Analysis and prediction of gas turbine performance with evaporative cooling processes by developing a stage stacking algorithm. (20th December 2020) - Salehi, Mirhamed; Eivazi, Hamidreza; Tahani, Mojtaba; Masdari, Mehran
Abstract: Water injection in stationary gas turbines is effective strategy for power augmentation and gas turbine efficiency improvement, in particular, at high ambient temperatures. In this study, a developed method for simulation of wet compression processes is introduced. This method is followed by the droplet evaporation analysis, aero-thermodynamic stage-stacking model, and a map zooming technique for evaluation the unknown parameters in the generalized performance curves by using grey wolf optimization (GWO) algorithm. The validity of the proposed algorithm is assessed through the comparison of the results with two experimental studies. The operating results are calculated for five different cities and 18 gas turbines organized in three classes. Moreover, a sensitivity analysis on the main input parameters is investigated with the use of variable importance (VI) analysis by constructing and training an artificial neural network. The results show that the variation of the output parameters is highly sensitive to the ambient temperature, relative humidity, and turbine inlet temperature (TIT). Results demonstrate that saturated fogging plus 1% overspray leads to a relative increase of 24.84% and 6.70% in net power output and thermal efficiency, respectively, at the corresponding ambient condition. It is observed that 23.94% of the increase in the inlet mass flow rate in this cooling approach is due to the injected water directly, where the compressor operating point is matched at a point with a higher inlet mass flow rate comparing to dry condition. Graphical abstract: Image 1 Highlights: A new method for simulation of fog and wet compression processes. Combination of stage stacking, triangles velocity analysis, and stage characteristic map. Development of a map zooming technique based on Gray Wolf Opti-mization (GWO) algorithm. Sensitivity analysis of the wet compression process using Variable Im-portance (VI). Estimating the operating parameters for 18 gas turbines organized in three classes.