Programming under probabilistic constraints with a random technology matrix

Optimization ◽  
1974 ◽  
Vol 5 (2) ◽  
pp. 109-116 ◽  
Author(s):  
András Prékopa
2015 ◽  
Vol 155 (1-2) ◽  
pp. 617-620 ◽  
Author(s):  
Alexander Kogan ◽  
Miguel A. Lejeune ◽  
James Luedtke

2011 ◽  
Vol 62 ◽  
pp. 21-35 ◽  
Author(s):  
Anis Ben Abdessalem ◽  
A. El Hami

In metal forming processes, different parameters (Material constants, geometric dimensions, loads …) exhibits unavoidable scatter that lead the process unreliable and unstable. In this paper, we interest particularly in tube hydroforming process (THP). This process consists to apply an inner pressure combined to an axial displacement to manufacture the part. During the manufacturing phase, inappropriate choice of the loading paths can lead to failure. Deterministic approaches are unable to optimize the process with taking into account to the uncertainty. In this work, we introduce the Reliability-Based Design Optimization (RBDO) to optimize the process under probabilistic considerations to ensure a high reliability level and stability during the manufacturing phase and avoid the occurrence of such plastic instability. Taking account of the uncertainty offer to the process a high stability associated with a low probability of failure. The definition of the objective function and the probabilistic constraints takes advantages from the Forming Limit Diagram (FLD) and the Forming Limit Stress Diagram (FLSD) used as a failure criterion to detect the occurrence of wrinkling, severe thinning, and necking. A THP is then introduced as an example to illustrate the proposed approach. The results show the robustness and efficiency of RBDO to improve thickness distribution and minimize the risk of potential failure modes.


Author(s):  
Wenqing Zheng ◽  
Hezhen Yang

Reliability based design optimization (RBDO) of a steel catenary riser (SCR) using metamodel is investigated. The purpose of the optimization is to find the minimum-cost design subjecting to probabilistic constraints. To reduce the computational cost of the traditional double-loop RBDO, a single-loop RBDO approach is employed. The performance function is approximated by using metamodel to avoid time consuming finite element analysis during the dynamic optimization. The metamodel is constructed though design of experiments (DOE) sampling. In addition, the reliability assessment is carried out by Monte Carlo simulations. The result shows that the RBDO of SCR is a more rational optimization approach compared with traditional deterministic optimization, and using metamodel technique during the dynamic optimization process can significantly decrease the computational expense without sacrificing accuracy.


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