Vol.8, No1, 2008, pp. 3-12
UDC 620.169.1:621.22-253

PROBABILISTIC MODEL FOR PITTING CORROSION AND FATIGUE LIFE ESTIMATION OF TURBINE BLADES

Yasmina Assoul 1,2

N. Bacha, D. Semmar 2

1 Faculty of Mechanical Engineering, University of Belgrade

2 Faculty of Engineering Sciences, Université Saâd Dahleb Blida, Algéria

Abstract

Operating conditions of turbine blades are very complicated because of the mechanical loads and severe environmental conditions of corrosively active fluid. Pressure and temperature cause mechanical load variations at start-up and shut-down, and may deteriorate dynamic properties of rotor blades.

Various corrosion mechanisms could develop on turbine blades depending on the operational environment. However, several updated studies of this problem show that the dominant failure mechanism in turbine blades results from fatigue crack propagation, initiating on pitting corrosion defects. This is a complex electrochemical and mechanical failure mechanism.

In order to prevent the occurrence of the unacceptable damage, today’s research is aimed at defining a procedure for estimating the lifetime and development of damage in studied structural parts, and in scheduling optimal periods for inspection by conventional methods. Considering the very large dispersion of relevant parameters and the lack of well defined models that exactly define the damaging process, the model developed here is a probabilistic damage tolerance model.

The model takes into account that most parameters are likely statistical, and based on this it attempts to determine the best probabilistic evaluation of the occurring failure. The concept of the studied model consists of seven stages: initiation of defect that creates the pit; pit growth; short crack initiation from pit; short crack growth; transition from short to long crack growth; long crack growth; and final fracture. This complex model coincides with several, not yet well defined, physical concepts and some proven events. By applying probability, the suggested mechanistic model estimates the probability of failure that abides by the results of classical deterministic models.

Keywords: corrosion, pitting, fatigue, crack growth, damage, life prediction, probability, reliability

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