Numerical Optimization of Single-chamber Mufflers Using Neural Networks and Genetic Algorithm

Ying-chun CHANG

Makale detayları

Numerical Optimization of Single-chamber Mufflers Using Neural Networks and Genetic Algorithm

Yazarlar:

Ying-chun CHANG

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Engineering and Environmental Sciences 2008 Vol.32 Issue 6, pp.313-322

Belge Tipi:

Makale

Yayın Tarihi:

5/2008

Belge Dili:

Türkçe

Document ID (Org No.):

2000744800

Sıklık:

Üç Yıllık

eISSN:

1303-6157

pISSN:

1300-0160

Özet:

 

To simplify the optimization process, a simplified  mathematical model of a muffler is constructed using a neural network  and a series of input design data (muffler dimensions) and output data  (theoretical sound transmission loss) that are obtained by utilizing a  theoretical mathematical model (TMM).  To assess an optimal muffler, a  neural network model (NNM) is used as the objective function in  conjunction with a genetic algorithm (GA).  Before the GA operation  can be carried out, however, the accuracy of the TMM must be checked  and be in accord with the experimental data.  Additionally, the NNM  must also be in agreement with the TMM. Also discussed are the  numerical cases of sound elimination relative to the various parameter  sets and pure tones (500, 1000, and 2000 Hz).  The results reveal that  the maximum value of the sound transmission loss (STL) can be  accurately obtained at the desired frequencies.  Consequently, the  algorithm proposed in this study can provide an efficient way to  develop optimal silencers for the requisite industries.

 

To simplify the optimization process, a simplified  mathematical model of a muffler is constructed using a neural network  and a series of input design data (muffler dimensions) and output data  (theoretical sound transmission loss) that are obtained by utilizing a  theoretical mathematical model (TMM).  To assess an optimal muffler, a  neural network model (NNM) is used as the objective function in  conjunction with a genetic algorithm (GA).  Before the GA operation  can be carried out, however, the accuracy of the TMM must be checked  and be in accord with the experimental data.  Additionally, the NNM  must also be in agreement with the TMM. Also discussed are the  numerical cases of sound elimination relative to the various parameter  sets and pure tones (500, 1000, and 2000 Hz).  The results reveal that  the maximum value of the sound transmission loss (STL) can be  accurately obtained at the desired frequencies.  Consequently, the  algorithm proposed in this study can provide an efficient way to  develop optimal silencers for the requisite industries.

Makale detayları

Numerical Optimization of Single-chamber Mufflers Using Neural Networks and Genetic Algorithm

Yazarlar: Ying-chun CHANG

Yayıncı: TUBITAK

Dergi: Turkish Journal of Engineering and Environmental Sciences 2008 Vol.32 Issue 6, pp.313-322

Belge Tipi: Makale

Yayın Tarihi: 5/2008

Belge Dili: Türkçe

Document ID (Org No.): 2000744800

Sıklık: Üç Yıllık

eISSN: 1303-6157

pISSN: 1300-0160

Sayı

Yazarlar:

Ying-chun CHANG

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Engineering and Environmental Sciences 2008 Vol.32 Issue 6, pp.313-322

Belge Tipi:

Makale

Yayın Tarihi:

5/2008

Belge Dili:

Türkçe

Document ID (Org No.):

2000744800

Sıklık:

Üç Yıllık

eISSN:

1303-6157

pISSN:

1300-0160