Approximate Hessian for Accelerated Convergence of Aerodynamic Shape Optimization Problems

2017 ◽  
Author(s):  
Doug Shi-Dong ◽  
Sivakumaran Nadarajah
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape ◽  
Accelerated Convergence ◽  
Approximate Hessian

Application of Physics-Based Surrogate Models to Benchmark Aerodynamic Shape Optimization Problems

2015 ◽  
Author(s):  
Leifur T. Leifsson ◽  
Slawomir Koziel ◽  
Yonatan Afework Tesfahunegn ◽  
Serhat Hosder ◽  
Joe-Ray Gramanzini
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape

scholarly journals Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
R. Mukesh ◽  
K. Lingadurai ◽  
U. Selvakumar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Aerodynamic Shape Optimization ◽  
Shape Optimization Problem ◽  
Aerodynamic Shape

The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

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