Application of Polynomial Chaos Expansion and Model Order Reduction for Dynamic Analysis of Structures with Uncertainties
Ji Dong Yang, Béatrice Faverjon, Herwig Peters, Nicole Kessissoglou
Abstract
Open-access reader
Ji Dong Yang, Béatrice Faverjon, Herwig Peters, Nicole Kessissoglou
Abstract
Open-access reader
Structural uncertainties greatly influence the dynamic responses of engineering structures. This work examines the variability in the frequency responses of a simply supported plate with uncertainties in its Young's modulus and damping. The polynomial chaos expansion method is used to model the uncertainties, which transforms the stochastic system equations to a set of deterministic equations. The results obtained from the stochastic model are compared with Monte Carlo simulations. To reduce the model order, the polynomial chaos expansion method is combined with the Arnoldi-based Krylov subspace technique. By reducing the number of equations involved in the numerical model, the computational efficiency is significantly increased.
OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Structural uncertainties greatly influence the dynamic responses of engineering structures. This work examines the variability in the frequency responses of a simply supported plate with uncertainties in its Young's modulus and damping. The polynomial chaos expansion method is used to model the uncertainties, which transforms the stochastic system equations to a set of deterministic equations. The results obtained from the stochastic model are compared with Monte Carlo simulations. To reduce the model order, the polynomial chaos expansion method is combined with the Arnoldi-based Krylov subspace technique. By reducing the number of equations involved in the numerical model, the computational efficiency is significantly increased.
Key concepts: Polynomial chaos, Krylov subspace, Reduction (mathematics), Applied mathematics, Polynomial, Model order reduction, Monte Carlo method, Polynomial expansion