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The development of advanced military aero-engines with high thrust-to-weight ratios requires high-temperature-rise (HTR) technology for core component combustors. This poses a major challenge to multidisciplinary design and optimization of combustors. This paper analyzes the technical characteristics of the HTR combustor and summarizes and proposes three current major technical challenges. For each technical challenge, systematic analysis and comprehensive discussion are conducted from the aspects of technical strategies and research progress. The three technologies reviewed in this paper include (1) combustion configuration technology, (2) liner thermal protection technology involving advanced liner cooling and ceramic matrix composite (CMC) liners, and (3) outlet distribution control technology. We also present our insights regarding current solutions and future research trends related to the three major technical limitations of the HTR combustor. [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]