The role of epistemic uncertainty of contact models in the design and optimization of mechanical systems with aleatoric uncertainty

被引:0
|
作者
M. R. Brake
机构
[1] Sandia National Laboratories,
来源
Nonlinear Dynamics | 2014年 / 77卷
关键词
Impact mechanics; Design optimization; Epistemic uncertainty; Aleatoric uncertainty; Dynamics; Contact;
D O I
暂无
中图分类号
学科分类号
摘要
Epistemic uncertainty, the uncertainty in the physical model used to represent a phenomenon, has a significant effect on the predictions of simulations of mechanical systems, particularly in systems with impact events. Impact dynamics can have a significant effect on a system’s functionality, stability, wear, and failure. Because high-fidelity models of systems with impacts often are too computationally intensive to be useful as design tools, rigid body dynamics and reduced order model simulations are used often, with the impact events modeled by ad hoc methods such as a constant coefficient of restitution or penalty stiffness. The choice of impact model, though, can have significant ramifications on design predictions. The effects of both epistemic and aleatoric (parametric) uncertainty in the choice of contact model are investigated in this paper for a representative multiple-degree of freedom mechanical system. Six contact models are considered in the analysis: two different constant coefficient of restitution models, a piecewise-linear stiffness and damping (i.e., Kelvin–Voight) model, two similar elastic-plastic constitutive models, and one dissimilar elastic-plastic constitutive model. Results show that the optimal mechanism design for each contact model appears extremely different. Further, the effects due to epistemic uncertainty are differentiated clearly in the response from the effects due to aleatoric uncertainty. Lastly, when the mechanisms are optimized to be robust against aleatoric uncertainty, the resulting designs show some robustness against epistemic uncertainty.
引用
收藏
页码:899 / 922
页数:23
相关论文
共 50 条
  • [41] Design Floods Considering the Epistemic Uncertainty
    Drobot, Radu
    Draghia, Aurelian Florentin
    Ciuiu, Daniel
    Trandafir, Romica
    WATER, 2021, 13 (11)
  • [42] The Role of Epistemic Uncertainty in Risk Analysis
    Dubois, Didier
    SCALABLE UNCERTAINTY MANAGEMENT, SUM 2010, 2010, 6379 : 11 - 15
  • [43] Engineering Uncertainty: The role of uncertainty in the design of complex technological and business systems
    Johnson, Brian David
    FUTURES, 2013, 50 : 56 - 65
  • [44] Graph neural network interatomic potential ensembles with calibrated aleatoric and epistemic uncertainty on energy and forces
    Busk, Jonas
    Schmidt, Mikkel N.
    Winther, Ole
    Vegge, Tejs
    Jorgensen, Peter Bjorn
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2023, 25 (37) : 25828 - 25837
  • [45] Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: Semantics and automated reasoning with theorem proving
    Ye, Kangfeng
    Woodcock, Jim
    Foster, Simon
    THEORETICAL COMPUTER SCIENCE, 2024, 1021
  • [46] The role of uncertainty in the optimization of groundwater remediation systems
    Huang, C
    Mayer, AS
    COMPUTATIONAL METHODS IN WATER RESOURCES XI, VOL 1: COMPUTATIONAL METHODS IN SUBSURFACE FLOW AND TRANSPORT PROBLEMS, 1996, : 359 - 366
  • [47] Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network
    Rathnakumar, Rahul
    Pang, Yutian
    Liu, Yongming
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 240
  • [48] Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation
    Jones, Craig K.
    Wang, Guoqing
    Yedavalli, Vivek
    Sair, Haris
    JOURNAL OF MEDICAL IMAGING, 2022, 9 (03)
  • [49] Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
    Wimmer, Lisa
    Sale, Yusuf
    Hofman, Paul
    Bischl, Bernd
    Huellermeier, Eyke
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 2023, 216 : 2282 - 2292
  • [50] DYNAMIC DESIGN USING THE KALMAN FILTER FOR FLEXIBLE SYSTEMS WITH EPISTEMIC UNCERTAINTY
    Keshavarzi, Elham
    McIntire, Matthew
    Hoyle, Christopher
    INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2015, VOL 2B, 2016,