摘要
针对超超临界锅炉水冷壁节流孔板结垢问题,建立了节流孔板的数值计算模型,分析了结垢的主要原因以及孔板结构参数对结垢程度的影响.在大量数值计算的基础上,采用BP人工神经网络建立了孔板结构参数与结垢程度之间的关系模型并采用遗传算法进行结构优化,得到节流孔板的最佳结构参数.结果表明:节流孔板处压力突降造成磁性氧化铁的析出并沉淀是节流孔板结垢的主要原因;节流孔板的入口倒角、出口倒角、孔径和厚度等结构参数对孔板壁面附近的最大压降幅度和孔板节流效果的影响不同,孔板的厚度越小越好,出口倒角对结垢几乎没有影响,而孔板的孔径和入口倒角对结垢程度影响较大;得到的最佳孔板尺寸参数为孔径9.54mm,入口倒角35°.
To solve the fouling problems occurring in throttle orifice for ultra supercritical boiler water walls,, numerical calculation models were set up to analyze the fouling causes and the influence of structural parameters on the fouling degree. A relational model between the structural parameters and fouling degree was established based on a large amount of calculation and using BP artificial neural network, with which optimal structural parameters were obtained by using genetic algorithms for minimum fouling of the ori- fice. Results show that the precipitation and deposition of magnetic oxide iron caused by sudden pressure drop near the orifice plate are found to be the main reasons of orifice fouling~ the influence of following structural parameters on the maximum pressure drop amplitude and throttling effect may be different a- round the orifice plate： such as the inlet angle, outlet angle, pore diameter and length of the orifice, etc. ; the shorter the orifice length, the better the performance effect; the outlet angle hardly affects the orifice fouling, but the inlet angle and pore diameter play an important role in influencing the fouling degree~ the optimal parameters of orifice plate are determined to be 9.54 mm for the pore diameter and 35° for the inlet angle.
出处
《动力工程学报》
CAS
CSCD
北大核心
2012年第11期830-835,共6页
Journal of Chinese Society of Power Engineering
关键词
水冷壁
节流孔板
结垢
BP神经网络
遗传算法
数值计算
water walls throttle orifice fouling BP neural network genetic algorithm numerical calcu-lation
作者简介
喻兰兰（1989一），女，江苏盐城人，硕士研究生，主要从事电站锅炉运行及故障诊断方面的研究．电话（Tel．）：13770963924；E—mail：llyu—seu@163．com．
