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Tangential effusion cooling of a combustor liner has a large difference from traditional effusion cooling on a plate. In this paper, numerical simulation is carried out to study the flow field, heat transfer characteristics and the factors affecting the cooling effectiveness of tangential effusion cooling of a combustor liner. It is found that the cooling film formed by the tangential jet is distributed in a divergent "horsetail" shape and adheres tightly to the inner wall of the liner, which increases the cooling area and effectiveness. Three different tangential inlet cooling hole arrangements and their cooling efficiencies are studied, and several important parameters that affect the cooling effectiveness are summarized. Then, an improved cooling hole arrangement is proposed, and its cooling efficiency is studied and compared with those of the original three arrangements. The results show that the new arrangement significantly improves the comprehensive cooling efficiency and decreases the wall temperature, thus confirming the effectiveness of the improved strategy and providing a theoretical basis for the subsequent cooling design to improve the cooling efficiency for a combustor liner. [ABSTRACT FROM AUTHOR]

We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit. This topologically assisted optimization contains two steps. First, a global evolutionary optimization is performed for the shape parameters, and then a topological analysis reveals the local and global extrema of the objective function directly from the data. This non-dimensional objective function compares the achieved thrust with the required torque. Rotor blades have a decisive contribution to the performance of quadcopters. A two-blade rotor with pre-defined chord length distribution is chosen as the baseline model. The simulation is performed in a moving reference frame with a k − ω turbulence model for the hovering condition. The rotor shape is parameterized by the twist angle distribution. The optimization of this distribution employs a genetic algorithm. The local maxima are distilled from the data using a novel topological analysis inspired by discrete scalar-field topology. We identify one global maximum to be located in the interior of the data and five further local maxima related to errors from non-converged simulations. The interior location of the global optimum suggests that small improvements can be gained from further optimization. The local maxima have a small persistence, i.e., disappear under a small ε perturbation of the figure of merit values. In other words, the data may be approximated by a smooth mono-modal surrogate model. Thus, the topological data analysis provides valuable insight for optimization and surrogate modeling. [ABSTRACT FROM AUTHOR]