Abstract

Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs). Functional design innovation commonly occurs at the system-level, followed by a geometric-level realization of functional LPM components. However, comparing these two levels in terms of behavioral outcomes can be challenging and time-consuming, leading to delays in design translations between system and mechanical engineers. In this paper, we propose a simulation-free scheme that compares LPMs and spatially discretized DPMs based on their model specifications and behaviors of interest, regardless of modeling languages and numerical methods. We adopt a model order reduction (MOR) technique that a priori guarantees accuracy, stability, and convergence to improve the computational efficiency of large-scale models. Our approach is demonstrated through the model consistency analysis of several mechanical designs, showing its validity, efficiency, and generality. Our method provides a systematic way to compare system-level and geometric-level designs, improving reliability and facilitating design translation.

Graphical Abstract Figure
Graphical Abstract Figure
Close modal

References

1.
Systemes
,
D.
,
2018
,
Dymola User Manual
,
Dassault Systèmes
,
Vélizy-Villacoublay, France
.
2.
Chen
,
J.
,
Ilies
,
H. T.
, and
Ding
,
C.
,
2022
, “
Graph-Based Shape Analysis for Heterogeneous Geometric Datasets: Similarity, Retrieval and Substructure Matching
,”
Comput. Aided Des.
,
143
, p.
103125
.
3.
Almattar
,
T.
,
2019
,
Learn SOLIDWORKS 2020: A Hands-On Guide to Becoming an Accomplished SOLIDWORKS Associate and Professional
,
Packt Publishing Ltd
.
4.
Chen
,
J.
, and
Ilieş
,
H. T.
,
2020
, “
Maximal Disjoint Ball Decompositions for Shape Modeling and Analysis
,”
Comput. Aided Des.
,
126
, p.
102850
.
5.
Karnopp
,
D. C.
,
Margolis
,
D. L.
, and
Rosenberg
,
R. C.
,
2012
,
System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems
,
John Wiley & Sons
,
Hoboken, NJ
.
6.
Wang
,
R.
, and
Shapiro
,
V.
,
2019
, “
Topological Semantics for Lumped Parameter Systems Modeling
,”
Adv. Eng. Inform.
,
42
, p.
100958
.
7.
Mazumder
,
S.
,
2015
,
Numerical Methods for Partial Differential Equations: Finite Difference and Finite Volume Methods
,
Academic Press
,
Cambridge, MA
.
8.
Mattiussi
,
C.
,
2000
, “The Finite Volume, Finite Element, and Finite Difference Methods as Numerical Methods for Physical Field Problems,”
Advances in Imaging and Electron Physics
,
Elsevier
,
Amsterdam, Netherlands
, pp.
1
146
.
9.
Fritzson
,
P.
,
2011
,
Introduction to Modeling and Simulation of Technical and Physical Systems With Modelica
,
John Wiley & Sons
,
Hoboken, NJ
.
10.
Ulrich
,
K. T.
,
2003
,
Product Design and Development
,
Tata McGraw-Hill Education
,
New York
.
11.
Finger
,
S.
, and
Rinderle
,
J.
,
1990
,
A Transformational Approach to Mechanical Design Using a Bond Graph Grammar
,
Carnegie Mellon University, Engineering Design Research Center
,
Pittsburgh, PA
.
12.
Engelson
,
V.
,
Bunus
,
P.
,
Popescu
,
L.
, and
Fritzson
,
P.
,
2003
, “
Mechanical CAD With Multibody Dynamic Analysis Based on Modelica Simulation
,”
44th Scandinavian Conference on Simulation and Modeling
,
Mälardalen University in Västerås, Sweden
,
Sept. 18–19
.
13.
Prabhu
,
D.
, and
Taylor
,
D.
,
1989
, “
Synthesis of Systems From Specifications Containing Orientations and Positions Associated With Flow Variables
,”
1989 ASME Design Automation Conference
,
Montreal, Quebec, Canada
,
Sept. 17–21
, pp.
273
280
.
14.
Greer
,
J. L.
,
2002
, “Effort Flow Analysis: A Methodology for Directed Product Evolution Using Rigid Body and Compliant Mechanisms,” PhD thesis, The University of Texas at Austin.
15.
Kota
,
S.
, and
Ananthasuresh
,
G.
