Topologically assisted optimization for rotor design

• Main Authors: Chen, X. (Author), Cornejo Maceda, G.Y (Author), Iollo, A. (Author), Li, P. (Author), Noack, B.R (Author), Wang, T. (Author), Yang, Y. (Author)
• Format: Article
• Language: English
• Published: American Institute of Physics Inc. 2023
• Subjects: Blade rotors; Evolutionary optimizations; Genetic algorithms; Global optimization; Local maximum; Objective functions; Optimisations; Rotor design; Shape optimization; Shape-optimization; Surrogate modeling; Topological analysis; Topology; Turbulence models; Two-blade
• Online Access: View Fulltext in Publisher

 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. © 2023 Author(s).