Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-28T01:48:02.259Z Has data issue: false hasContentIssue false
  • English
  • Français

Numerical Assessment of Optimal Equipment Allocation and Close-Fitting Acoustical Hoods Within a Multi-Noise Plant Using an Artificial Immune Method

Published online by Cambridge University Press:  20 December 2012

M.-C. Chiu
M.-C. Chiu*
Affiliation:
Department of Mechanical and Automation Engineering, Chung Chou University of Science and Technology Changhua, Taiwan 51003, R.O.C.
*
Corresponding author ([email protected])
Get access

Abstract

High noise levels in a multi-noise plant can be harmful to workers and can lead to both psychological and physiological problems. Consequently, noise control work on equipment such as acoustic hoods becomes vital. However, research work of shape optimization on space-constrained close-fitting acoustic hoods has been neglected.

In this paper, a sound insertion loss used for evaluating the acoustic performance of an acoustical hood will be adopted. A numerical case for depressing the noise levels at the receiving points along the boundary of three kinds of multi-equipment plants by optimally designing a shaped one-layer close-fitting acoustic hood and reallocating the equipment within a constrained space will also be introduced. Moreover, an artificial immune method (AIM) is adopted and coupled with the equations of sound attenuation and minimal variation square in conjunction with a twelve-point monitoring system.

Consequently, this paper provides a quick and effective method for reducing the noise impact around a plant by optimally designing a shaped one-layer close-fitting acoustic hood and reallocating equipment within the AIM searching technique.

