Aircraft often operate in humid atmospheres, where water-vapor phase changes can alter the flow structure. Although buffeting is observed around airfoils under certain operating conditions in the transonic regime, the effects of humidity on this phenomenon remain insufficiently understood. In this study, numerical simulations were conducted to clarify the effects of atmospheric humidity on transonic buffet over an airfoil. A delayed detached-eddy simulation was applied to the transonic flow around an OAT15A supercritical airfoil, which exhibited a typical buffet phenomenon in the experiment. The governing equations were coupled with a nonequilibrium condensation model that included homogeneous nucleation and droplet growth. The free-stream relative humidity (RH) was varied from 0% to 100%. Under the dry condition, a typical transonic buffet is reproduced, consistent with the experimental observations. As the humidity increases, the latent heat release in the supersonic region ahead of the main shock reduces the effective Mach number, weakens the main shock, and shifts it downstream. At an intermediate humidity (approximately 60% RH), the flow becomes quasi-steady: a condensation shock forms upstream, and a lambda-shaped stepped compression appears, which eases the shock/boundary-layer interaction and strongly suppresses the large-amplitude buffet. At a higher humidity, a self-sustained unsteadiness develops, characterized by moving shocks that detach from the main shock and propagate toward the leading edge. This resulted in oscillations with a higher dominant frequency than in dry air. Dynamic mode decomposition shows that the suction-side feedback mechanisms differ between dry and high-humidity conditions. Moreover, aerodynamic performance varied nonmonotonically with humidity.

Abstract In Japan, gas turbine combined cycle (GTCC) power plants are currently operated under rapid start-up, shutdown, and partial-load conditions to maintain a stable power supply, suppressing the fluctuation of electric power due to renewable energy sources such as solar and wind power. The off-design condition may cause unsteady and unstable flows in the compressor stage, increasing mechanical stresses on the blades. To reveal the critical flow behavior and avoid blade failure, an innovative method that can clarify the flow physics in these systems is urgently required. This study proposes a classification method for time-series big data obtained from full-annulus flow computations under various flow conditions assisted by the self-organizing map (SOM) method, which is a popular machine-learning technique. First, full-annulus unsteady flow computations under various flow conditions were conducted for an actual compressor in an industrial gas turbine at a GTCC power plant. The inlet guide vane (IGV) angle was set to various values that corresponded to the full-load operation and transitional flow rate conditions during partial-load operation. The time-series data of the load on the blades in the various cases were obtained from the computations. All time-series data were converted into frequency data using fast Fourier transform (FFT) and classified by the SOM. As a result, the proposed classification method could appropriately classify the critical flow conditions in which large and unstable pressure fluctuations are observed. This method may be used to avoid critical operating conditions that lead to blade failure.

Abstract Gas turbine combined cycle (GTCC) power plants daily undergo start-up, shutdown and partial-load operations to maintain load invariance of electric power supply. The off-design conditions in this process may give rise to unexpected and unsteady flow behavior in the gas turbine, resulting in fatigue failure. JERA Co. Inc. currently operates gas turbines at many advanced combined cycle (ACC) power plants using a start-up and a partial-load operations. During the start-up procedure, some machine vibrations were reported at the ACC power plant when acoustic emission (AE) measurements were applied to the gas turbine compressor. A small crack in the first-stage stator blade was found at another unit during the overhaul. The partial-load operation may be in a similar condition to that in the startup procedure. This study elucidated the cause of machine vibration measured in an actual gas turbine compressor of an ACC power plant using full-annulus flow computation to indicate the possibility of blade failure. The full-annulus unsteady flows through the inlet guide vanes (IGV) and 1.5 rotor and stator stages in the compressor were simulated. The frequency of the simulated vibration was compared with that of the natural vibration of the blades, which was obtained by conducting eigenvalue analysis. The results for the partial-load condition captured an abnormal vibration resulting from a rotating stall. The rotating stall frequency closely matched the second-mode frequency of the first-stage stator blade’s natural vibration. This result suggests that the partial-load condition could be related to the cause of the blade failure due to the blade vibration.