This paper addresses the problem of mapping a vector of input variables (corresponding to discrete samples from a time-varying input) to a vector of output variables (discrete samples of the time-dependent response). This mapping is typically performed by a mechanistic model. However, when the mechanistic model is complex and dynamic, the computational effort to iteratively generate the response for design purposes can be burdensome. Metamodels (or, surrogate models) can be computationally efficient replacements, especially when the input variables have some amplitude and frequency bounds. Herein, a simple metamodel in the form of a transfer matrix is created from a matrix of a few training inputs and a corresponding matrix of matching responses provided by simulations of the dynamic mechanistic model. A least-squares paradigm reveals a simple way to link the input matrix to the columns of the response matrix. Application of singular value decomposition (SVD) introduces significant computational advantages since it provides matrices whose properties give, in an elegant fashion, the transfer matrix. The efficacy of the transfer matrix is shown through an investigation of a nonlinear, underdamped, double mass–spring–damper system. Arbitrary excitations and selected sinusoids are applied to check accuracy, speed and robustness of the methodology. The sources of errors are identified and ways to mitigate them are discussed. When compared to the ubiquitous Kriging approach, the transfer matrix method shows similar accuracy but much reduced computation time.
Skip Nav Destination
Article navigation
October 2017
Technical Briefs
The Transfer Matrix Metamodel for Dynamic Systems With Arbitrary Time-Variant Inputs
Gordon J. Savage,
Gordon J. Savage
Department of Systems Design Engineering,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: gjsavage@uwaterloo.ca
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: gjsavage@uwaterloo.ca
Search for other works by this author on:
Young Kap Son
Young Kap Son
Department of Mechanical and Automotive Engineering,
Andong National University,
1375 Gyeongdong-ro,
Andong-si, Gyeongsangbuk-do 36729, South Korea
e-mail: ykson@anu.ac.kr
Andong National University,
1375 Gyeongdong-ro,
Andong-si, Gyeongsangbuk-do 36729, South Korea
e-mail: ykson@anu.ac.kr
Search for other works by this author on:
Gordon J. Savage
Department of Systems Design Engineering,
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: gjsavage@uwaterloo.ca
University of Waterloo,
Waterloo, ON N2L 3G1, Canada
e-mail: gjsavage@uwaterloo.ca
Young Kap Son
Department of Mechanical and Automotive Engineering,
Andong National University,
1375 Gyeongdong-ro,
Andong-si, Gyeongsangbuk-do 36729, South Korea
e-mail: ykson@anu.ac.kr
Andong National University,
1375 Gyeongdong-ro,
Andong-si, Gyeongsangbuk-do 36729, South Korea
e-mail: ykson@anu.ac.kr
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 18, 2017; final manuscript received July 22, 2017; published online August 30, 2017. Assoc. Editor: Gary Wang.
J. Mech. Des. Oct 2017, 139(10): 104502 (6 pages)
Published Online: August 30, 2017
Article history
Received:
February 18, 2017
Revised:
July 22, 2017
Citation
Savage, G. J., and Son, Y. K. (August 30, 2017). "The Transfer Matrix Metamodel for Dynamic Systems With Arbitrary Time-Variant Inputs." ASME. J. Mech. Des. October 2017; 139(10): 104502. https://doi.org/10.1115/1.4037630
Download citation file:
Get Email Alerts
Cited By
DeepJEB: 3D Deep Learning-Based Synthetic Jet Engine Bracket Dataset
J. Mech. Des (April 2025)
Design and Justice: A Scoping Review in Engineering Design
J. Mech. Des (May 2025)
Related Articles
Sequential Design Process for Screening and Optimization of Robustness and Reliability Based on Finite Element Analysis and Meta-Modeling
J. Comput. Inf. Sci. Eng (August,2022)
Design of Dynamic Systems Using Surrogate Models of Derivative Functions
J. Mech. Des (October,2017)
Network Analysis of Design Automation Literature
J. Mech. Des (October,2018)
Boosting Engineering Optimization With a Novel Recursive Transfer Bifidelity Surrogate Modeling
J. Mech. Des (March,2025)
Related Proceedings Papers
Related Chapters
Getting Ready for Production
Total Quality Development: A Step by Step Guide to World Class Concurrent Engineering
Cellular Automata: In-Depth Overview
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
An Adaptive Fuzzy Control for a Multi-Degree-of-Freedom System
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17