Multi-parameter sensitivity analysis and application research in the robust optimization design for complex nonlinear system

2014 ◽  
Vol 28 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Tao Ma ◽  
Weigang Zhang ◽  
Yang Zhang ◽  
Ting Tang
2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis ◽  
Ricardo Soto ◽  
Broderick Crawford

Toward a multi-objective optimization robust problem, the variations in design variables (DVs) and design environment parameters (DEPs) include the small variations and the large variations. The former have small effect on the performance functions and/or the constraints, and the latter refer to the ones that have large effect on the performance functions and/or the constraints. The robustness of performance functions is discussed in this paper. A postoptimality sensitivity analysis technique for multi-objective robust optimization problems (MOROPs) is discussed, and two robustness indices (RIs) are introduced. The first one considers the robustness of the performance functions to small variations in the DVs and the DEPs. The second RI characterizes the robustness of the performance functions to large variations in the DEPs. It is based on the ability of a solution to maintain a good Pareto ranking for different DEPs due to large variations. The robustness of the solutions is treated as vectors in the robustness function space (RF-Space), which is defined by the two proposed RIs. As a result, the designer can compare the robustness of all Pareto optimal solutions and make a decision. Finally, two illustrative examples are given to highlight the contributions of this paper. The first example is about a numerical problem, whereas the second problem deals with the multi-objective robust optimization design of a floating wind turbine.


Author(s):  
Wei Shi ◽  
Pingting Chen ◽  
Xueying Li ◽  
Ren Jing ◽  
Hongde Jiang

The first-stage rotor squealer tip is a key area in gas turbine for both aerodynamic performance and blade cooling tasks, which should be carefully designed. However, harsh operating conditions near the rotor squealer tip can cause the geometry of the squealer tip to degrade, and manufacturing inaccuracies can also cause the squealer tip geometry to deviate from the ideal design. These geometry deviations would change the flow field near the blade tip, which will influence the thermal and aerodynamic performance. Thus, it is important to quantitatively investigate the effects of squealer tip geometry deviation on the aerothermal performance. In this paper, a typical transonic first-stage turbine is employed, and three important geometric features of squealer tip, the tip clearance height (H), the squealer depth (D), and the squealer edge chamfer (R), are selected. An uncertainty quantification process is performed to study the effect of deviation of H, D, and R on the aerothermal performance. Many cases with different geometry features are checked in the current study using 3D Reynolds-averaged Navier–Stokes simulations. A parameter sensitivity analysis using Sobol Indice method is carried out to identify the key parameters to aerothermal performance of the squealer tip. The uncertainty quantification results show that the existence of the tip chamfer reduces the size of separation bubble and the dwelling range of the scraping vortex, thus, the blockage effect of the leakage flow is weakened, which results in larger amount of leakage flow and more mixing loss of squealer tips with edge chamfer than those without edge chamfer. The results of the parameter sensitivity analysis show that the height of tip clearance is the main factor that affects the aerodynamic performance of the squealer tip. This work provides a certain guiding direction for the optimization design of the turbine groove tip.


2011 ◽  
Vol 47 (10) ◽  
pp. 1186-1190 ◽  
Author(s):  
Feng Li ◽  
Guangwei Meng ◽  
Lirong Sha ◽  
Liming Zhou

Author(s):  
H. Torab

Abstract Parameter sensitivity for large-scale systems that include several components which interface in series is presented. Large-scale systems can be divided into components or sub-systems to avoid excessive calculations in determining their optimum design. Model Coordination Method of Decomposition (MCMD) is one of the most commonly used methods to solve large-scale engineering optimization problems. In the Model Coordination Method of Decomposition, the vector of coordinating variables can be partitioned into two sub-vectors for systems with several components interacting in series. The first sub-vector consists of those variables that are common among all or most of the elements. The other sub-vector consists of those variables that are common between only two components that are in series. This study focuses on a parameter sensitivity analysis for this special case using MCMD.


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