The optimized design of foam aluminum foam muffler is carried out based on optimization software ISIGHT. The muffler performances before and after optimization of parameters, such as the length and diameter of inlet pipe, outlet pipe and idle pipe were simulated and analyzed with software FLUENT and SYSNOISE. The results show that sound transmission loss after optimization increases in average by about 5 dB, and pressure loss reduces by about 11 % comparing with that before. Consequently, the optimized foam aluminum muffler is of better acoustic and aerodynamic performance.

The calculation procedure of Aures roughness model is discussed in detail. The basic principle and calculation skills of the algorithm are described. Some points that can easily lead to errors are pointed out and discussed in detail. Then the algorithm is implemented with MATLAB. And the roughness of typical signals is calculated.?The results are compared with the experimental data of Zwicker and S. Kemp and the numerical data from NI’s calculation modulus. The correctness of the algorithm is verified.

Using the timevarying NARMA (Nonlinear Auto Regressive Moving Average) model and the improved recursive least square algorithm, an identification method for nonlinear timevarying structure system is proposed. Firstly, the dynamic model of the timeindependent structure system is changed to an autoregressivemovingaverage model by means of linear transform method. Then the nonlinear function of this model is expanded to a polynomial about input and output using Taylor expansion, and the polynomial timevarying NARMA model, which is a linear combination of parameters, is obtained. Using the basic sequences to fit the timevarying parameters of the model, the nonlinear timevarying system is then transformed into a linear timeinvariant system, whose parameters can be estimated by improved recursive least square algorithm. Finally, the proposed method is validated by the simulation of a 3DOF structural system with nonlinear timevarying stiffness.

Extraction of the signals, which can represent sound source information and meanwhile include noise signals, from acoustic sensors is the key technique for sound emission monitoring. To eliminate the noise efficiently and raise the signaltonoise ratio, this paper brings up a dualadaptive noisecancellation algorithm, which includes adaptive subband decomposition algorithm and adaptive noise cancellation algorithm. First of all, to reduce the complexity in computation and realize parallel algorithm, the method of the subband decomposition, which matches the noise powerspectrum density, is adopted for nonuniform signal subband decomposition. Then, to save the computer time, the effective filtering is performed according to the distribution of noise in the subband. The subbands with lownoise or essentially without noise do not need filtering, while the adaptive filtering algorithm is used for the other subbands. The simulation shows that this method can greatly save computer time in comparison with the traditional adaptive noise cancellation method with uniform subbands, and the effect of filtering has been improved.