Abstract
This paper is devoted to investigation on thermal diffusion and thermomechanical coupling in gas foil bearing (GFB). The authors shortly describe the construction of GFB as well as fundamental advantages emerging from its applications. As being critical to the scope of the present study, one of the most significant reason of GFB damages is discussed, namely thermal stability loss. The known methods used to avoid the above mentioned problem are also shortly described. Moreover, the elaborated test rig is presented, which is planned to be used to identify the properties of GFB, including characterization of working conditions for the selected component of GFB’s supporting layer – a top foil. As discussed in the paper, the experimental work will enable numerical model validation procedure. Finally, the authors introduce the numerical tools and methods proposed to model thermal diffusion and thermomechanical coupling. The multiphysics approach in numerical simulations is also considered to effectively address the nature of the modeled phenomena in GFB, taking into account strain and temperature fields. Commercial finite element code, nonlocal formulations for finite differences, as well as, peridynamics are shortly characterized to show modeling capabilities for GFB. Exemplary results of numerical simulations for the modeled thermal diffusion are also presented. The paper is complemented with the description of the proposed application of shape memory alloys to improve the elasto-damping properties of GFB.
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Acknowledgement
The authors acknowledge the project „Mechanisms of stability loss in high-speed foil bearings - modeling and experimental validation of thermomechanical couplings”, no. 2017/27/B/ST8/01822 financed by the National Science Center, Poland.
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Kantor, S., Roemer, J., Pawlik, J., Żywica, G., Bagiński, P., Martowicz, A. (2019). Properties identification for gas foil bearings - experimental instrumentation and numerical approach. In: Uhl, T. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-20131-9_342
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DOI: https://doi.org/10.1007/978-3-030-20131-9_342
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