Effects of Water-to-Air Mass Ratio on Long-Term Washing Efficiency and Erosion Risk in an Axial Compressor Under Online Washing Conditions

Abstract

Abstract One of the main reasons of gas turbines performance losses is the deposition of dirt on the compressor blades. Dirt deposit has to be periodically removed to keep the engine performance as high as possible. This is the reason motivating the presence of online water washing systems (WWS) in most of the compressor gas turbines. Such systems aim at cleaning the compressor blades to recover efficiency; thus, the larger the water flow the better it is assumed the compressor is cleaned (fixing all the other conditions). In the present work we simulate the long-term behaviour of a real axial compressor, from the inlet to the first-stage rotor, subject to online water washing with different water flow rates. The frozen rotor approach is adopted to solve the flow field in the rotor region. Simulations are performed by using the unsteady k-ε realizable model coupled with a Lagrangian tracking of the injected liquid phase. Water droplets erosion is handled by using a semi-empirical model developed by the authors. In each simulation 504000 parcels have been tracked, providing statistically reliable predictions. To simulate long-term evolution of the washing process, a discrete mesh morphing technique coupled with the use of specific scale factors is adopted. Each of the tested configuration is composed of three successive erosive steps up to the blade compressor end-of-life. Six different injection configurations are here assessed in terms of long-time average washing efficiency and erosion risk. Results predicted, show the dependence of the considered washing indices on water mass flow rate and set the stage for the development of a washing optimization tool, which helps the design and management processes. In the present simulations the optimal configurations are WAMF* = 0.250 in the case that a small weight is given to the washing indices, and WAMF* = 0.750 for high weights.