A signal processing method applied to direct simulation Monte Carlo for predicting acoustic attenuation in gas mixtures

摘要

A theoretical predictive model of acoustic attenuation in gas mixtures is necessary for analysing the composition of a gas mixture. A signal-processing framework applied to a direct simulation Monte Carlo (DSMC) method was developed for this purpose. As the DSMC system was proved to be an externally linear and time-invariant system in this paper, the acoustic attenuation of certain frequencies could be estimated by nonlinearly curve fitting on the basis of the data from the Fourier transform of the output signal. Two benefits could be gained from our work. First, if the simulation lasts enough time steps, the data from Fourier transform for one DSMC simulation could theoretically include every point of the attenuation spectrum. Second, the nonlinear curve-fitting method could reduce simulation time without losing predictive precision. Hence the signal processing method developed in this paper makes the DSMC simulation more efficient in analysing the relation between acoustic intensity attenuation and wave frequency. The result of our signal-processing framework based DSMC shows that the acoustic attenuation spectrum is dependent upon the composition of the gas mixture. Gas mixtures of nitrogen with variable additions of oxygen and carbon dioxide were considered in this paper. The frequency range of interest is from 8 MHz to 232 MHz in our predictive model.