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作 者:金启航 李天硕 李海洋 段钰锋[1,2] Jin Qihang;Li Tianshuo;Li Haiyang;Duan Yufeng(School of Energy and Environment,Southeast University,Nanjing 210096,China;Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,Southeast University,Nanjing 210096,China)
机构地区:[1]东南大学能源与环境学院,南京210096 [2]东南大学能源热转换及其过程测控教育部重点实验室,南京210096
出 处:《东南大学学报(自然科学版)》2023年第3期559-566,共8页Journal of Southeast University:Natural Science Edition
基 金:国家重点研发计划资助项目(2016YFC0201105).
摘 要:为提高在线监测系统(CEMS)中音速喷嘴的计量精度,通过纯气体组分条件下圆环形喉部音速喷嘴的数值模拟研究,建立了关于流出系数与扩散段参数、气体种类以及雷诺数关系的BP神经网络模型,分析了临界背压比与喉部雷诺数的关系,预测了CEMS音速喷嘴的流动特性,并与实验结果进行对比.结果表明,BP神经网络方法与数值模拟方法均可对喷嘴流量进行合理预测,最大误差仅为1.004%.当喷嘴喉径由0.1432 mm增加至0.3502 mm时,临界背压比较数值模拟值降低约20%.BP神经网络模型可以快速有效地预测CEMS音速喷嘴采样流量,当雷诺数大于4000时临界背压比显著下降.To improve the metrological accuracy of sonic nozzles in the continuous emission monitoring system(CEMS),based on the numerical simulation of toroidal-throat sonic nozzle under pure gas component conditions,a back propagation(BP)neural network model of the relationship of the discharge coefficient of sonic nozzles with diffusion section parameters,the gas type,and the Reynolds number was established.The relationship between the critical back pressure ratio and the throat Reynolds number was investigated.The flow characteristics of the sonic nozzle in the CEMS were predicted and compared with the experimental results.The results show that both the BP neural network method and the numerical simulation method can predict reasonably the nozzle flow rate within a maximum error of 1.004%.When the nozzle throat diameter increases from 0.1432 to 0.3502 mm,the critical back pressure ratio decreases by about 20%compared with the result of the numerical simulation.The BP neural network model can quickly and effectively predict the sampling flow rate of the sonic nozzle in the CEMS.The critical back pressure ratio decreases remarkably when the Reynolds number is larger than 4000.
关 键 词:稀释抽取式CEMS 音速喷嘴 数值模拟 流出系数 临界背压比
分 类 号:X851[环境科学与工程—环境工程]
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