Multi-objective optimization of a diesel particulate filter: an acoustic approach

• Published in: Particulate science and technology Vol. 40; no. 4; pp. 465 - 474
• Main Authors: Ozturk, Sinem, Erol, Haluk
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 19-05-2022; Taylor & Francis Ltd
• Subjects: Acoustic properties; Automobiles; Design optimization; Diesel engines; Diesel particulate filter; Fluid filters; Genetic algorithms; multi-objective optimization; Multiple objective analysis; nondominated sorting genetic algorithm; Parameters; pressure drop; Soot; Sorting algorithms; Sound transmission; sound transmission loss; Transmission loss
• ISSN: 0272-6351; 1548-0046
• DOI: 10.1080/02726351.2021.1964116

In order to decrease the soot particles' harmful emission, it is necessary to use a diesel particulate filter (DPF) on cars. Recent years, many countries standardize using DPF on cars. In this paper, a multi-objective design optimization study is performed by using the nondominated sorting genetic algorithm (NSGA-II) to obtain an optimum DPF geometry. In the present study, two objective functions were determined to obtain an optimum DPF. The first was to maximize sound transmission loss (TL) values to increase the acoustical properties of the DPF, while the second was to minimize the back pressure to cause the diesel engine to work efficiently and consume less energy. The optimization problem has been constructed in the form of maximizing the sum of the values of TL at all frequencies, as optimizing the problem for each frequency separately means a new DPF at each frequency, which has a different geometry. In the literature, optimization of the DPF has been carried out for a few parameters by just changing one parameter value and keeping others stable. However, in this paper, optimization of the acoustic performance of DPFs was performed for more than one parameter in the range of their specific values simultaneously.