,
1995
, “
Designing Compliant Mechanisms
,”
Mech. Eng. CIME
,
117
(
11
), pp.
93
97
.
16.
Greer
,
J. L.
,
Jensen
,
D. D.
, and
Wood
,
K. L.
,
2004
, “
Effort Flow Analysis: A Methodology for Directed Product Evolution
,”
Des. Stud.
,
25
(
2
), pp.
193
214
.
17.
Mollemans
,
W.
,
Schutyser
,
F.
,
Van Cleynenbreugel
,
J.
, and
Suetens
,
P.
,
2004
, “
Fast Soft Tissue Deformation With Tetrahedral Mass Spring Model for Maxillofacial Surgery Planning Systems
,”
International Conference on Medical Image Computing and Computer-Assisted Intervention
,
Saint-Malo, France
,
Sept. 26–29
, Springer, pp.
371
379
.
18.
Baraff
,
D.
, and
Witkin
,
A.
,
1998
, “
Large Steps in Cloth Simulation
,”
25th Annual Conference on Computer Graphics and Interactive Techniques
,
Orlando, FL
,
July 19–24
, pp.
43
54
.
19.
Kähler
,
K.
,
Haber
,
J.
, and
Seidel
,
H.-P.
,
2001
, “Geometry-Based Muscle Modeling for Facial Animation,”
Graphics Interface
, Vol. 2001, pp.
37
46
.
20.
Gelder
,
A. V.
,
1998
, “
Approximate Simulation of Elastic Membranes by Triangulated Spring Meshes
,”
J. Graph. Tools
,
3
(
2
), pp.
21
41
.
21.
Baudet
,
V.
,
Beuve
,
M.
,
Jaillet
,
F.
,
Shariat
,
B.
, and
Zara
,
F.
,
2007
, “New Mass-Spring System Integrating Elasticity Parameters in 2d.”
22.
Natsupakpong
,
S.
, and
Çavuşoğlu
,
M. C.
,
2010
, “
Determination of Elasticity Parameters in Lumped Element (Mass–Spring) Models of Deformable Objects
,”
Graph. Models
,
72
(
6
), pp.
61
73
.
23.
Baur
,
U.
,
Benner
,
P.
, and
Feng
,
L.
,
2014
, “
Model Order Reduction for Linear and Nonlinear Systems: A System-Theoretic Perspective
,”
Arch. Comput. Methods Eng.
,
21
(
4
), pp.
331
358
.
24.
Fang
,
D.
,
Huang
,
Z.
,
Zhang
,
J.
,
Hu
,
Z.
, and
Tan
,
J.
,
2022
, “
Flow Pattern Investigation of Bionic Fish by Immersed Boundary–Lattice Boltzmann Method and Dynamic Mode Decomposition
,”
Ocean Eng.
,
248
, p.
110823
.
25.
Qu
,
Z.-Q.
,
2004
,
Model Order Reduction Techniques With Applications in Finite Element Analysis: With Applications in Finite Element Analysis
,
Springer Science & Business Media
,
Berlin, Germany
.
26.
Huang
,
B.
, and
Wang
,
J.
,
2022
, “
Applications of Physics-Informed Neural Networks in Power Systems—A Review
,”
IEEE Trans. Power Syst.
,
38
(
1
), pp.
572
588
.
27.
Fang
,
D.
,
Zhang
,
J.
, and
Huang
,
Z.
,
2023
, “
Modal Analysis on Mechanism of Bionic Fish Swimming by Dynamic Mode Decomposition
,”
Ocean Eng.
,
273
, p.
113897
.
28.
Chahlaoui
,
Y.
,
Gallivan
,
K. A.
,
Vandendorpe
,
A.
, and
Van Dooren
,
P.
,
2005
, “Model Reduction of Second-Order Systems,”
Dimension Reduction of Large-Scale Systems
,
Springer
,
Amsterdam, The Netherlands
, pp.
149
172
.
29.
Besselink
,
B.
,
Tabak
,
U.
,
Lutowska
,
A.
,
Van de Wouw
,
N.
,
Nijmeijer
,
H.
,
Rixen
,
D. J.
,
Hochstenbach
,
M.
, and
Schilders
,
W.
