Yan,Zhang Xiong,Yang Lanlan,et al.Three dimensional investigation of a novel plasma display panel with metal barrier plates[C].SID 02 Digest,Boston,USA,2002:404-408.
  • 2Zhang Xiong,Li Qing,Tu Yan,et al.A novel AC PDP with shadow mask[C].SID 02 Digest,Boston,USA,2002:748-752.
  • 3Zhang Xiong,Li Qing,Tu Yan,et al.,A novel shadow mask PDP with high luminance and contrast[C].SID 03 Digest,Baltimore,USA,2003:149-151.
  • 4Yang Lanlan,Tu Yan,Wang Baoping,et al.Two-dimensional simulation of the effects of the driving waveforms on the shadow mask PDP cell discharge[J],Plasma Sources Sci Technol.2005(14):686-691.
  • 5杨兰兰,屠彦,王保平,尹涵春,童林夙.新型荫罩式PDP单元结构的优化设计[J].真空科学与技术,2003,23(6):389-394. 被引量:5
  • 6何坚勇编著.最优化方法[M].北京:清华大学出版社,2007
  • 7张雄,屠彦,杨兰兰,王保平,尹涵春,童林夙.三维PDP放电过程数值模拟软件[J].光电子技术,2004,24(4):214-217. 被引量:3

二级参考文献10

  • 1[1]Deschamps J,Doyeux H.IDW′96,1996:275
  • 2[2]Young Kyo shin,Chae Hwa shon et al.IEEE Trans Plasma Sci,27(5):1366~1370
  • 3[3]Heui Seob Jeong,Buhm-Jae Shin,Ki-Woong Whang.IEEE Trans Plasma Sci 27(1):171~180
  • 4[4]Passchier J D P,Goedheer W J.J Appl Phys,1993,74(6):3744~3749
  • 5[5]Ganter R,Callegari Th,Pitchford L C et al.Applied Surface Science,2002,192:299~308
  • 6[6]Meiunier J,Belenguar Ph.J Appl Phys,1995,78(2):742
  • 7Meunier J. Numerical model of an AC plasma display panel cell in neon-xenon mixtures[J]. J.Appl. Phys., 1995, 78(4): 2 233-2 242
  • 8Ramana Veerasingam, Robert B Campbell, Robert T McGr-ath. One-dimensional fluid and circuit simulation of an AC plasma display cell[J],IEEE Trans. On Plasma Science,1995,23(4):688-698
  • 9Boeuf J P, Pitchford L C. Calculated characteristics of an AC plasma display panel cell[J]. IEEE Trans. On Plasma Science, 1996,24(4):95-101
  • 10Robert B Campbell, Ramana Veerasingam, Robert T. Mc-Grath. A two-dimensional multispecies fluid model of the plasma in an AC plasma display panel[J].IEEE Trans. On Plasma Science, 1995,23(4):698-716

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  • 1D Goldberg. Genetic algorithms in search, optimization, and machine learning[ D ]. Boston : Addison-Wesley Professional, 1989.
  • 2D E Goldberg, K Deb. A comparative analysis of selection schemes used in genetic algorithms [ J ]. Foundations of Genetic Algorithms, 1991,44 ( 3 ) : 69 -93.
  • 3S J Wu, P T Chow. Steady-state genetic algorithms for discrete optimization of trusses[ J]. Computers & Structures, 1995 (56) : 979-991.
  • 4F Herrera, M Lozano, J L Verdegay. Tackling real-coded genetic algorithms:operators and tools for behavioural analysis [ J ]. Artificial Intelligence Review, 1998 (12) : 265-319.
  • 5K Deb. Multi-objective optimization using evolutionary algorithms [ M ]. New York:John Wiley & Sons ,2001.
  • 6H M Pandey, A Chaudhary, D Mehrotra. A comparative review of approaches to prevent premature convergence in GA [ J ]. Applied Soft Computing, 2014 ( 24 ) : 1047-1077.
  • 7H I-I Rosenbrock. An automatic method for finding the greatest or least value of a function[ J ]. The Computer Journal, 1960(3 ) : 175-184.
  • 8C Guria, P K Bhattacharya, S K Gupta. Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA) [ J ]. Computers & Chemical Engineering, 2005 ( 29 ) : 1977-1995.
  • 9K S Nawaz Ripon,S Kwong,K F Man. A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization[J] Information Sciences,2007 (177) :632-654.
  • 10T F Edgar. Optimization of chemical processes[ M ]. 2nd ed. New York:McGraw-Hill,2001.

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