A Response Surface Method for Design Space Exploration and Optimization of Analog Circuits

2011 ◽  
Author(s):  
Arnab Khawas ◽  
Amitava Banerjee ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Analog Circuits ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Surface Method

Multi-objective optimization and analysis for the design space exploration of analog circuits and solar cells

2017 ◽  
Vol 62 ◽  
pp. 373-383 ◽  
Author(s):  
Andrea Patanè ◽  
Andrea Santoro ◽  
Piero Conca ◽  
Giovanni Carapezza ◽  
Antonino La Magna ◽  
...  
Keyword(s):  
Solar Cells ◽  
Analog Circuits ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Multi Objective Optimization ◽  
Multi Objective

Adjoint-Response Surface Method in Aerodynamic Shape Optimization of Turbomachinery Blades

2016 ◽  
Author(s):  
Xiao Tang ◽  
Jiaqi Luo ◽  
Feng Liu
Keyword(s):  
Cost Function ◽  
Response Surface ◽  
Response Surface Method ◽  
Design Space ◽  
Three Dimensional ◽  
Gradient Methods ◽  
Control Parameters ◽  
Surface Method ◽  
Optimization Stage ◽  
Turbomachinery Blades

An adjoint-response surface method is developed to give global representation of cost function in a parametrized design space for turbomachinery blades. Radial basis function (RBF) based and quadratic polynomial (QP) based response surface models are constructed using both the values of cost function and its adjoint gradients with respect to geometry control parameters. The method is tested on a quasi-three dimensional NACA0012 blade row, then applied to the transonic Rotor 67. In preliminary design optimization stage, when the number of undetermined control parameters is large, the QP based model can provide a global image of the cost function in high dimensional design space with a small amount of sample points. In two-parameter fine optimization stage, high resolution can be achieved with the RBF based models. This gradient-enhanced response surface method is useful in guiding designers to discover the global optimum which may be missed by local gradient methods in a complicated design space. It may also be used as substitute of CFD flow solver in time consuming iterative design and optimization.

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