,
2013
, “
A Comparison of Model Reduction Techniques From Structural Dynamics, Numerical Mathematics and Systems and Control
,”
J. Sound Vib.
,
332
(
19
), pp.
4403
4422
.
30.
Lohmann
,
B.
, and
Salimbahrami
,
B.
,
2000
, “
Introduction to Krylov Subspace Methods in Model Order Reduction
,”
Colloquium of Automation
,
Leer, Germany
.
31.
Gugercin
,
S.
,
Antoulas
,
A. C.
, and
Beattie
,
C.
,
2008
, “
H2 Model Reduction for Large-Scale Linear Dynamical Systems
,”
SIAM J. Matrix Anal. Appl.
,
30
(
2
), pp.
609
638
.
32.
Beattie
,
C. A.
, and
Gugercin
,
S.
,
2009
, “
A Trust Region Method for Optimal H 2 Model Reduction
,”
48h IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conference
,
Shanghai, China
,
Dec. 15–18
, IEEE, pp.
5370
5375
.
33.
Panzer
,
H. K.
,
2014
, “Model Order Reduction by Krylov Subspace Methods With Global Error Bounds and Automatic Choice of Parameters,” PhD thesis, Technische Universität München.
34.
Wang
,
R.
, and
Behandish
,
M.
,
2021
, “
Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable Tradeoffs
,”
AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences
,
Stanford, CA
.
35.
Bathe
,
K.-J.
,
2006
,
Finite Element Procedures
,
Prentice Hall
,
Hoboken, NJ
.
36.
Atkinson
,
K.
,
Han
,
W.
, and
Stewart
,
D. E.
,
2011
,
Numerical Solution of Ordinary Differential Equations
,
John Wiley & Sons
,
Hoboken, NJ
.
37.
Callier
,
F. M.
, and
Desoer
,
C. A.
,
2012
,
Linear System Theory
,
Springer Science & Business Media
,
Berlin, Germany
.
38.
Antoulas
,
A. C.
,
Beattie
,
C. A.
, and
Gugercin
,
S.
,
2010
, “Interpolatory Model Reduction of Large-Scale Dynamical Systems,”
Efficient Modeling and Control of Large-Scale Systems
,
Springer
,
Amsterdam, The Netherlands
, pp.
3
58
.
39.
Peeters
,
J.
, and
Michiels
,
W.
,
2013
, “
Computing the H2 Norm of Large-Scale Time-Delay Systems
,”
IFAC Proc. Vol.
,
46
(
3
), pp.
114
119
.
40.
Castagnotto
,
A.
,
Varona
,
M. C.
,
Jeschek
,
L.
, and
Lohmann
,
B.
,
2017
, “
sss & sssmor: Analysis and Reduction of Large-Scale Dynamic Systems in Matlab
,”
At-Automatisierungstechnik
,
65
(
2
), pp.
134
150
.
41.
Trefethen
,
L. N.
, and
Bau
,
D.
,
2022
,
Numerical Linear Algebra
,
SIAM
,
Philadelphia, PA
.
42.
Wang
,
R.
,
2021
,
Consistency Analysis Between Lumped and Distributed Parameter Models
,
The University of Wisconsin-Madison
.
43.
Rewieński
,
M.
, and
White
,
J.
,
2006
, “
Model Order Reduction for Nonlinear Dynamical Systems Based on Trajectory Piecewise-Linear Approximations
,”
Linear Algebra Appl.
,
415
(
2–3
), pp.
426
454
.
44.
Peng
,
L.
, and
Mohseni
,
K.
,
2016
, “
Symplectic Model Reduction of Hamiltonian Systems
,”
SIAM J. Sci. Comput.
,
38
(
1
), pp.
A1
A27
.
45.
Chaturantabut
,
S.
, and
Sorensen
,
D. C.
,
2010
, “
Nonlinear Model Reduction Via Discrete Empirical Interpolation
,”
SIAM J. Sci. Comput.
,
32
(
5
), pp.
2737
2764
.
46.
Marzouk
,
Y. M.
, and
Najm
,
H. N.
,
2009
, “
Dimensionality Reduction and Polynomial Chaos Acceleration of Bayesian Inference in Inverse Problems
,”
J. Comput. Phys.
,
228
(
6
), pp.
1862
1902
.
You do not currently have access to this content.