摘要
为了实现炉内温度场的实时在线监测,阐述了基于声学理论的三维温度场重建原理,介绍了奇异值分解(singular value decomposition,SVD)算法。采用最小二乘法和SVD算法分别对炉膛火焰分布的几种典型模型:单峰模型、双峰模型以及四峰模型,进行了仿真重建,并对两种算法的仿真结果做了比较。仿真得到了稳定的重建结果,给出了不同温度场分布模型及其重建图像,分析了重建误差。仿真结果表明,两种算法均可用于炉膛三维温度场重建,并且具有一定的抗干扰能力,与最小二乘法相比,奇异值分解算法具有更好的稳定性。
In order to realize the real-time monitoring of temperature field in furnace, the principle of three-dimensional temperature field reconstruction based on acoustic theory was analyzed. Singular value decomposition(SVD) algorithm was introduced. Three typical temperature field models, such as one-peak model, two-peak model and four-peak model, were simulated with only a few acoustic data by least squares method and SVD algorithm respectively. Simulation results acquired by these two algorithms were compared. The simulation acquired a stable reconstruction. In addition, the different temperature field models as well as their reconstruction images were given and reconstruction errors were analyzed. Simulation results indicate that these two algorithms are able to be used to reconstruct furnace three-dimensional temperature field and have good anti-disturbance ability. Compared with least squares method, SVD algorithm has better stability.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2014年第S1期147-152,共6页
Proceedings of the CSEE
基金
高等学校学科创新引智计划(B12034)
北京高等学校青年英才计划项目
中央高校基本科研业务费专项资金资助~~
关键词
声学测温
三维温度场
重建算法
奇异值分解
最小二乘法
acoustic pyrometry
three-dimensional temperature field
reconstruction algorithms
singular value decomposition
least squares method