CAJ | 학술논문

通过采集鼓泡流化床风帽入口的压力信号,利用小波方法对信号进行消噪处理后,对流化床风帽入口压力信号的时间序列进行了相空间重构,利用G-P算法计算了重构相空间的关联维数,并对冷态实验台采集的风帽压力数据进行了分析处理.结果表明：鼓泡流化床风帽压力波动信号具有混沌特性;自相关函数为0.1时,对应的延迟时间较为合适;典型冷态试验工况下,当表观气速增大时,关联维数呈上升趋势;当静床高增大时,关联维数随之减小.
통과채집고포류화상풍모입구적압력신호,이용소파방법대신호진행소조처리후,대류화상풍모입구압력신호적시간서렬진행료상공간중구,이용G-P산법계산료중구상공간적관련유수,병대랭태실험태채집적풍모압력수거진행료분석처리.결과표명：고포류화상풍모압력파동신호구유혼돈특성;자상관함수위0.1시,대응적연지시간교위합괄;전형랭태시험공황하,당표관기속증대시,관련유수정상승추세;당정상고증대시,관련유수수지감소.
Pressure data of wind caps in a cold-state bubbling fluidized-bed test rig were analyzed by means of collecting pressure signals at entrance of the wind caps,performing pressure signal denoising with the help of wavelet method,reconstructing phase space of relevant time series data and calculating correlation dimension of the reconstructed phase space using G-P algorithm.Results show that the pressure fluctuation signals of wind caps in bubbling fluidized bed have chaos characteristics.The time delay,coinciding with the point where the autocorrelation function reaches a value of 0.1,is found to be more appropriate.Under typical experimental conditions,the correlation dimension rises with increasing superficial gas velocity and reducing static bed height.