Abstract
A novel auto design method of acoustic filter based on genetic programming (GP) is proposed in this paper. Unlike the model-based auto optimization method by genetic algorithms that optimize acoustic filters only in the parameter domain, the proposed method can optimizes the acoustic filter in both the structure region and the parameter region simultaneously thanks to its tree representation of individuals. In the proposed method, the widely used acoustic components have been equivalent to the basic elements in the topology. The acoustic filter individuals in GP are composed of these elements. Transfer matrix method is employed to calculate the transmission loss of the acoustic filter individuals. By a certain number of generations, the GP finds the individuals with good fitness value. The feasibility of the method is validated by two numerical experiments. The first case study is to validate the searching ability of the method. The result shows that the proposed method has excellent ability to search the optimal solution with transmission loss curve close to the objective transmission loss curve. The second experiment is based on a typical automobile muffler design strategies that requires a high noise reduction magnitude at two specified frequency band and a certain noise reduction magnitude at the whole frequency band, the evolution result meets the design requirements well. The results of these two numerical experiments demonstrate that the proposed method has excellent searching ability and is feasible in the auto design of acoustic filter.
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Toyoshima Y, Nagai T, Hosoya N. Exhaust system for engine on passenger car. Eng Technol. 2001;3(2):90–5.
Yasuda T, Chaoqun Wu, Nakagawa N, et al. Predictions experimental studies of the tail pipe noise of an automotive muffler using a one dimensional CFD model. Appl Acoust. 2010;71(8):701–7.
Yasuda T, Wu C, Nakagawa N, Nagamura K. Studies on an automobile muffler with the acoustic characteristic of low-pass filter and Helmholtz resonator. Appl Acoust. 2013;74(1):49–57.
Airaksinen T, Heikkola E. Multiobjective muffler shape optimization with hybrid acoustics modeling. J Acoust Soc Am. 2011;130(3):1359–69.
Lima KFD, Lenzi A, Barbieri R. The study of reactive silencers by shape and parametric optimization techniques. Appl Acoust. 2011;72(4):142–50.
Chen F. Optimization design of muffler based on acoustic transfer matrix and genetic algorithm. J Vibro Eng. 2014;16(5):2216–23.
Min-Chie Chiu. Numerical assessment for a broadband and tuned noise using hybrid mufflers and a simulated annealing method. J Sound Vib. 2013;332(12):2923–40.
Chiu MC, Chang YC. Shape optimization of multi-chamber cross-flow mufflers by SA optimization. J Sound Vib. 2008;312(3):526–50.
Barbieri R, Barbieri N, Lima KFD. Some applications of the PSO for optimization of acoustic filters. Appl Acoust. 2015;89:62–70.
Jin WL. Optimal topology of reactive muffler achieving target transmission loss values: design and experiment. Appl Acoust. 2015;88:104–13.
Jang GW, Lee JW. Optimal partition layout of expansion chamber muffler with offset inlet/outlet. Int J Automot Technol. 2015;16(5):885–93.
Fan Z. Design automation of mechatronic systems using evolutionary computation and bond graph. Michigan State University; 2004.
Hu J, Li S. Genetic programming and creative design of mechatronic system. 1st. China Machine Press; 2009 (in Chinese).
Poli R, Langdon WB, McPhee NF. A field guide to genetic programming. Lulu Enterprises UK Ltd, UK; 2008.
Koza JR, Keane MA, Streeter MJ. Routine automated synthesis of five patented analog circuits using genetic programming. Soft Comput. 2004;8(5):318–24.
Hu J, Fan Z, Wang J, et al. GPBG: a framework for evolutionary design of multi-domain engineering systems using genetic programming and bond graphs. Des Evol. 2007:319–345.
Seo K, Fan Z, Hu J, et al. Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming. Mechatronics. 2003;13(8):851–85.
Gamma technologies. GT-POWER User’s manual. Version.7.0. 2009.
Mechel FP. Formulas of acoustics. Berlin: Springer; 2008.
Zhao S. Reduction and isolation of noise. Tongji University Press; 1989 (in Chinese).
Munjal ML. Acoustics of ducts and mufflers. Wiley & Sons; 1987.
Koza JR. Genetic programming: on the programming of computers by means of natural selection. MIT Press; 1992.
Acknowledgements
This project is supported by Applied Basic Research Project of Wuhan (Grant No. 2016010101010027), Independent Innovation Foundation of Wuhan University of Technology (Grant No. 172104001).
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Appendix
Appendix
Appendix and supplement both mean material added at the end of a book. An appendix gives useful additional information, but even without it the rest of the book is complete: In the appendix are forty detailed charts. A supplement, bound in the book or published separately, is given for comparison, as an enhancement, to provide corrections, to present later information, and the like: A yearly supplement is issue.
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Zhou, Q., Wu, C., Zhao, W., Hua, W., Liu, L. (2018). A Novel Auto Design Method of Acoustic Filter Based on Genetic Programming. In: Tan, J., Gao, F., Xiang, C. (eds) Advances in Mechanical Design. ICMD 2017. Mechanisms and Machine Science, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-10-6553-8_45
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DOI: https://doi.org/10.1007/978-981-10-6553-8_45
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