Open AccessImplementation of genetic algorithms for supersonic airfoil optimization
Author(s) -
A Michelotti,
A Cavini,
R Giacopino,
F Misino,
L Piottoli
Publication year - 2022
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1226/1/012030
Subject(s) - airfoil , mach number , lift coefficient , angle of attack , supersonic speed , lift (data mining) , pressure coefficient , computational fluid dynamics , computer science , genetic algorithm , shock (circulatory) , range (aeronautics) , mathematical optimization , mathematics , algorithm , aerospace engineering , aerodynamics , engineering , physics , reynolds number , mechanics , medicine , turbulence , data mining
This paper describes the exploitation of Genetic Algorithms for the selection and optimization of supersonic airfoils. The main objective of the optimization is to ensure the possibility to reach the maximum efficiency at a given lift coefficient. The surrogate model presented in this paper implements the shock-expansion theory, and the optimization problem is constrained with respect to three design variables, since Bézier curves are used for the parameterization of the Diamond-Shaped and the 40-elements Double Circular Arc airfoil geometries. The airfoils were optimized for three different Mach numbers and two different lift coefficients. A thickness-constrained optimization has been run to evaluate the possibility to obtain a specific lift coefficient at a given Mach number and to understand how this parameter influences the optimized airfoil shape performance in terms of efficiency and the range of feasible angles-of-attack. An off-design evaluation is also presented, allowing for a comparison of the optimized geometries in terms of versatility. High fidelity models were validated with RAE 2822 airfoil using STAR CCM+, and they were compared to the surrogated model to ensure higher quality in the results. In conclusion, the Genetics Algorithms optimization coupled with the shock-expansion theory model results to be a fast and valuable solution to select and optimize airfoil solutions. The application of this approach has shown that a 20% reduction in airfoil thickness leads to a 25% efficiency improvement, while widening the range of viable angles of attack.