Keywords

Multi-noise plantClose-fitting acoustical hoodArtificial immune method
Type
Articles
Information
Journal of Mechanics , Volume 29 , Issue 1 , March 2013 , pp. 185 - 195
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Alley, B. C., Dufresne, R. M., Kanji, N. and Reesal, M.R., “Costs of Workers' Compensation Claims for Hearing Loss,” Journal of Occupational Medicine, 31, pp. 134–138 (1989).Google Scholar
2. Cheremisinoff, P. N. and Cheremisinoff, P. P., Industrial Noise Control Handbook, Ann Arbor Science, Michigan (1977).Google Scholar
3. Beranak, L. L. and Work, G.A., “Sound Transmission through Multiple Structures Containing Flexible Blankets,” Journal of the Acoustical Society of America, 7, p. 419 (1949).Google Scholar
4. London, A., “Transmission of Reverberant Sound Through Single Wall,” Journal of the Acoustical Society of America, 22, pp. 270–279 (1950).Google Scholar
5. Crocker, M. J., “A System Approach to the Transmission of Sound and Vibration through Structures,” Noise-Con, 94, pp. 525–533 (1994).Google Scholar
6. Oldham, D. J. and Hilarby, S. N., “The Acoustical Performance of Small Close Fitting Enclosures, Part 1: Theoretical Methods,” Journal of Sound and Vibration, 150, pp. 261–281 (1991).Google Scholar
7. Oldham, D. J. and Hilarby, S. N., “The Acoustical Performance of Small Close Fitting Enclosures, Part 2: Experimental Investigation,” Journal of Sound and Vibration, 150, pp. 283–300 (1991).CrossRefGoogle Scholar
8. Fahy, F., Sound and Structural Vibration, Academic Press, San Diego, Ca (1989).Google Scholar
9. Beranek, L. L. and Ver, I. L., Noise and Vibration Control Engineering, John Wiley and Sons, New York (1992).Google Scholar
10. Kinsler, L. E. and Frey, A. R., Fundamentals of Acoustics, John Wiley and Sons, New York (1982).Google Scholar
11. Jackson, R. S., “The Performance of Acoustic Hoods at Low Frequencies,” Acustics, 12, pp. 139–152 (1962).Google Scholar
12. Jackson, R. S., “Some Aspects of Acoustic Hoods,” Journal of Sound and Vibration, 3, pp. 82–94 (1966).CrossRefGoogle Scholar
13. Junger, M. C., Sound through an Elastic Enclosure Acoustically Coupled to a Noise Source, ASME Paper No-WA/DE-12 (1970).Google Scholar
14. Hine, M. J., “Acoustic Hood Design in Theory and Practice,” Noise-Con, 72, pp. 278–281 (1972).Google Scholar
15. Moreland, J. B., “Low Frequency Noise Reduction of Acoustic Enclosures,” Noise Control Engineering Journal, 23, pp. 140–149 (1984).Google Scholar
16. Roberts, J., “The Principle of Noise Control with Enclosures,” Journal of Electrical and Electronic Engineering, Australia, 10, pp. 151–155 (1990).Google Scholar
17. Yeh, L. J., Chang, Y. C. and Chiu, M. C., “Optimization of Allocation and Noise Reduction on Multi-noise System by Using Genetic Algorithm,” Noise & Vibration Worldwide, 35, pp. 11–18 (2004).CrossRefGoogle Scholar
18. Chiu, M. C. and Lan, T. S., “Noise Source Identification in Sound Field by Using Simulated Annealing,” Acta Acustica United with Acustica, 93, pp. 486–497 (2007).Google Scholar
19. Blanks, J. E., Optimal Design of an Enclosure for a Portable Generator, Master Thesis, Virginia Polytechnic Institute and State University (1997).Google Scholar
20. Chiu, M. C., “The Numerical Assessment of Optimal Equipment Allocation and Acoustical Barrier Shape in a Multi-noise Plant by Using the Genetic Algorithm Method,” Journal of Marine Science and Technology, 18, pp. 56–68 (2010).Google Scholar
21. Ishida, Y., Hirayama, H., Fujita, H., Ishiguro, A. and Mori, K., Immunity-Based Systems-Intelligent System by Artificial Immune Systems, Corona Publishing Company Inc. Japan (1998).Google Scholar
22. Dasgupta, D., Artificial Immune Systems and Their Applications, Springer-Verlag (1999).Google Scholar
23. de Castro, L. N. and von Zuben, F. J., Artificial Immune Systems: Part I-Basic Theory and Applications, Technical Report TR-DCA01/99 (1999).Google Scholar
24. Carter, J. H., “The Immune System as a Model for Pattern Recognition and Classification,” Journal of the American Medical Information Association, 7, pp. 28–41 (2000).Google Scholar
25. Mori, M., Tsukiyama, M. and Fukuda, T., “Immune Algorithm with Searching Diversity and Its Application to Allocation Problem,” Transaction of Institute of Electrical Engineering of Japan, 113, pp. 872–878 (1993).Google Scholar
26. Bersini, H. and Varela, F. J., The Immune Learning Mechanisms: Reinforcement, Recruitment and Their Applications, In Paton, R. Edition, Computing with Biological Metaphors, Chapman and Hall, pp. 166–192 (1994).Google Scholar
27. Hajela, P., Yoo, J. and Lee, J., “GA Based Simulation of Immune Networks-application in Structural Optimization,” Engineering Optimization, 29, pp. 131–149 (1997).Google Scholar
28. Fukuda, T., Mori, M. and Tsukiyama, M., “Immune Network Genetic Algorithm for Adaptive Production Scheduling,” Proceeding of the 15th IFAC World Congress, 3, pp. 57–60 (1993).Google Scholar
29. Tomoyuki, M., “An Application of Immune Algorithms for Job-shop Scheduling Problems,” Proceedings of the 5th International Symposium on Assembly and Task Planning, pp. 146–150 (2003).Google Scholar
30. Knight, T. and Timmis, J., “AINE: An Immunological Approach to Data Mining,” Proceeding of the IEEE International Conference on Data Mining, pp. 297–304 (2001).Google Scholar
31. Kephart, J. O., A Biological Inspired Immune System for Computers, in Brooks, R. A. and Maes, P. (Eds.), Artificial Life IV Proceeding of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, MIT press, pp. 130–139 (1994).Google Scholar
32. Kim, J. and Bentley, P., “Negative Selection and Niching by an Artificial Immune System for Network Instrusion Detection,” Proceeding of Genetic and Evolutionary Computation Conference, pp. 149–158 (1999).Google Scholar
33. Ku, C. C, “Multimodal Topology Optimization of Structure Using Distributed Artificial Immune Algorithm,” Master Thesis, Department of Mechanical Engineering, Tatung University (2003).Google Scholar