Development of a water droplet erosion model for large steam turbine blades

2003 ◽  
Vol 17 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Byeong- Eun Lee ◽  
Kap- Jong Riu ◽  
Se- Hyun Shin ◽  
Soon- Bum Kwon
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Dingjun Li ◽  
Peng Jiang ◽  
Fan Sun ◽  
Xiaohu Yuan ◽  
Jianpu Zhang ◽  
...  

Abstract The water-droplet erosion of low-pressure steam turbine blades under wet steam environments can alter the vibration characteristics of the blade, and lead to its premature failure. Using high-velocity oxygen-fuel (HVOF) sprayed water-droplet erosion resistant coating is beneficial in preventing the erosion failure, while the erosion behavior of such coatings is still not revealed so far. Here, we examined the water-droplet erosion resistance of Cr3C2–25NiCr and WC–10Co–4Cr HVOF sprayed coatings using a pulsed water jet device with different impingement angles. Combined with microscopic characterization, indentation, and adhesion tests, we found that: (1) both of the coatings exhibited a similar three-stage erosion behavior, from the formation of discrete erosion surface cavities and continuous grooves to the broadening and deepening of the groove, (2) the erosion rate accelerates with the increasing impingement angle of the water jet; besides, the impingement angle had a nonlinear effect on the cumulative mass loss, and 30° sample exhibited the smallest mass loss per unit area (3) an improvement in the interfacial adhesion strength, fracture toughness, and hardness of the coating enhanced the water-droplet erosion resistance. These results provide guidance pertaining to the engineering application of water erosion protective coatings on steam turbine blades.


2017 ◽  
Vol 4 (8) ◽  
pp. 086510 ◽  
Author(s):  
H S Kirols ◽  
D Kevorkov ◽  
A Uihlein ◽  
M Medraj

Author(s):  
Joerg Schuerhoff ◽  
Andrei Ghicov ◽  
Karsten Sattler

Blades for low pressure steam turbines operate in flows of saturated steam containing water droplets. The water droplets can impact rotating last stage blades mainly on the leading edge suction sides with relative velocities up to several hundred meters per second. Especially on large blades the high impact energy of the droplets can lead to a material loss particularly at the inlet edges close to the blade tips. This effect is well known as “water droplet erosion”. The steam turbine manufacturer use several techniques, like welding or brazing of inlays made of erosion resistant materials to reduce the material loss. Selective, local hardening of the blade leading edges is the preferred solution for new apparatus Siemens steam turbines. A high protection effect combined with high process stability can be ensured with this Siemens hardening technique. Furthermore the heat input and therewith the geometrical change potential is relatively low. The process is flexible and can be adapted to different blade sizes and the required size of the hardened zones. Siemens collected many years of positive operational experience with this protection measure. State of the art turbine blades often have to be developed with precipitation hardening steels and/or a shroud design to fulfill the high operational requirements. A controlled hardening of the inlet edges of such steam turbine blades is difficult if not impossible with conventional methods like flame hardening. The Siemens steam turbine factory in Muelheim, Germany installed a fully automated laser treatment facility equipped with two co-operating robots and two 6 kW high power diode laser to enable the in-house hardening of such blades. Several blade designs from power generation and industrial turbines were successfully laser treated within the first year in operation. This paper describes generally the setup of the laser treatment facility and the application for low pressure steam turbine blades made of precipitation hardening steels and blades with shroud design, including the post laser heat treatments.


Author(s):  
Zheyuan Zhang ◽  
Bin Yang ◽  
Di Zhang ◽  
Yonghui Xie

In this paper, the water droplet erosion ( WDE) characteristics of four blade materials under different high-speed liquid-solid impingement conditions are tested with the experimental steps and data processing methods in ASTM-G73. On the basis of cumulative erosion curves, the maximum erosion rate ( ERmax) is obtained to analyze the WDE resistance of the testing materials quantitatively. The effects of target surface roughness, impact angle and impact velocity on the WDE characteristics of blade materials under specific working conditions are studied. The velocity coefficients ( n) of the testing materials under high-speed liquid-solid impact are analyzed. In order to investigate the mechanism of WDE, the material failure characteristics and shallow layer hardness ( SLH) at three characteristic moments corresponding to different WDE periods, are investigated with the SEM morphology of erosion section and hardness distribution along the depth direction at the impact position. The research results can provide reference data and technical support for the structure design and material selection of steam turbine blades.


2007 ◽  
Vol 41 (5) ◽  
pp. 295-301
Author(s):  
A. I. Danilin ◽  
S. I. Adamov ◽  
A. Zh. Chernyavskii ◽  
M. I. Serpokrylov

Author(s):  
Zheyuan Zhang ◽  
Tianyuan Liu ◽  
Di Zhang ◽  
Yonghui Xie

Abstract In this paper, a method for predicting remaining useful life (RUL) of turbine blade under water droplet erosion (WDE) based on image recognition and machine learning is presented. Using the experimental rig for testing the WDE characteristics of materials, the morphology pictures of specimen surface at different times in the process of WDE are collected. According to the data processing method of ASTM-G73 and the cumulative erosion-time curves, the WDE stages of materials is quantitatively divided and the WDE life coefficient (ζ) is defined. The life coefficient (ζ) could be used to calculate the RUL of turbine blades. One convolutional neural network model and three machine learning models are adopted to train and predict the image dataset. Then the training process and feature maps of the Resnet model are studied in detail. It is found that the highest prediction accuracy of the method proposed in this paper can be 0.949, which is considered acceptable to provide reference for turbine overhaul period and blade replacement time.


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