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基于简化传热关系的冷却壁热面温度预测智能仿真
引用本文:钱中,杜朝辉,王平阳.基于简化传热关系的冷却壁热面温度预测智能仿真[J].钢铁研究学报,2007,19(5):103-0.
作者姓名:钱中  杜朝辉  王平阳
作者单位:上海交通大学航空航天系,上海,200240
摘    要: 冷却壁工作过程中的温度高低直接影响其使用寿命。以铸钢冷却壁的传热为研究对象,根据热态试验结果,提出简化传热关系式,并将其与神经网络技术相结合,形成冷却壁热面温度预测仿真模型。与试验值相比,仿真输出值的最大相对误差不足2%,说明这种智能模型准确、可靠。同时采用的研究方法在冷却壁的研究中具有参考价值。

关 键 词:冷却壁  传热  热面温度  神经网络  智能仿真
文章编号:1001-0963(2007)05-0103-04
收稿时间:1900-01-01;
修稿时间:01 15 2007 12:00AM

Intelligent Simulation of Cooling Stave Hot Surface Temperature Prediction Based on Simplified Heat Transfer Relationship
QIAN Zhong,DU Zhao-hui,WANG Ping-yang.Intelligent Simulation of Cooling Stave Hot Surface Temperature Prediction Based on Simplified Heat Transfer Relationship[J].Journal of Iron and Steel Research,2007,19(5):103-0.
Authors:QIAN Zhong  DU Zhao-hui  WANG Ping-yang
Affiliation:Department of Aeronautical and Astronautical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Abstract:Temperature of cooling stave under working condition directly affects stave campaign life. Heat transfer of cast steel cooling stave is the research object, and simplified heat transfer formula is proposed based on thermal state test results. Then the simplified mode is combined with artificial neural network to form a prediction model of cooling stave hot surface temperature. Maximum relative error of simulation outputs compared with experiment data is less than 2%, indicating that such an intelligent model is accurate and reliable. In addition, the research method of this paper is quoteworthy for research on cooling stave.
Keywords:cooling stave  heat transfer  hot surface temperature  neural network  intelligent simulation
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