Advances in Thermal Barrier Coatings Modeling, Simulation, and Analysis: A Review

Fluid flow and heat transfer characteristics of three-dimensional slot film cooling in an annular combustor

Prediction of thermo-mechanical performance for effusion cooling by machine learning method

Conditional Generative Adversarial Networks for modelling fuel sprays

By a series of time-resolved experiments and large eddy simulations, the present work exhibits the transient behaviors and unsteady characteristics of film cooling with different trench configurations. The inevitable mainstream oscillation in turbine operation is considered, with a Strouhal number of 0.0128 and an amplitude of 0.50. The results show that: 1) The main instantaneous features are well captured by both experimental and numerical methods, and the transient behaviors of film cooling with trench design can be accurately predicted by the present large eddy simulation, even considering mainstream oscillation. 2) Without mainstream oscillation, film cooling unsteadiness is mainly caused by the temporal evolution of near-wall vortices, and the trench configurations exhibit a strong correlation on the unsteadiness level. The design with a higher trench height can evidently enhance the near-wall stability of film cooling while keeping a high time-averaged film effectiveness. 3) Film cooling unsteadiness is significantly aggravated by the mainstream oscillation. Under the present oscillation condition, the standard deviation of instantaneous film effectiveness is increased over 350.0 %, compared to the non-oscillation data. Meanwhile, the regions with high unsteadiness level are revealed with mainstream oscillation, which is mainly caused by the temporary coverage of cooling air in the target surface. 

Experimental and numerical investigations on transient film cooling performances with trenched holes considering mainstream oscillation

The film cooling of cylindrical holes embedded in transverse trenches under superposition has shown promise for protecting the critical components of a high-pressure turbine from thermal damage. To optimize the relevant parameters and provide a suitable film cooling strategy, it is important to predict the effectiveness of lateral-averaged adiabatic film cooling with the trench effect on the surface of a blade. However, high-fidelity semi-empirical correlations for film cooling under superposition conditions with a trench have rarely been examined. This study establishes a gated recurrent unit (GRU) neural network model to predict the effectiveness of lateral-averaged film cooling under multiple-row superposition conditions with a trench. In general, a gated recurrent unit (GRU) neural network model is built with a large sequence of one-dimensional (1D) parameters, including the depth and width of the trench, compound angle, location of the hole, and blowing ratio. The computational fluid dynamics (CFD) method is used to provide a training dataset for the model. After careful testing and validation, the results predicted by the GRU agreed well with the CFD results. Moreover, the performance and robustness of the GRU were better than those of other recurrent neural network model, such as the long short-term memory (LSTM) model. Integrated with the GRU model, the sparrow search algorithm was adopted to optimize the parameters of the trench. The film cooling effectiveness of the optimized case improved by 1.6% compared with the best case, 28.5% compared with the worst case in dataset and 23.5% compared with the no-trench case.

Predicting and Optimizing Multirow Film Cooling with Trenches Using Gated Recurrent Unit Neural Network

Uncertainty quantification of the superposition film cooling with trench using supervised machine learning

Transpiration cooling is a promising cooling measure for high-temperature components in gas turbines. Although the flow and heat transfer characteristics of transpiration cooling have been investigated in several experimental and numerical investigations, the design optimization of transpiration cooling is less studied until now. In this paper, a multi-objective optimization framework based on the ANN surrogate model and the NSGA-Ⅱ is built to optimize the transpiration cooling of C3X turbine vane. The transpiration cooled C3X vane consists of multiple porous zones whose porosities could be different and are treated as the design parameters. The optimization objectives are minimum area-averaged vane surface temperature and minimum aerodynamic loss. A total of 960 CFD simulations are performed to prepare the sample dataset. After the validation of the surrogate-based optimization framework, the Pareto front is obtained as an output. Three representative Pareto points are chosen from the Pareto front, considering the best cooling performance, the lowest aerodynamic loss, and a balance between them. The porosity distributions and coolant allocation, cooling performances, and aerodynamic losses for the three Pareto points are discussed, and the involved flow and heat transfer characteristics are analyzed. The results reveal that to reach a balance between the cooling efficiency and the aerodynamic loss for an optimal transpiration cooled C3X vane, coolant should be appropriately distributed to the leading edge, as well as zones immediately downstream of the leading edge. 

Multi-objective optimization of transpiration cooling for high pressure turbine vane

Two-Dimensional Prediction of The Superposition Film Cooling With Trench Based on Conditional Generative Adversarial Network

The advancement of aircraft engines relies heavily on film cooling technology. To enhance the film cooling efficiency in high-pressure turbines, many passive flow control methods have been explored. Downstream of the cooling hole, a semi-sphere vortex generator (SVG) decreases the lateral dispersion of the coolant and increases the efficiency of film cooling. To better understand the influence and uncertainty of SVG parameters such as the compound angle, size, and location, a supervised learning-based artificial neural network model is developed to identify the nonlinear mapping between the input parameters and the horizontal-averaged film cooling efficiency. Training data are generated by computational fluid dynamics. The model is quite accurate and stable after sufficient testing and validation. Through Monte Carlo simulations, the framework is used to analyze the thermal and flow characteristics of the film cooling efficiency. The radius of the SVG dominates the film cooling effectiveness at low blowing ratios, whereas at comparatively large blowing ratios, the angular placement of the SVG downstream of the cooling hole is the most important element. The angular position of the SVG has a much stronger impact than the distance at both low and high blowing ratios between the cooling hole and the SVG.

Fast prediction and uncertainty analysis of film cooling with a semi-sphere vortex generator using artificial neural network

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Papers

Predicting and Optimizing Multirow Film Cooling with Trenches Using Gated Recurrent Unit Neural Network

Uncertainty quantification of the superposition film cooling with trench using supervised machine learning

Multi-objective optimization of transpiration cooling for high pressure turbine vane

Two-Dimensional Prediction of The Superposition Film Cooling With Trench Based on Conditional Generative Adversarial Network

Fast prediction and uncertainty analysis of film cooling with a semi-sphere vortex generator using artificial neural network