Abstract
An integrated reverse engineering methodology is proposed for a large-scale fully operational steam turbine rotor, considering issues related to the development of the CAD and FE model and the application of robust and effective computational finite element model updating techniques based on experimental modal analysis procedures. First, using an integrated reverse engineering strategy, the digital shape of the three sections of a steam turbine rotor was developed and the final parametric CAD model was created. The finite element model of the turbine was developed using tetrahedral solid elements. Due to complex geometry of the structure, the developed model consists of about fifty-five million DOFs. The identification of modal characteristics of the frame is based on acceleration time histories, which are obtained through an experimental investigation of its dynamic response in a support-free state by imposing impulsive loading. Experimentally identified modal modes and modal frequencies compared to the FE model predicted ones constitute the actual measure of fit. CMA-ES optimization algorithm is then implemented in order to finely tune material parameters, such as modulus of elasticity and density, in order to best match experi-mental and numerical data. Direct comparison of the numerical and experimental data verified the reliability and accuracy of the methodology applied. The identified finite element model is repre-sentative of the initial structural condition of the turbine and is used to develop a simplified finite element model, which then used for the turbine rotordynamic analysis. Accumulated knowledge of the dynamic behavior of the specific steam turbine system, could be implemented in order to eval-uate stability or instability states, fatigue growth in the turbine blades, changes in the damping of the bearing system and perform necessary scheduled optimal and cost-effective maintenance strategies. Additionally, upon a series of scheduled experimental data collection, a permanent output-only vibration SHM system could be installed and even a proper dynamic balancing could be investigated and designed.
Highlights
The largest proportion of electricity worldwide is produced using some kind of turbine (Steam, Hydro, Nuclear, etc.) As large density of energy flows through the turbines, the rotation speeds are extremely high, large inertia loads are developed, shaft and blade deform extensively, blades corrode and high levels of vibration occur, all leading to strong dynamic instabilities (Bavastri et al, 2008)
Two parts are used to model the blades and their circumferential ring at small diameter stages, whereas three parts are used to model eight large diameter stages of the intermediate pressure assigning two parts for the blades and one for the circumferential ring. Both the rotor shaft and the turbine blades have been built from a single material, steel, the mechanical treatment that all parts undertake in order to take their final form and shape as well as their assembly may change the overall stiffness of the structure related to an FE model where assumption are made beforehand
The Covariance Matrix Adaptation—Evolution Strategy (CMA-ES) framework introduced in previous work (Giagopoulos and Arailopoulos, 2017) is applied to update the developed FE models
Summary
The largest proportion of electricity worldwide is produced using some kind of turbine (Steam, Hydro, Nuclear, etc.) As large density of energy flows through the turbines, the rotation speeds are extremely high, large inertia loads are developed, shaft and blade deform extensively, blades corrode and high levels of vibration occur, all leading to strong dynamic instabilities (Bavastri et al, 2008). Measured and predicted modal parameters are used to quantify the discrepancy between numerical and experimental models, defining both modal and response residuals (Giagopoulos and Arailopoulos, 2015a,b, 2016; Arailopoulos and Giagopoulos, 2016), in a single-objective optimization problem. Accumulated knowledge of the dynamic behavior of the steam turbine system, could be later implemented in order to evaluate stability or instability states, fatigue growth in the turbine blades, changes in the damping of the bearing system and perform necessary scheduled optimal and cost-effective maintenance strategies (Bavastri et al, 2008; Booysen et al, 2015; Plesiutschnig et al, 2016). The experimental modal identification procedure is described, in order to identify modal modes and frequencies of the actual structures of the intermediate pressure sections. J3(θ ) and J4(θ ) are selected to represent the measure of fit corresponding to the identified and predicted FRFs of the system:
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