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contributor authorJiang, Zhen
contributor authorChen, Shishi
contributor authorApley, Daniel W.
contributor authorChen, Wei
date accessioned2017-05-09T01:31:05Z
date available2017-05-09T01:31:05Z
date issued2016
identifier issn1050-0472
identifier othermd_138_08_081403.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/161819
description abstractModel uncertainty is a significant source of epistemic uncertainty that affects the prediction of a multidisciplinary system. In order to achieve a reliable design, it is critical to ensure that the disciplinary/subsystem simulation models are trustworthy, so that the aggregated uncertainty of system quantities of interest (QOIs) is acceptable. Reduction of model uncertainty can be achieved by gathering additional experiments and simulations data; however, resource allocation for multidisciplinary design optimization (MDO) and analysis remains a challenging task due to the complex structure of the system, which involves decision makings about where (sampling locations), what (disciplinary responses), and which type (simulations versus experiments) for allocating more resources. Instead of trying to concurrently make the above decisions, which would be generally intractable, we develop a novel approach in this paper to break the decision making into a sequential procedure. First, a multidisciplinary uncertainty analysis (MUA) is developed to identify the input settings with unacceptable amounts of uncertainty with respect to the system QOIs. Next, a multidisciplinary statistical sensitivity analysis (MSSA) is developed to investigate the relative contributions of (functional) disciplinary responses to the uncertainty of system QOIs. The input settings and critical responses to allocate resources are selected based on the results from MUA and MSSA, with the aid of a new correlation analysis derived from spatialrandomprocess (SRP) modeling concepts, ensuring the sparsity of the selected inputs. Finally, an enhanced preposterior analysis predicts the effectiveness of allocating experimental and/or computational resource to answer the question about which type of resource to allocate. The proposed method is applied to a benchmark electronic packaging problem to demonstrate how epistemic model uncertainty is gradually reduced via resource allocation for data gathering.
publisherThe American Society of Mechanical Engineers (ASME)
titleReduction of Epistemic Model Uncertainty in Simulation Based Multidisciplinary Design
typeJournal Paper
journal volume138
journal issue8
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4033918
journal fristpage81403
journal lastpage81403
identifier eissn1528-9001
treeJournal of Mechanical Design:;2016:;volume( 138 ):;issue: 008
contenttypeFulltext


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