This paper presents the results of a rotorcraft preliminary design problem, solved as a multiobjective design optimization problem. A lift- and thrust-augmented coaxial compound configuration is used to demonstrate the approach. The basic optimization problem is converted into a sequence of approximate optimization problems, in which approximate Pareto frontiers are calculated based on response surfaces, obtained from radial basis function interpolation of all the designs analyzed at every step of the sequence. The Pareto frontiers are computed using a genetic algorithm. The designs are analyzed using a high-fidelity rotorcraft analysis that includes nonlinear finite element models of the rotor blade and a free vortex wake model of the rotor inflow. The results presented indicate that 1) the preliminary design problem can be effectively solved using formal numerical design optimization techniques, which therefore can complement classical design methodologies; 2) with appropriate physics-based constraints, the design optimization can be carried out by the computer completely unattended; 3) the optimization methodology is sufficiently robust to deal with multiple local optima and other numerical difficulties; and 4) the methodology is efficient enough to allow the use of high-fidelity analyses throughout the optimization, with the use of graphical processing unit computing (Compute Unified Device Architecture/Fortran) contributing to the computational efficiency.
Multiobjective Performance Optimization of a Coaxial Compound Rotorcraft Configuration
Journal of Aircraft ; 54 , 4 ; 1498-1507
2017-04-07
10 pages
Article (Journal)
Electronic Resource
English
Multiobjective Performance Optimization of a Coaxial Compound Rotorcraft Configuration
Online Contents | 2017
|MULTIDISCIPLINARY, MULTIOBJECTIVE TRIM OPTIMIZATION FOR A COAXIAL-PUSHER ROTORCRAFT CONFIGURATION
British Library Conference Proceedings | 2019
|Multidisciplinary Trim Analysis of a Coaxial-Pusher Rotorcraft Configuration
British Library Conference Proceedings | 2018
|