Research Papers: Design Automation

Heuristics-Enhanced Model Fusion Considering Incomplete Data Using Kriging Models

[+] Author and Article Information
Anton v. Beek

University of Michigan-Shanghai Jiao Tong
University Joint Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: mr.v.beek@sjtu.edu.cn

Mian Li

University of Michigan-Shanghai Jiao Tong
University Joint Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: mianli@sjtu.edu.cn

Chao Ren

Corporate Technology of Siemens Ltd.,
Shanghai 200240, China
e-mail: chao.ren@siemens.com

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 22, 2017; final manuscript received October 22, 2017; published online December 13, 2017. Assoc. Editor: Samy Missoum.

J. Mech. Des 140(2), 021403 (Dec 13, 2017) (11 pages) Paper No: MD-17-1357; doi: 10.1115/1.4038596 History: Received May 22, 2017; Revised October 22, 2017

Simulation models are widely used to describe processes that would otherwise be arduous to analyze. However, many of these models merely provide an estimated response of the real systems, as their input parameters are exposed to uncertainty, or partially excluded from the model due to the complexity, or lack of understanding of the problem's physics. Accordingly, the prediction accuracy can be improved by integrating physical observations into low fidelity models, a process known as model calibration or model fusion. Typical model fusion techniques are essentially concerned with how to allocate information-rich data points to improve the model accuracy. However, methods on subtracting more information from already available data points have been starving attention. Subsequently, in this paper we acknowledge the dependence between the prior estimation of input parameters and the actual input parameters. Accordingly, the proposed framework subtracts the information contained in this relation to update the estimated input parameters and utilizes it in a model updating scheme to accurately approximate the real system outputs that are affected by all real input parameters (RIPs) of the problem. The proposed approach can effectively use limited experimental samples while maintaining prediction accuracy. It basically tweaks model parameters to update the computer simulation model so that it can match a specific set of experimental results. The significance and applicability of the proposed method is illustrated through comparison with a conventional model calibration scheme using two engineering examples.

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Grahic Jump Location
Fig. 1

Flowchart of the proposed HEMF method

Grahic Jump Location
Fig. 2

Relation between ato and ero

Grahic Jump Location
Fig. 4

Results of the HEMF method on a friction ring assembly compared to a conventional updating method: (a) discrepancy between computer simulation, and experimental response friction ring model; (b) verification of the HEMF model for friction ring assembly; (c) response distributions for slacked mean, and tightened standard deviation for friction ring assembly; (d) individual parameter distributions for slacked mean, and tightened standard deviation of the friction ring assembly in millimeter; (e) response distributions for tightened mean and slacked standard deviation of the friction ring assembly; and (f) individual parameter distributions for tightened mean and slacked standard deviation of the friction ring assembly in millimeter

Grahic Jump Location
Fig. 5

The electrical amplifier circuit and its components

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Fig. 3

Configuration of friction ring assembly

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Fig. 6

Discrepancy in computer simulation and experimental responses of the electrical amplifier

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Fig. 7

Results of the HEMF method on an electrical amplifier assembly compared to the conventional method: (a) probability density functions for calibrated electrical amplifier response for the same design as its training data in Volts; (b) u-pooling results for calibrated electrical amplifier response for the same design as its training data; (c) probability density functions for calibrated electrical amplifier response for tightened standard deviation and perturbed mean in Volts; (d) u-pooling results for calibrated electrical amplifier response for tightened standard deviation and perturbed mean; (e) probability density functions for calibrated electrical amplifier response for slacked standard deviation and perturbed mean in Volts; and (f) u-pooling results for calibrated electrical amplifier response for slacked standard deviation and perturbed mean



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