Adjoint parameter sensitivity analysis for the hydrodynamic lattice Boltzmann method with applications to design optimization

2009 ◽  
Vol 38 (4) ◽  
pp. 910-923 ◽  
Author(s):  
Georg Pingen ◽  
Anton Evgrafov ◽  
Kurt Maute
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Boltzmann Method

Parameter Sensitivity Analysis in Two-Level Design Optimization

1990 ◽  
Vol 112 (3) ◽  
pp. 354-361 ◽  
Author(s):  
W.-C. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Optimization Problem ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Local Effects ◽  
Level Design ◽  
Small Change ◽  
Optimum Solution

Parameter sensitivity analysis of a two-level design optimization problem can be decomposed into two levels. In the first-level, the sensitivities of the subproblems are calculated separately. In the second-level, the sensitivities obtained for the first-level subproblems are coordinated to obtain the overall sensitivity information. Using this approach, we cannot only obtain the overall effect that a small change in a parameter has on the optimum solution of a problem (or system) but also its local effects on the subproblems (or subsystems). A simple two-bar truss example demonstrates the approach.

Parameter Sensitivity Analysis in Two-Level Design Optimization

1989 ◽  
Author(s):  
W.-C. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Optimization Problem ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Local Effects ◽  
Level Design ◽  
Small Change ◽  
Optimum Solution

Abstract Parameter sensitivity analysis of a two-level design optimization problem can be decomposed into two levels. In the first-level, the sensitivities of the subproblems are calculated separately. In the second-level, the sensitivities obtained for the first-level subproblems are co-ordinated to obtain the overall sensitivity information. Using this approach, we can not only obtain the overall effect that a small change in a parameter has on the optimum solution of a problem (or system) but also its local effects on the subproblems (or subsystems). A simple two-bar truss example demonstrates the approach.

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