Particle deposition tests were conducted in a turbine deposition facility with an internally staged single-tube combustor to investigate the individual effect of the gas temperature and angle of attack. Sand particles were seeded to the combustor and deposited on a turbine blade with film-cooling holes at temperatures representative of modern engines. Fuel-air ratios were varied from 0.022 to 0.037 to achieve a gas temperature between 1272 and 1668 K. Results show that capture efficiency increased with increasing gas temperature. A dramatic increase in capture efficiency was noted when gas temperature exceeded the threshold. The deposition formed mostly downstream of the film-cooling holes on the pressure surface, while it concentrated on the suction surface at the trailing edge. Deposition tests at angles of attack between 10° and 40° presented changes in both deposition mass and distribution. The capture efficiency increased with the increase in the angle of attack, and simultaneously the growth rate slowed down. On the blade pressure surface, sand deposition was distributed mainly downstream of the film-cooling holes near the trailing edge in the case of the small angle of attack, while it concentrated on the region around the film-cooling holes near the leading edge, resulting in the partial blockage of holes, in the case of the large angle of attack.

To understand the variations in pressure drop and heat transfer characteristics within the scavenge pipe of aero-engines, studying and attempting to discriminate the flow patterns of two-phase flow inside the scavenge pipe is of great significance. To achieve this, this paper establishes a flow pattern identification model. High-speed photography was utilized to capture images of four distinct flow patterns inside the scavenge pipe under typical operating conditions. Through image preprocessing, feature extraction, and Relief-F feature selection, the primary texture and shape features are obtained as inputs for the identification model. Four machine learning methods, namely unsupervised learning K-means, supervised learning backpropagation neural network (BP), radial basis function neural network (RBF), and support vector machine (SVM), are selected for flow pattern identification. For the optimization of hyperparameters in supervised learning methods, this paper utilizes the particle swarm optimization (PSO) algorithm. Consequently, PSO-BP, PSO-RBF, and PSO-SVM models are further established. After inputting the two types of features, texture and shape, into the mentioned models, a comparison of the classification accuracy and generalization ability of the four models is conducted. The results indicate that, for the flow pattern identification problem of oil–air two-phase flow inside the scavenge pipe studied in this paper, the most suitable identification model is the PSO-SVM model.

Particle deposition could decrease the aerodynamic performance and cooling efficiency of turbine vanes and blades. The particle motion in the flow and its temperature are two important factors affecting its deposition. The size of the particle influences both its motion and temperature. In this study, the motion of particles with the sizes from 1 to 20 μm in the first stage of a turbine are firstly numerically simulated with the steady method, then the particle deposition on the vanes and blades are numerically simulated with the unsteady method based on the critical viscosity model. It is discovered that the particle deposition on vanes mainly formed near the leading and trailing edge on the pressure surface, and the deposition area expands slowly to the whole pressure surface with the particle size increasing. For the particle deposition on blades, the deposition area moves from the entire pressure surface toward the tip with the particle size increasing due to the effect of rotation. For vanes, the particle capture efficiency increases with the particle size increasing since Stokes number and temperature of the particle both increase with its size. For blades, the particle capture efficiency increases firstly and then decreases with the particle size increasing.

Aircraft pitot tubes are sophisticated instruments designed to detect airflow pressure and relay this information to onboard computers and flight instruments, enabling the calculation of airspeed through the measurement of total-static pressure differences. The formation of ice on aircraft pitot tubes can compromise the acquisition of airspeed data, misguide pilots, and potentially cause catastrophic flight control failures. This article introduces a predictive methodology for identifying critical conditions that lead to icing on aircraft pitot tubes. Utilizing numerical simulation techniques, the methodology calculates the critical conditions for pitot tube icing across cruise flight regimes and atmospheric conditions, resulting in the generation of a critical condition envelope surface. By comparing these critical conditions against actual sensor data, a predictive danger zone can be established, offering an advanced warning system to ensure flight safety.

Poiseuille flows at two Reynolds numbers (Re) 2.5 × 10⁻² and 5.0 are simulated by two different smoothed particle hydrodynamics (SPH) schemes on regular and irregular initial particles' distributions. In the first scheme, the viscous stress is calculated directly by the basic SPH particle approximation, while in the second scheme, the viscous stress is calculated by the combination of SPH particle approximation and finite difference method (FDM). The main aims of this paper are (a) investigating the influences of two different schemes on simulations and reducing the numerical instability in simulating Poiseuille flows discovered by other researchers and (b) investigating whether the similar instability exists in other cases and comparing results with the two viscous stress approximations. For Re = 2.5 × 10⁻², the simulation with the first scheme becomes instable after the flow approaches to steady-state. However, this instability could be reduced by the second scheme. For Re = 5.0, no instability for two schemes is found.