A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters
For the frequency response analysis of acoustic field with random and interval parameters, a nonintrusive uncertain analysis method named Polynomial Chaos Response Surface (PCRS) method is proposed. In the proposed method, the polynomial chaos expansion method is employed to deal with the random par...
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Hindawi Limited
2016-01-01
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Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2016/3693262 |
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doaj-31ab0799a686483a80db5c79846c5ea02020-11-24T21:05:58ZengHindawi LimitedShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/36932623693262A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval ParametersMingjie Wang0Zhimin Wan1Qibai Huang2State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Automobile and Transportation Engineering, Nantong Vocational University, Nantong 226000, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaFor the frequency response analysis of acoustic field with random and interval parameters, a nonintrusive uncertain analysis method named Polynomial Chaos Response Surface (PCRS) method is proposed. In the proposed method, the polynomial chaos expansion method is employed to deal with the random parameters, and the response surface method is used to handle the interval parameters. The PCRS method does not require efforts to modify model equations due to its nonintrusive characteristic. By means of the PCRS combined with the existing interval analysis method, the lower and upper bounds of expectation, variance, and probability density function of the frequency response can be efficiently evaluated. Two numerical examples are conducted to validate the accuracy and efficiency of the approach. The results show that the PCRS method is more efficient compared to the direct Monte Carlo simulation (MCS) method based on the original numerical model without causing significant loss of accuracy.http://dx.doi.org/10.1155/2016/3693262 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mingjie Wang Zhimin Wan Qibai Huang |
spellingShingle |
Mingjie Wang Zhimin Wan Qibai Huang A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters Shock and Vibration |
author_facet |
Mingjie Wang Zhimin Wan Qibai Huang |
author_sort |
Mingjie Wang |
title |
A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters |
title_short |
A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters |
title_full |
A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters |
title_fullStr |
A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters |
title_full_unstemmed |
A New Uncertain Analysis Method for the Prediction of Acoustic Field with Random and Interval Parameters |
title_sort |
new uncertain analysis method for the prediction of acoustic field with random and interval parameters |
publisher |
Hindawi Limited |
series |
Shock and Vibration |
issn |
1070-9622 1875-9203 |
publishDate |
2016-01-01 |
description |
For the frequency response analysis of acoustic field with random and interval parameters, a nonintrusive uncertain analysis method named Polynomial Chaos Response Surface (PCRS) method is proposed. In the proposed method, the polynomial chaos expansion method is employed to deal with the random parameters, and the response surface method is used to handle the interval parameters. The PCRS method does not require efforts to modify model equations due to its nonintrusive characteristic. By means of the PCRS combined with the existing interval analysis method, the lower and upper bounds of expectation, variance, and probability density function of the frequency response can be efficiently evaluated. Two numerical examples are conducted to validate the accuracy and efficiency of the approach. The results show that the PCRS method is more efficient compared to the direct Monte Carlo simulation (MCS) method based on the original numerical model without causing significant loss of accuracy. |
url |
http://dx.doi.org/10.1155/2016/3693262 |
work_keys_str_mv |
AT mingjiewang anewuncertainanalysismethodforthepredictionofacousticfieldwithrandomandintervalparameters AT zhiminwan anewuncertainanalysismethodforthepredictionofacousticfieldwithrandomandintervalparameters AT qibaihuang anewuncertainanalysismethodforthepredictionofacousticfieldwithrandomandintervalparameters AT mingjiewang newuncertainanalysismethodforthepredictionofacousticfieldwithrandomandintervalparameters AT zhiminwan newuncertainanalysismethodforthepredictionofacousticfieldwithrandomandintervalparameters AT qibaihuang newuncertainanalysismethodforthepredictionofacousticfieldwithrandomandintervalparameters |
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1716767212243517440 |