Geometric design of friction ring dampers in blisks using nonlinear modal analysis and Kriging surrogate model

Abstract

Integrally bladed disks (blisk) have been widely used in the turbo-machinery industry due to its high aerodynamic performance and structural efficiency. A friction ring damper (FRD) is usually integrated in the system to improve its low damping. However, the design of the geometry of this FRD become complex and computationally expensive due to the strong nonlinearities from friction interfaces. In this work, we propose an efficient modelling strategy based on advanced nonlinear modal analysis and Kriging surrogate models to design and optimize the geometry of a 3D FRD attached to a high fidelity full-scale blisk. The 3D ring damper is parametrised with a few key geometrical parameters. The impact of each geometric parameter and their sensitivities to nonlinear dynamic response can be efficiently assessed using Kriging meta-modelling based on a few damped nonlinear normal modes. Results demonstrate that the damping performances of ring dampers can be substantially optimized through the proposed modelling strategy whilst key insights for the design of the rings are given. It is also demonstrated that the distribution of the contact normal load on the contact interfaces has a strong influence on the damping performances and can be effectively tuned via the upper surface geometry of the ring dampers.

A multilevel data-driven Bayesian approach for stochastic model updating of nonlinear aeroelastic systems

“…Even with this low degree of freedom case study, this process can take between 3-10 seconds. A Kriging algorithm is used to train data-driven model f [36]. To effectively capture subcritical behaviour, f is set up with 𝐾 𝛼2 , 𝐾 𝛼3 and LCO amplitude as inputs and velocity as the output.…”

Data-Driven Bayesian Inference for Stochastic Model Identification of Nonlinear Aeroelastic Systems

“…Kernel-based surrogate models namely, Kriging (Gaussian Process (GP) regression) has been employed for cases with limited training data [52,53]. Kriging models have been found to be remarkably effective in modeling complex nonlinear systems with the added benefit of requiring only minimal training data [54]. A major benefit of Kriging is the ability to seamlessly integrate new observations into the current model framework [55,56].…”

“…Kriging surrogate models have been employed to propagate uncertainty in bifurcation diagrams of landing gear designs by Tartaruga [57] and ring damper designs by Sun et.al. [54]. Lee et.al.…”

Data-driven Bayesian inference for stochastic model identification of nonlinear aeroelastic systems

“…Nonlinear prediction can be achieved effectively through the use of data-driven methods such as Polynomial Chaos Expansion (PCE) and neural networks [44][45][46][47]. Kriging models, consisting of weighted regression functions and correlation functions, have been also found to be remarkably effective in modeling complex nonlinear systems with the added benefit of requiring only minimal training data [48]. A major benefit of Kriging is the ability to seamlessly integrate new observations into the current model framework [49,50].…”