The purpose of this paper is to propose an effective strategy for the design of turbine blades with underplatform dampers (UPDs). The strategy involves damper “pre-optimization,” already proposed by the authors, to exclude, before the blades-coupled nonlinear calculation, all those damper configurations leading to low damping performance. This paper continues this effort by applying pre-optimization to determine a damper configuration which will not jam, roll, or detach under any in-plane platform kinematics (i.e., blade bending modes). Once the candidate damper configuration has been found, the damper equilibrium equations are solved by using both the multiharmonic balance method (MHBM) and the direct-time integration (DTI) for the purpose of finding the correct number of Fourier terms to represent displacements and contact forces. It is shown that contrarily to non-preoptimized dampers, which may display an erratic behavior, one harmonic term together with the static term ensures accurate results. These findings are confirmed by a state-of-the-art code for the calculation of the nonlinear forced response of a damper coupled to two blades. Experimental forced response functions (FRF) of the test case with a nominal damper are available for comparison. The comparison of different damper configurations offers a “high-level” validation of the pre-optimization procedure and highlights the strong influence of the blades mode of vibration on the damper effectiveness. It is shown that the pre-optimized damper is not only the most effective but also the one that leads to a faster and more flexible calculation.
The Relevance of Damper Pre-Optimization and Its Effectiveness on the Forced Response of Blades
Manuscript received October 4, 2017; final manuscript received November 4, 2017; published online April 5, 2018. Editor: David Wisler.
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Gastaldi, C., Berruti, T. M., and Gola, M. M. (April 5, 2018). "The Relevance of Damper Pre-Optimization and Its Effectiveness on the Forced Response of Blades." ASME. J. Eng. Gas Turbines Power. June 2018; 140(6): 062505. https://doi.org/10.1115/1.4038773
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