scholarly journals Experimental Research on Water Droplet Erosion Resistance Characteristics of Turbine Blade Substrate and Strengthened Layers Materials

Materials ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4286
Author(s):  
Juan Di ◽  
Shunsen Wang ◽  
Xiaojiang Yan ◽  
Xihang Jiang ◽  
Jinyi Lian ◽  
...  
Keyword(s):  
Solid Solution ◽  
Steam Turbine ◽  
Turbine Blade ◽  
Laser Cladding ◽  
Water Droplet ◽  
Positive Contribution ◽  
Water Droplet Erosion ◽  
Stellite Alloy ◽  
New Findings ◽  
Starting Point

In this paper, the water droplet erosion (WDE) performance of typical martensitic precipitation substrate 0Cr17Ni4Cu4Nb in steam turbine final stage, laser solid solution strengthened sample, laser cladding sample and brazed stellite alloy samples have been studied based on a high-speed rotating waterjet test system. The WDE resistance of several materials from strong to weak is in sequence: Brazed stellite alloy > laser cladding sample > laser solid solution sample > martensitic substrate. Furthermore, the WDE resistance mechanism and the failure mode of brazed stellite alloy have been revealed. It is found that the hard carbide in the stellite alloy is the starting point of crack formation and propagation. Under the continuous droplet impact, cracks grow and connect into networks, resulting in the removal of carbide precipitates and WDE damage. It is proved that the properties of the Co-based material itself is the reason for its excellent WDE resistance. And the carbides have almost no positive contribution to its anti-erodibility. These new findings are of great significance to process methods and parameter selection of steam turbine blade materials and surface strengthened layers.

Water-droplet erosion behavior of high-velocity oxygen-fuel-sprayed coatings for steam turbine blades

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Dingjun Li ◽  
Peng Jiang ◽  
Fan Sun ◽  
Xiaohu Yuan ◽  
Jianpu Zhang ◽  
...  
Keyword(s):  
Steam Turbine ◽  
Water Droplet ◽  
Erosion Resistance ◽  
Turbine Blades ◽  
Impingement Angle ◽  
Erosion Behavior ◽  
Water Droplet Erosion ◽  
Steam Turbine Blades ◽  
Oxygen Fuel ◽  
Sprayed Coatings

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.

Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning

2020 ◽  
Author(s):  
Zheyuan Zhang ◽  
Tianyuan Liu ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Machine Learning ◽  
Turbine Blade ◽  
Image Recognition ◽  
Water Droplet ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Feature Maps ◽  
Water Droplet Erosion ◽  
Data Processing Method

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.

Numerical Investigation of Stator Blades Bow Effect on the Rotor Blade Erosion of a Wet Steam Turbine

2014 ◽  
Vol 670-671 ◽  
pp. 769-773
Author(s):  
Hong Yao ◽  
Wan Long Han ◽  
Shi Ming Pan ◽  
Zhong Qi Wang
Keyword(s):  
Steam Turbine ◽  
Numerical Investigation ◽  
Water Droplet ◽  
Mechanical Design ◽  
Design Method ◽  
Three Dimensional ◽  
Rotor Blades ◽  
Wet Steam ◽  
Water Droplet Erosion ◽  
Long Time

The water droplet erosion protection of the rotor blades has been an important issue for a long time, regardless of the design. The aim of this paper is to present a aerodynamic design method for decrease risk of water droplet erosion in wet steam turbine, as well as to present the comparison between then five diffrent bow stator blades. This paper also presents numerical investigation of three dimensional wet steam flows in a stage. This stage has long transonic blades designed using recent aerodynamic and mechanical design methods. The results show that, the one of the five diffrent bow stator blades decrease rist of water droplet erosion of rotaional blades, and the change of the efficiency is small.

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
Keyword(s):  
Steam Turbine ◽  
Water Droplet ◽  
Turbine Blades ◽  
Erosion Model ◽  
Water Droplet Erosion ◽  
Steam Turbine Blades

Water droplet erosion of stainless steel steam turbine blades

2017 ◽  
Vol 4 (8) ◽  
pp. 086510 ◽  
Author(s):  
H S Kirols ◽  
D Kevorkov ◽  
A Uihlein ◽  
M Medraj
Keyword(s):  
Stainless Steel ◽  
Steam Turbine ◽  
Water Droplet ◽  
Turbine Blades ◽  
Water Droplet Erosion ◽  
Steam Turbine Blades

Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning

2021 ◽  
Author(s):  
Zheyuan Zhang ◽  
Tianyuan Liu ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Machine Learning ◽  
Turbine Blade ◽  
Image Recognition ◽  
Water Droplet ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Feature Maps ◽  
Water Droplet Erosion ◽  
Data Processing Method

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 (?) is defined. The life coefficient (?) 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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