A novel hybrid Neumann expansion method for stochastic analysis of mistuned bladed discs
A novel hybrid Neumann expansion method for stochastic analysis of mistuned bladed discs
The paper presents a novel hybrid method to enhance the computational efficiency of matrix inversions during the stochastic analysis of mistuned bladed disc systems. The method is based on the use of stochastic Neumann expansion in the frequency domain, coupled with a matrix factorization in the neighbourhood of the resonant frequencies. The number of the expansion terms is used as an indicator to select the matrix inversion technique to be used, without introducing any additional computational cost. The proposed method is validated using two case studies, where the dynamics an aero-engine bladed disc is modelled first using a lumped parameter approach and then with high-fidelity finite element analysis. The frequency responses of the blades are evaluated according to different mistuning patterns via stiffness or mass perturbations under the excitation provided by the engine orders. Results from standard matrix factorization methods are used to benchmark the responses obtained from the proposed hybrid method. Unlike classic Neumann expansion methods, the new technique can effectively update the inversion of an uncertain matrix with no convergence problems during Monte Carlo simulations. The novel hybrid method is more computationally efficient than standard techniques, with no accuracy loss.
241-253
Yuan, J.
4bcf9ce8-3af4-4009-9cd0-067521894797
Allegri, G.
8dd43a78-5f53-472f-853a-1b2fd7b129e4
Scarpa, F.
684472c3-1a28-478a-a388-5fd896986c1d
Patsias, S.
e7e4a982-00c2-4025-ba71-32c101489b7c
Rajasekaran, R.
c5f6d9b9-8517-49e1-afc5-5830d2d1390d
1 May 2016
Yuan, J.
4bcf9ce8-3af4-4009-9cd0-067521894797
Allegri, G.
8dd43a78-5f53-472f-853a-1b2fd7b129e4
Scarpa, F.
684472c3-1a28-478a-a388-5fd896986c1d
Patsias, S.
e7e4a982-00c2-4025-ba71-32c101489b7c
Rajasekaran, R.
c5f6d9b9-8517-49e1-afc5-5830d2d1390d
Yuan, J., Allegri, G., Scarpa, F., Patsias, S. and Rajasekaran, R.
(2016)
A novel hybrid Neumann expansion method for stochastic analysis of mistuned bladed discs.
Mechanical Systems and Signal Processing, 72-73, .
(doi:10.1016/j.ymssp.2015.11.011).
Abstract
The paper presents a novel hybrid method to enhance the computational efficiency of matrix inversions during the stochastic analysis of mistuned bladed disc systems. The method is based on the use of stochastic Neumann expansion in the frequency domain, coupled with a matrix factorization in the neighbourhood of the resonant frequencies. The number of the expansion terms is used as an indicator to select the matrix inversion technique to be used, without introducing any additional computational cost. The proposed method is validated using two case studies, where the dynamics an aero-engine bladed disc is modelled first using a lumped parameter approach and then with high-fidelity finite element analysis. The frequency responses of the blades are evaluated according to different mistuning patterns via stiffness or mass perturbations under the excitation provided by the engine orders. Results from standard matrix factorization methods are used to benchmark the responses obtained from the proposed hybrid method. Unlike classic Neumann expansion methods, the new technique can effectively update the inversion of an uncertain matrix with no convergence problems during Monte Carlo simulations. The novel hybrid method is more computationally efficient than standard techniques, with no accuracy loss.
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More information
Accepted/In Press date: 13 November 2015
Published date: 1 May 2016
Additional Information:
The authors would like to acknowledge the support of Rolls-Royce plc for the support of this research through the Composites University Technology Centre (UTC) at the University of Bristol, UK. Special acknowledgement goes also to the Strategic Investment in Low carbon Engine Technology (SILOET) programme supported by Rolls-Royce plc and Technology Strategy Board (TSB), and to the China Scholarship Council.
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Local EPrints ID: 479208
URI: http://eprints.soton.ac.uk/id/eprint/479208
ISSN: 0888-3270
PURE UUID: b716f639-ecc3-47f5-bbea-51f88bcaf123
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Date deposited: 20 Jul 2023 16:45
Last modified: 17 Mar 2024 04:20
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Author:
J. Yuan
Author:
G. Allegri
Author:
F. Scarpa
Author:
S. Patsias
Author:
R. Rajasekaran
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