Evaluation on Excess Entropy Scaling Method Predicting Thermal Transport Properties of Liquid HFC/HFO Refrigerants

The application of the excess entropy scaling(EES)method to predict the viscosity,thermal conductivity and thermal diffusivity of HFC/HFO refrigerants is evaluated in this paper.The universal coefficients of the EES model were firstly obtained from the properties of HFC refrigerants,and the accuracy of the model was further investigated with HFO properties.It was suggested that the EES model correlated the viscosity very well with the average absolute deviations(AADs)of most HFC refrigerants lower than 6.55%except R32.The correlations also provided very good prediction on the viscosity for R1234yf and R1234ze(E),but not for R1336mzz(Z).The prediction of thermal conductivity for both HFC and HFO refrigerants was generally well with the maximum AAD of 11.44%.However,the paper also indicated that there were no universal thermal diffusivity coefficients for even HFC refrigerants,and the linear function could not fit the thermal diffusivity curve very well.Therefore,the exclusively two-order polynomial correlations based on the EES model were presented for each HFC/HFO refrigerant.

AN INFORMATIC APPROACH TO A LONG MEMORY STATIONARY PROCESS

Long memory is an important phenomenon that arises sometimes in the analysis of time series or spatial data.Most of the definitions concerning the long memory of a stationary process are based on the second-order properties of the process.The mutual information between the past and future I_(p−f) of a stationary process represents the information stored in the history of the process which can be used to predict the future.We suggest that a stationary process can be referred to as long memory if its I_(p−f) is infinite.For a stationary process with finite block entropy,I_(p−f) is equal to the excess entropy,which is the summation of redundancies that relate the convergence rate of the conditional(differential)entropy to the entropy rate.Since the definitions of the I_(p−f) and the excess entropy of a stationary process require a very weak moment condition on the distribution of the process,it can be applied to processes whose distributions are without a bounded second moment.A significant property of I_(p−f) is that it is invariant under one-to-one transformation;this enables us to know the I_(p−f) of a stationary process from other processes.For a stationary Gaussian process,the long memory in the sense of mutual information is more strict than that in the sense of covariance.We demonstrate that the I_(p−f) of fractional Gaussian noise is infinite if and only if the Hurst parameter is H∈(1/2,1).