Complex Measures And Complexity Analysis Of Airflow Signals

With the rapid development of the industry,the types and quantities of dangerous chemical products in our life keep increasing.The toxic gas leakage accidents occur frequently.It causes great harm to public safety and the environment.The signal of airflow field is affected by many kinds of surface factors,such as temperature difference in air,pressure,surface heat island effect,topography etc.The signal of airflow field exhibits strong nonstationary and uncertainty,which further makes great difficulties to the estimation of toxic gas diffusion path and the localization of leakage source.Dynamic mechanism and temporal-spatial evolution analysis of the airflow field will contribute to a better understanding of gas diffusion behavior,and will help to build effective warning and emergency responding system for gas leakage accident.Therefore,this paper use the complex measures to analyze the complexity of the signals from airflow field.The main studying contents of this thesis are as follows:(1)The Lempel-Ziv complexity,permutation entropy and power spectral entropy are used to study the difference of wind velocity signals from time domain and frequency domain.The results show that the horizontal component of wind velocity in the two environments is dominant,which affects the complexity of the wind velocity signal.That is,the intensity of wind speed signal is mainly reflected in two horizontal components,which has lower correlation with the vertical component.The fluctuation of the vertical component in the outdoor environment is more complex and the fluctuation of the horizontal component in the indoor environment is more complex.(2)For the multi-scale feature of the structure of the airflow field signal,multi-scale sample entropy and multivariate multi-scale sample entropy are applied to analyze the complexity of wind speed / wind direction signals from the horizontal and vertical plane of the airflow field.The results show that the entropy values of wind speed signals in horizontal plane are lower than that in vertical plane,that is,wind speed signals in horizontal plane are less complex than that in vertical plane.But the complexity of wind direction signals is different.On the low scale,the complexity of the horizontal signal is between the two vertical surface signals,but with the scale factor increasing,a vertical plane signal is the most complex.(3)Existing entropy methods are mainly designed from the time-domain and frequency-domain,which makes sidedness of the analysis result.Hence,we put forward a new time series complexity estimation method,based on image microstructure frequency analysis.Firstly,the one-dimensional time series are mapped into two-dimensional images by using time-series phase space reconstruction,that is,the phase space points of one-dimensional time series are determined by delay time and embedding dimension technique,and then get the two-dimensional gray-level image which reflects the connection of the phase space points.Secondly,we use the image key point technique to determine local microstructure position and descriptor.Finally,according to the adaptive clustering results of image local microstructure descriptors,we can determine the microstructure category and frequency,and the recursive entropy of one-dimensional time-series image micro-structure is calculated.The test results show that the method is sensitive to the change of the signals and can distinguish diffident signals accurately.