Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 130-142 |
| Number of pages | 13 |
| Journal | Composite Structures |
| Volume | 143 |
| Early online date | 13 Feb 2016 |
| DOIs | |
| Publication status | Published - 20 May 2016 |
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Keywords
- uncertainty quantification
- polynomial neural network
- stochastic natural frequency
- latin hypercube
- composite plate
Cite this
Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach. / Dey, S. ; Naskar, S.; Mukhopadhyay, T. ; Gohs, U. ; Spickenheuer, A. ; Bittrich, L. ; Sriramula, S.; Adhikari, S.; Heinrich, G. .
In: Composite Structures, Vol. 143, 20.05.2016, p. 130-142.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach
AU - Dey, S.
AU - Naskar, S.
AU - Mukhopadhyay, T.
AU - Gohs, U.
AU - Spickenheuer, A.
AU - Bittrich, L.
AU - Sriramula, S.
AU - Adhikari, S.
AU - Heinrich, G.
N1 - Acknowledgement SN and SS gratefully acknowledge the financial support from Lloyd’s Register Foundation Centre during this work.
PY - 2016/5/20
Y1 - 2016/5/20
N2 - This paper presents the quantification of uncertain natural frequency for laminated composite plates by using a novel surrogate model. A group method of data handling in conjunction to polynomial neural network (PNN) is employed as surrogate for numerical model and is trained by using Latin hypercube sampling. Subsequently the effect of noise on a PNN based uncertainty quantification algorithm is explored in this study. The convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM). Both individual and combined variation of stochastic input parameters are considered to address the influence on the output of interest. The sample size and computational cost are reduced by employing the present approach compared to direct Monte Carlo simulation (MCS).
AB - This paper presents the quantification of uncertain natural frequency for laminated composite plates by using a novel surrogate model. A group method of data handling in conjunction to polynomial neural network (PNN) is employed as surrogate for numerical model and is trained by using Latin hypercube sampling. Subsequently the effect of noise on a PNN based uncertainty quantification algorithm is explored in this study. The convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM). Both individual and combined variation of stochastic input parameters are considered to address the influence on the output of interest. The sample size and computational cost are reduced by employing the present approach compared to direct Monte Carlo simulation (MCS).
KW - uncertainty quantification
KW - polynomial neural network
KW - stochastic natural frequency
KW - latin hypercube
KW - composite plate
U2 - 10.1016/j.compstruct.2016.02.007
DO - 10.1016/j.compstruct.2016.02.007
M3 - Article
VL - 143
SP - 130
EP - 142
JO - Composite Structures
JF - Composite Structures
SN - 0263-8223
ER -