A Prognostics Approach Connecting Oxidation Damage to Mechanical Criteria

Oxidation of gas turbine parts is one of the damage mechanisms limiting firing rate and outage interval, thereby reducing potential efficiency and increasing operational cost. While oxidation isn’t an immediate integrity threat in itself, it has indirect impact by reducing load carrying cross sections, changing weight and stiffness distribution, causing cooling air leakages, changing material properties, changing parts aerodynamics characteristics et cetera. This in turn potentially results in shorter creep life, drifting eigenfrequencies, overheating of other parts, increased brittleness and performance loss. Since it is very complicated to analyze all these possible situations in detail, parts are often rejected because of their appearance rather than because of actually approaching a level of damage where it will have consequences on the operability of the turbine. Further, the rules tend to be general rather than customer specific, being set for the entire allowed operation envelope of the part rather than based upon the particular conditions of the unit where the part is in service. This paper presents a simple, first-step prognostics model that connects oxidation damage to local one-dimensional stress and stiffness and local cross-sectional force. An example is given where simple oxidation models are used to predict a detailed oxidation state with regard to multiple aspects. Herein, this oxidation state includes different aspects with regard to the type of oxidation and includes additional characteristics to be considered in the following. By connecting the model to measured characteristics instead of pure oxidation criteria it will be easier to: firstly, apply more relevant criteria that can be evaluated on a site-by-site basis. This will allow high-precision oxidation prognostics with criteria relevant for the operational safety of the equipment. Secondly, more accurately compare predictions to experiences, allowing more detailed operation experience evaluation as well as more relevant input to root cause investigations. This will allow more accurate root cause determination and will result in fewer data points needed to draw statistically sound conclusions from field experiences. Providing such possibility has a significant impact on corresponding applications, as it includes larger operation. Simultaneously, risks can be controlled and quantified better than today providing an important benefit for an application like gas turbines depending strongly on the reliability of the components utilized. The model is general in its nature and is formulated to allow application with any oxidation rule that can be formulated as a mathematical algorithm that is suitable for time integration. The main limitations are its restriction to one space dimension and the assumption of a constant temperature field. If the model is found useful, natural next steps are to extend the model to three space dimensions and a more complex temperature model. The model should also be tested with more accurate oxidation models.