Fatigue life prediction of turbine blades with geometric imperfections made of stainless steel
Makgwantsha Hermelton Mashiachidi,
Dawood A. Desai
Abstract:This research addresses critical challenges faced by steam turbine blades, particularly in low-pressure (LP) turbines, where premature failures are common due to stress concentrations at the blade root area. The study introduces a numerical methodology aimed at predicting the life of mistuned steam turbine blades, with a focus on variations in blade geometry which have received limited exploration in existing literature. A simplified, scaled-down mistuned steam turbine bladed disc model was developed using Aba… Show more
Purpose
The purpose of this paper is to investigate the silt erosion performance of Bare, 75%Cr2O3 + 25%Al2O3 and 85%Cr2O3 + 15Al2O3-coated SS304 under various control parameters such as rotation speed, concentration of silt and particle size of silt used for making slurry. This can provide insight for using chromia and alumina-based coatings for hydro-turbines.
Design/methodology/approach
Taguchi approach was used to identify the effect of three input parameters on the bare and coated alloys. L16 orthogonal array is used for determining the signal-to-noise (S/N) ratio for each process parameter. For each level of parameters taken into consideration about the erosion wear, the arithmetic mean of the S/N ratio is calculated. On the essence of the results of S/N ratios, it is possible to determine the effect of the most dominating parameters of the erosion wear.
Findings
Results show that the erosion increases with an increase in silt concentration (Wt.%). It has been analyzed that the rotational speed has the most significant effect followed by the particle size and concentration on erosion wear for all uncoated and coated SS-304 samples. Maximum resistance to erosion is provided by 85%Cr2O3 + 15%Al2O3. The least erosion wear for process parameters has occurred at the optimal parametric combination of rotational speed (N) = 415 rev/min, concentration (C) = 15 Wt.% and particle size range as <53 µm for uncoated and coated stainless steel.
Originality/value
The study clearly shows the silt erosion performance of chromia and alumina coatings of different compositions at different input parameters.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0028/
Purpose
The purpose of this paper is to investigate the silt erosion performance of Bare, 75%Cr2O3 + 25%Al2O3 and 85%Cr2O3 + 15Al2O3-coated SS304 under various control parameters such as rotation speed, concentration of silt and particle size of silt used for making slurry. This can provide insight for using chromia and alumina-based coatings for hydro-turbines.
Design/methodology/approach
Taguchi approach was used to identify the effect of three input parameters on the bare and coated alloys. L16 orthogonal array is used for determining the signal-to-noise (S/N) ratio for each process parameter. For each level of parameters taken into consideration about the erosion wear, the arithmetic mean of the S/N ratio is calculated. On the essence of the results of S/N ratios, it is possible to determine the effect of the most dominating parameters of the erosion wear.
Findings
Results show that the erosion increases with an increase in silt concentration (Wt.%). It has been analyzed that the rotational speed has the most significant effect followed by the particle size and concentration on erosion wear for all uncoated and coated SS-304 samples. Maximum resistance to erosion is provided by 85%Cr2O3 + 15%Al2O3. The least erosion wear for process parameters has occurred at the optimal parametric combination of rotational speed (N) = 415 rev/min, concentration (C) = 15 Wt.% and particle size range as <53 µm for uncoated and coated stainless steel.
Originality/value
The study clearly shows the silt erosion performance of chromia and alumina coatings of different compositions at different input parameters.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0028/
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