Uncertainty Quantification for Multiscale Simulations1

2001 ◽  
Vol 124 (1) ◽  
pp. 29-41 ◽  
Author(s):  
B. DeVolder ◽  
J. Glimm ◽  
J. W. Grove ◽  
Y. Kang ◽  
Y. Lee ◽  
...  

A general discussion of the quantification of uncertainty in numerical simulations is presented. A principal conclusion is that the distribution of solution errors is the leading term in the assessment of the validity of a simulation and its associated uncertainty in the Bayesian framework. Key issues that arise in uncertainty quantification are discussed for two examples drawn from shock wave physics and modeling of petroleum reservoirs. Solution error models, confidence intervals and Gaussian error statistics based on simulation studies are presented.

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Sui Yaguang ◽  
Zhang Dezhi ◽  
Tang Shiying ◽  
Li Jie ◽  
Lin Qizhao

A method for cylindrical explosion-containment vessels was presented, which used symmetrical implosion loading cooperating with the vessels to control the out-explosion loading, increasing the anti-explosion ability of explosion-containment vessels. In this study, theoretical analysis was developed first and response of cylindrical vessels loaded with implosion and out-explosion was discussed. Approximate expressions for final circumferential strain were obtained. Comparison between the theoretical calculations and the numerical simulations showed that the proposed method could effectively reduce the plastic strain of cylindrical explosion-containment vessels. The theoretical analysis introduced in this study can provide reference for related research. In addition, problems such as spall and defense of shock wave need to be solved before the presented method could be carried out in practical application.


2007 ◽  
Vol 666 (2) ◽  
pp. 1277-1283 ◽  
Author(s):  
L. Heggland ◽  
B. De Pontieu ◽  
V. H. Hansteen

Author(s):  
Shantanu Shahane ◽  
Soham Mujumdar ◽  
Namjung Kim ◽  
Pikee Priya ◽  
Narayana Aluru ◽  
...  

Die casting is a type of metal casting in which liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters is difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification is proposed. This framework includes high-speed numerical simulations of solidification, micro-structure and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.


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