Multi-Objective Optimization of a Counter Rotating Open Rotor using Evolutionary Algorithms
Paper in proceedings, 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.

Differential Evolution

Evolutionary Algorithm

Multi Objective Optimization

Genetic Algorithm

Counter Rotating Open Rotor



Gonzalo Montero Villar

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Daniel Lindblad

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

Niklas Andersson

Chalmers, Mechanics and Maritime Sciences, Fluid Dynamics

AIAA 2018-2929

2018 Multidisciplinary Analysis and Optimization Conference, AIAA AVIATION Forum
Atlanta, USA,

Areas of Advance


Subject Categories

Aerospace Engineering

Computational Mathematics

Fluid Mechanics and Acoustics


C3SE (Chalmers Centre for Computational Science and Engineering)



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7/2/2018 1