• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1211-1222
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• 2011

It is necessary to use prestressed modal analysis to calculate the modal frequencies and mode shapes of a prestressed structure such as a spinning blade, a preloaded structure, or a thermally deformed pipe, because the prestress effect sometimes causes significant changes in the frequencies and mode shapes. When the finite element model under consideration has a very large number of degrees of freedom, repeated prestressed modal analyses for investigating the prestress effects might become too computationally expensive to finish within a reasonable design-process time. To alleviate these computational difficulties, a Krylov subspace-based model order reduction, which reduces the number of degrees of freedom of the original finite element model and speeds up the necessary prestressed modal analysis with the reduced order models (ROMs), is presented. The numerical process for the moment-matching model reduction is performed directly on the full order models (FOMs) (modeled in ANSYS) by the Arnoldi process. To demonstrate the advantages of this approach for performing prestressed modal analysis, the prestressed wheel and the compressor impeller under their high-speed rotation are considered as examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.3 s.246
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• pp.310-317
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• 2006

Currently most practical vibration and structural problems in automotive suspensions require the use of the finite element method to obtain their structural responses. When the finite element model has a very large number of degrees of freedom the harmonic and dynamic analyses are computationally too expensive to repeat within a feasible design process time. To alleviate the computational difficulty, this paper presents a moment-matching based model order reduction (MOR) which reduces the number of degrees of freedom of the original finite element model and speeds up the necessary simulations with the reduced-size models. The moment-matching model reduction via the Arnoldi process is performed directly to ANSYS finite element models by software mor4ansys. Among automotive suspension components, a knuckle is taken as an example to demonstrate the advantages of this approach for vibration simulation. The frequency and transient dynamic responses by the MOR are compared with those by the mode superposition method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.