Aeroelastic Design Optimization using a Multifidelity Quasi-Newton Method

2018 ◽  
Author(s):  
Dean Bryson ◽  
Markus Rumpfkeil
Keyword(s):  
Design Optimization ◽  
Newton Method ◽  
Aeroelastic Design ◽  
Quasi Newton

scholarly journals Aerostructural Design Optimization Using a Multifidelity Quasi-Newton Method

2019 ◽  
Vol 56 (5) ◽  
pp. 2019-2031 ◽  
Author(s):  
Dean E. Bryson ◽  
Markus P. Rumpfkeil
Keyword(s):  
Design Optimization ◽  
Newton Method ◽  
Quasi Newton

scholarly journals Multifidelity Quasi-Newton Method for Design Optimization

AIAA Journal ◽  
2018 ◽  
Vol 56 (10) ◽  
pp. 4074-4086 ◽  
Author(s):  
Dean E. Bryson ◽  
Markus P. Rumpfkeil
Keyword(s):  
Design Optimization ◽  
Newton Method ◽  
Quasi Newton

scholarly journals Robust Iterative Solvers for Gao Type Nonlinear Beam Models in Elasticity

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
B. Borsos ◽  
János Karátson
Keyword(s):  
Finite Element ◽  
Newton Method ◽  
Newton's Method ◽  
Numerical Experiments ◽  
Elliptic Problems ◽  
Iterative Solvers ◽  
Finite Element Solution ◽  
Element Solution ◽  
Nonlinear Beam ◽  
Quasi Newton

Abstract The goal of this paper is to present various types of iterative solvers: gradient iteration, Newton’s method and a quasi-Newton method, for the finite element solution of elliptic problems arising in Gao type beam models (a geometrical type of nonlinearity, with respect to the Euler–Bernoulli hypothesis). Robust behaviour, i.e., convergence independently of the mesh parameters, is proved for these methods, and they are also tested with numerical experiments.

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

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