文件名称:189-199
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- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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As the compact design of a muffler system within a constrained
environment of a existing machine room becomes obligatory, it also
becomes essential to maximize the acoustic performance of mufflers
under space constraints. In this paper, the shape optimization of a
double-chamber muffler with an extended tube is presented. The
main characteristic of the solution methodology is the use of genetic
algorithm (GA) as the optimizer. In the paper, the acoustic perfor-
mance of sound transmission loss (STL) derived by transfer matrix is
conjugated with the techniques of GA searching. A numerical case-As the compact design of a muffler system within a constrained
environment of a existing machine room becomes obligatory, it also
becomes essential to maximize the acoustic performance of mufflers
under space constraints. In this paper, the shape optimization of a
double-chamber muffler with an extended tube is presented. The
main characteristic of the solution methodology is the use of genetic
algorithm (GA) as the optimizer. In the paper, the acoustic perfor-
mance of sound transmission loss (STL) derived by transfer matrix is
conjugated with the techniques of GA searching. A numerical case
environment of a existing machine room becomes obligatory, it also
becomes essential to maximize the acoustic performance of mufflers
under space constraints. In this paper, the shape optimization of a
double-chamber muffler with an extended tube is presented. The
main characteristic of the solution methodology is the use of genetic
algorithm (GA) as the optimizer. In the paper, the acoustic perfor-
mance of sound transmission loss (STL) derived by transfer matrix is
conjugated with the techniques of GA searching. A numerical case-As the compact design of a muffler system within a constrained
environment of a existing machine room becomes obligatory, it also
becomes essential to maximize the acoustic performance of mufflers
under space constraints. In this paper, the shape optimization of a
double-chamber muffler with an extended tube is presented. The
main characteristic of the solution methodology is the use of genetic
algorithm (GA) as the optimizer. In the paper, the acoustic perfor-
mance of sound transmission loss (STL) derived by transfer matrix is
conjugated with the techniques of GA searching. A numerical case
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