What does this research mean for the field?

An interface-degree-of-freedom-reduced Craig–Bampton substructuring method can predict the dynamic characteristics of shaft generator systems with over 99% reduction in computational size while keeping bending mode prediction errors below 0.60%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Quantify the accuracy and computational cost of interface-degree-of-freedom-reduced Craig–Bampton substructuring for predicting natural frequency changes following rotor replacement in shaft generator systems.

September 5, 2026MachinesOpen Access

Interface-DOF-Reduced Craig–Bampton Substructuring for Efficient Dynamic Characteristic Prediction of Shaft Generator Systems

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Authors

JSJi-Min Seo SBSeunghun Baek

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Overview

Simulation study demonstrates accurate vibration prediction in shaft generator systems, indicating major computational savings through reduced-order substructuring.

Key Points

Quantify the accuracy and computational cost of interface-degree-of-freedom-reduced Craig–Bampton substructuring for predicting natural frequency changes following rotor replacement in shaft generator systems.
Divided the shafting system into shaft and rotor substructures to construct Craig–Bampton reduced-order models independently for each component.
Applied a three-stage verification pipeline comparing finite element analysis against experimental modal analysis, reduced-order modeling against finite element analysis, and reduced-order modeling against experimental modal analysis.
The Craig–Bampton model reproduced assembly bending modes with a maximum error of 0.26% compared to the parent finite element model and achieved a subspace modal assurance criterion of 1.0000 below 720 Hz, reducing model size from 192,678 to 5,379 degrees of freedom.
Retaining 20% of interface degrees of freedom achieved a 99.39% model reduction (down to 1,173 degrees of freedom) while maintaining first, second, and third bending mode prediction errors bounded below 0.60%.

Cite This Study

Seo et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4536b95aff0620ebdd9 https://doi.org/10.3390/machines14091007

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September 5, 2026MachinesOpen Access

Interface-DOF-Reduced Craig–Bampton Substructuring for Efficient Dynamic Characteristic Prediction of Shaft Generator Systems

View Full Paper

Ask AI

Bookmark

Authors

JSJi-Min Seo SBSeunghun Baek

Discussion

Member takes

Overview

Simulation study demonstrates accurate vibration prediction in shaft generator systems, indicating major computational savings through reduced-order substructuring.

Key Points

Quantify the accuracy and computational cost of interface-degree-of-freedom-reduced Craig–Bampton substructuring for predicting natural frequency changes following rotor replacement in shaft generator systems.
Divided the shafting system into shaft and rotor substructures to construct Craig–Bampton reduced-order models independently for each component.
Applied a three-stage verification pipeline comparing finite element analysis against experimental modal analysis, reduced-order modeling against finite element analysis, and reduced-order modeling against experimental modal analysis.
The Craig–Bampton model reproduced assembly bending modes with a maximum error of 0.26% compared to the parent finite element model and achieved a subspace modal assurance criterion of 1.0000 below 720 Hz, reducing model size from 192,678 to 5,379 degrees of freedom.
Retaining 20% of interface degrees of freedom achieved a 99.39% model reduction (down to 1,173 degrees of freedom) while maintaining first, second, and third bending mode prediction errors bounded below 0.60%.

Cite This Study

Seo et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4536b95aff0620ebdd9 https://doi.org/10.3390/machines14091007

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