Structural Optimization of a Muffler for a Marine Pumping System Based on Numerical Calculation

Liu, Houlin; Lin, Juncong; Hua, Runan; Li, Dong

doi:10.3390/jmse10070937

Cited by 1 publication

(1 citation statement)

References 22 publications

(20 reference statements)

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“…The acoustic TL at all octave Band Center Frequencies (OBCFs) are calculated and compared with available experimental results in literature. Table 3 presents a convergence and comparison study for a muffler structure optimization design to eliminate the noise in the pumping system by Liu et al [47]. The muffler is silenced at the outlet of the pump pipeline system, the muffler material is commercial steel and the structure of muffler is shown in Figure 11.…”

Section: Validation Of the Proposed Methodsmentioning

confidence: 99%

Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm

Altabey

Noori

et al. 2022

Materials

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Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers’ industrials, and its design may one day be equipped with deep learning based algorithms.

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Section: Validation Of the Proposed Methodsmentioning

confidence: 99%