Train carbody and truck structures are designed to exhibit primary natural frequency modes great enough to avoid unwanted resonant oscillations with normal track interactions. Critical bounce modes can be excited by typical track in the 2–4 Hz range. Trains are designed with first modes above this threshold. Historically, simplified approaches are employed to predict natural frequencies of the main truck and carbody train structures independently. Since the advent of high powered computing, more detailed finite element analysis (FEA) eigenvalue approaches have been used to more accurately predict natural frequency of structures. Still, the typical FEA approach uses simplified boundary conditions and partial models to determine natural frequencies of individual components, neglecting the interaction with other connected structures. In this paper, a detailed, holistic approach is presented for an entire Light Rail Vehicle (LRV). The analysis is performed on a fully detailed FEA model of the LRV, including trucks and suspension, carbody structures, non-structural mass, articulation, as well as intercar and truck-carbody connections. The model was developed for detailed crashworthiness investigations, which requires a high level of fidelity compared to what is typically required for static and modal analysis. Using the same model for multiple purposes speeds up development while also improving the accuracy of the analyses. In this paper, the modal analysis methodology developed is described. A case study is presented investigating the often neglected contribution of windows, cladding, and flooring on the overall carbody natural frequency.
Predicting the Natural Frequency of Train Structures Using Detailed Finite Element Models
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MacNeill, RA, & Gough, G. "Predicting the Natural Frequency of Train Structures Using Detailed Finite Element Models." Proceedings of the 2016 Joint Rail Conference. 2016 Joint Rail Conference. Columbia, South Carolina, USA. April 12–15, 2016. V001T02A013. ASME. https://doi.org/10.1115/JRC2016-5835
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