contributor author | Zhang, Baoqiang | |
contributor author | Guo, Qintao | |
contributor author | Wang, Yan | |
contributor author | Zhan, Ming | |
date accessioned | 2019-09-18T09:00:43Z | |
date available | 2019-09-18T09:00:43Z | |
date copyright | 3/14/2019 12:00:00 AM | |
date issued | 2019 | |
identifier issn | 1555-1415 | |
identifier other | cnd_014_05_051006.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4257854 | |
description abstract | Extensive research has been devoted to engineering analysis in the presence of only parameter uncertainty. However, in modeling process, model-form uncertainty arises inevitably due to the lack of information and knowledge, as well as assumptions and simplifications made in the models. It is undoubted that model-form uncertainty cannot be ignored. To better quantify model-form uncertainty in vibration systems with multiple degrees-of-freedom, in this paper, fractional derivatives as model-form hyperparameters are introduced. A new general model calibration approach is proposed to separate and reduce model-form and parameter uncertainty based on multiple fractional frequency response functions (FFRFs). The new calibration method is verified through a simulated system with two degrees-of-freedom. The studies demonstrate that the new model-form and parameter uncertainty quantification method is robust. | |
publisher | American Society of Mechanical Engineers (ASME) | |
title | Model-Form and Parameter Uncertainty Quantification in Structural Vibration Simulation Using Fractional Derivatives | |
type | Journal Paper | |
journal volume | 14 | |
journal issue | 5 | |
journal title | Journal of Computational and Nonlinear Dynamics | |
identifier doi | 10.1115/1.4042689 | |
journal fristpage | 51006 | |
journal lastpage | 051006-12 | |
tree | Journal of Computational and Nonlinear Dynamics:;2019:;volume( 014 ):;issue: 005 | |
contenttype | Fulltext | |