During load operations the gas turbine is characterized by cyclic transients and long dwell times, resulting clearance variation occurs. Tight clearances in both the compressor and the turbine section is one key to high component efficiencies. However, under any operating condition, some clearance must be maintained in order to avoid contact between rotating and stationary parts. The optimization of the clearances during the running is one of the most relevant key to improve the engine performance, a Hydraulic Clearance Optimization (HCO) system is installed on the Ansaldo Energia gas-turbine models fleet in order to control turbine blade-tip clearances by appropriate shifting of the rotor against the flow direction and consequently reducing the radial gaps above the turbine blade tips. In particular the proposed procedure takes into account the HCO effect on the blade clearances and their optimizing during the operation conditions. The proposed procedure is based on a combination of 2D/3D FEM model of complete rotor-casing assembly and field operation data. The computational approach is based on a 2D axi-symmetric finite element model of the engine rotor, including the stacked rotor discs and the connecting tie-rod. Particular care has been put in the definition of the boundary conditions which are: the centrifugal loads, the tierod pre-tightening, the thermal transient conditions. To take into account 3D effect of airfoil twisting and bending, the 3D models of the assembly blade-disk are built. The 3D FE displacements of the tip blades are combined with the 2D FE displacements of the complete rotor in the evaluation process. 3D casing FEM model are built to calculate the casing distortion due to temperature difference between cylindrical shell and horizontal flange. The procedure evaluates the axial and radial clearance combining the rotor/stator displacements. The clearance and temperature values are measured and filed thanks to tip timing capacitive sensors and thermocouples sensors installed on same stages. The proposed procedure gives results in good agreement with the measured clearances in all the transient operation conditions.

Geometric uncertainties involved in the rotor blade manufacturing process are a major concern for designers. The deviation of the produced components from their nominal geometry have an impact on the Natural Frequencies (NF) that, under certain circumstances, may have negative effects on the dynamic forced response in operative conditions. Geometric defects are usually limited by imposing dimensional tolerances based on empirical considerations, simplified approach that may lead to costly manufacturing requirements that still may not guarantee safe results. This paper proposes a probabilistic representation of the geometric uncertainties for rotor blades and defines a procedure to evaluate their effects on the blade NFs. The deviation from nominal geometry is represented through the Principal Component Analysis (PCA) where it is expressed as a sum of characteristic geometric shapes (GUMs) modulated by mutually uncorrelated random variables (Principal Components, PC). The effect of each GUM is then linearly propagated on the blade NFs and a sensitivity matrix is finally defined. The procedure is applied to a case-study that concerns a set of 50 nominally identical compressor blades and the ability of GUMs to represent the effects of geometric uncertainties is tested.

Geometric uncertainties in the blade manufacturing process have important consequences in terms of dynamical properties of bladed disks. In this paper, we address the problem of modeling a full bladed disk composed by blades having uncertain geometry. The geometric imperfection of the blades is represented and analyzed according to a procedure previously presented by the authors, based on the principal component analysis (PCA) and the mesh morphing. The dynamical model of the full disk is constructed following the component mode synthesis (CMS) approach. The blade geometry is represented using a probabilistic model constructed from an experimental dataset. The effect of the geometric uncertainties is assessed using a linear uncertainty propagation approach, leading to a procedure that is fast enough to be embedded into a Monte Carlo simulation (MCS) loop.

Gas turbine engines must withstand severe thermo-mechanical conditions during present-day load operation, characterized by cyclic transients and long dwell times. Indeed engine components are subject to thermal transient conditions, thermo-mechanical strain and stress fields; those are not easily measurable during operation, making calculations hardly confirmable. All these operational factors can lead a turbine component life reduction, finally increasing lifetime costs. The developed approach has been based on several calculations, such as thermal and FEM stress evaluation on the rotor components, tuned or validated by different field measurements carried out by thermocouples in the rotor core and the pre-tightening load variation of tie-rod. Transient disks and tie-rod temperatures (calculated by an in-house Secondary Air System code) have been tuned on experimental data. Thus, for rotor thermo-mechanical analysis more reliable boundary conditions have been provided. Rotor FEM analysis has been finally checked comparing the variation of the tie-bolt tension (calculated by FEM analysis) with the experimental behaviour observed during different operating conditions.

Geometric uncertainties in the blade manufacturing process have important consequences in terms of dynamical properties of bladed disks. In this paper we address the problem of modeling a full bladed disk composed by blades having uncertain geometry. The geometric imperfection of the blades is represented and analyzed according to a procedure previously presented by the authors, based on the principal component analysis and the mesh morphing. The dynamical model of the full disk is constructed following the component mode synthesis approach. The blade geometry is represented using a probabilistic model constructed from an experimental dataset. The effect of the geometric uncertainties is assessed using a linear propagation approach, leading to a procedure that is fast enough to be embedded into a Monte Carlo simulation loop.