Model reduction using dynamic and iterated IRS techniques

Friswell, Michael I.; Garvey, Seamus D.; Penny, J.E.T.

doi:10.1006/jsvi.1995.0451

Cited by 277 publications

(157 citation statements)

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“…This method does not require a solution of the full-system eigenvalues, but it can reduce the system with better accuracy to predict the low-frequency resonances than static reduction. Friswell et al [10] extended the IRS method by using the dynamic reduction as the basic transformation instead of static reduction. They also introduced an iteratively generated correction term using the best available estimate of the reduced model to converge to the modal parameters of the full system.…”

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confidence: 99%

Robustness of System Equivalent Reduction Expansion Process on Spacecraft Structure Model Validation

Sairajan

Aglietti

2012

AIAA Journal

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Test-analysis models are used in the validation of the finite element models of spacecraft structures. Here, a probabilistic approach is used to assess the robustness of a system equivalent reduction expansion process based testanalysis model when experimental and analytical modes contain different levels of inaccuracy. The approach is applied to three spacecraft models, and Monte Carlo simulations were used to determine the sensitivity of the normalized cross-orthogonality check to the system equivalent reduction expansion process reduced matrix. The effect of parameters used in this reduction and the amount of inaccuracies that can be tolerated in the modes before failing the normalized cross-orthogonality check were also determined. The results show that the probability to pass the normalized cross-orthogonality check is highly determined by the number of modes used in the reduction. The relation between capability of the finite element models to predict the frequency-response function and the quality of the model validation determined using normalized cross-orthogonality check is also investigated, and it is observed that the quantities are not always correlated. This study also shows that the sensor locations can be optimally chosen using the system equivalent reduction expansion process reduced mass matrix, and this can increase the probability to pass the normalized cross-orthogonality check.

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confidence: 99%

Robustness of System Equivalent Reduction Expansion Process on Spacecraft Structure Model Validation

Sairajan

Aglietti

2012

AIAA Journal

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“…Then, by modal analysis using the reduced mass and stiffness, the natural frequency f could be obtained for objective function calculation. In the step, many model reduction techniques developed can be adopted, such as Guyan reduction method (Guyan 1965), Improved Reduced System (IRS) (O'Callahan 1989), Iterated Improved Reduced System (IIRS) (Friswell et al 1995), and Kuhar reduced method (Kuhar and Stahle 1974) and so on. Influences of the proposed OSP strategy using different model reduction techniques on optimal results will be discussed in section.…”

Section: Osp Calculationmentioning

confidence: 99%

Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures

Lu¹,

Wang²,

Liao³

et al. 2015

Smart Structures and Systems

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Abstract. An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little.

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“…The proposed methods are compared with the Guyan reduction (Guyan, 1964) and the Improved Reduction System (IRS) method (Friswell et al, 1995) on the one hand, and with three Second-Order Balanced Truncation (SOBT) methods on the other.…”

Section: Numerical Examplesmentioning

confidence: 99%

Extracting second-order structures from single-input state-space models: Application to model order reduction

Guillet¹,

Mourllion²,

Birouche³

et al. 2011

International Journal of Applied Mathematics and Computer Science

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This paper focuses on the model order reduction problem of second-order form models. The aim is to provide a reduction procedure which guarantees the preservation of the physical structural conditions of second-order form models. To solve this problem, a new approach has been developed to transform a second-order form model from a state-space realization which ensures the preservation of the structural conditions. This new approach is designed for controllable single-input state-space realizations with real matrices and has been applied to reduce a single-input second-order form model by balanced truncation and modal truncation.

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Model reduction using dynamic and iterated IRS techniques

Cited by 277 publications

References 0 publications

Robustness of System Equivalent Reduction Expansion Process on Spacecraft Structure Model Validation

Robustness of System Equivalent Reduction Expansion Process on Spacecraft Structure Model Validation

Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures

Extracting second-order structures from single-input state-space models: Application to model order reduction

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