Multi-Objective Optimization of a Counter Rotating Open Rotor using Evolutionary Algorithms

Paper i proceeding, 2018

In the present work, a recently developed platform for turbomachinery design and optimization based on Evolutionary Algorithms (EAs) is presented. Two types of evolutionary algorithms are implemented. The first one based on Genetic Algorithms (GAs) and the second one on Differential Evolution (DE), both able to handle single- and multi-objective as well as constrained and unconstrained optimization problems. Meta-modeling based on Radial Basis Functions (RBFs) is used in order to help accelerate the optimization when the objective function is too expensive to be evaluated inside the EA. Details on the implementation as well as validation results for a set of well-known benchmark cases are presented. The platform is also combined with 3D CFD simulations to optimize the aerodynamic performance of a Counter Rotating Open Rotor (CROR). Results from the CROR optimization are discussed and analyzed.