@Article{icces.2023.09388,
AUTHOR = {Xuhang Lin, Haibo Chen},
TITLE = {Robust Shape Optimization of Sound Barriers Based on Isogeometric  Boundary Element Method and Polynomial Chaos Expansion},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {27},
YEAR = {2023},
NUMBER = {1},
PAGES = {1--1},
URL = {http://www.techscience.com/icces/v27n1/54091},
ISSN = {1933-2815},
ABSTRACT = {As an important and useful tool for reducing noise, the sound barrier is of practical significance. The sound 
barrier has different noise reduction effects for different sizes, shapes and properties of the sound absorbing 
material. Liu et al. [1] have performed shape optimization of sound barriers by using isogeometric boundary 
element method and method of moving asymptotes (MMA). However, in engineering practice, it is difficult 
to determine some parameters accurately such as material properties, geometries, external loads. 
Therefore, it is necessary to consider these uncertainty conditions in order to ensure the rationality of the 
numerical calculation of engineering problems. In this study, based on isogeometric boundary element 
method, a robust optimization design method for the shape optimization of the sound barrier is proposed. 
The uncertainties of the wavenumber of the acoustic wave and the coefficient of the sound absorbing 
material are considered. The uncertainty of the surface admittance of the sound absorbing material is 
described by a random field, and the expansion optimal linear estimation (EOLE) method is used to 
discretize it into a series of uncorrelated random variables. Meanwhile, the stochastic response of the 
structure is calculated with the polynomial chaos expansion (PCE) method and isogeometric boundary 
element method. The sensitivity of the stochastic response is also obtained by the PCE method. The weighted 
sum of the mean and standard deviation of the stochastic response is set as the objective function for the 
robust design optimization. Finally, the optimization problem is also solved by MMA. Numerical examples 
show that the results of the proposed robust optimization method are more suitable in the situation whose 
parameters of the material and load are uncertain than the results of the deterministic optimization.},
DOI = {10.32604/icces.2023.09388}
}