Dynamic System Synthesis With a Bond Graph Approach: Part I—Synthesis of One-Port Impedances

1993 ◽  
Vol 115 (3) ◽  
pp. 357-363 ◽  
Author(s):  
R. C. Redfield ◽  
S. Krishnan
Keyword(s):  
Dynamic System ◽  
Frequency Domain ◽  
Conceptual Design ◽  
Transfer Functions ◽  
Multiple Bond ◽  
Bond Graph ◽  
Bond Graphs ◽  
Active Systems ◽  
Graph Structures ◽  
Synthesis Approach

This is the first part of a two-part paper developing a procedure for impedance synthesis and dynamic system conceptual design using a bond graph approach. The first part examines impedance synthesis with bond graphs and develops some useful properties of impedance related to bond graph structure. The second part of the paper uses the impedance synthesis approach as a tool in conceptual design for generating dynamic system solutions meeting frequency domain input-output specifications. In Part I, impedance and transfer functions are directly formulated from bond graphs. An invertible procedure is developed in order to allow for synthesis. Frequency domain properties of bond graphs are developed that will aid in impedance synthesis and the synthesis of bond graph structures from impedance specifications is formulated with a view toward conceptual design. The synthesis is not unique, a given impedance can be synthesized into multiple bond graph structures depending on its complexity. The work presented focuses on the synthesis of passive impedances but may be generalized for active systems.

scholarly journals Bond graph based frequency domain sensitivity analysis of multidisciplinary systems

2002 ◽  
Vol 216 (1) ◽  
pp. 85-99 ◽  
Author(s):  
W Borutzky ◽  
J Granda
Keyword(s):  
Frequency Domain ◽  
Transfer Functions ◽  
Linear Time ◽  
Graph Model ◽  
Bond Graph ◽  
Sensitivity Matrix ◽  
Symbolic Form ◽  
Multidisciplinary Systems ◽  
Set Up ◽  
The Time Domain

Multidisciplinary systems are described most suitably by bond graphs. In order to determine unnormalized frequency domain sensitivities in symbolic form, this paper proposes to construct in a systematic manner a bond graph from another bond graph, which is called the associated incremental bond graph in this paper. Contrary to other approaches reported in the literature the variables at the bonds of the incremental bond graph are not sensitivities but variations (incremental changes) in the power variables from their nominal values due to parameter changes. Thus their product is power. For linear elements their corresponding model in the incremental bond graph also has a linear characteristic. By deriving the system equations in symbolic state space form from the incremental bond graph in the same way as they are derived from the initial bond graph, the sensitivity matrix of the system can be set up in symbolic form. Its entries are transfer functions depending on the nominal parameter values and on the nominal states and the inputs of the original model. The sensitivities can be determined automatically by the bond graph preprocessor CAMP-G and the widely used program MATLAB together with the Symbolic Toolbox for symbolic mathematical calculation. No particular program is needed for the approach proposed. The initial bond graph model may be non-linear and may contain controlled sources and multiport elements. In that case the sensitivity model is linear time variant and must be solved in the time domain. The rationale and the generality of the proposed approach are presented. For illustration purposes a mechatronic example system, a load positioned by a constant-excitation d.c. motor, is presented and sensitivities are determined in symbolic form by means of CAMP-G/MATLAB.

Comparative Study of Bond Graph Models for Hydraulic Transmission Lines With Transient Flow Dynamics

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Limin Yang ◽  
Jørgen Hals ◽  
Torgeir Moan
Keyword(s):  
Flow Rate ◽  
Transmission Lines ◽  
Transient Flow ◽  
Transfer Functions ◽  
Separation Of Variables ◽  
Bond Graph ◽  
Input Output ◽  
Bond Graphs ◽  
Graph Representations ◽  
Linear Resistance

The partial differential equation describing one-dimensional flow in a hydraulic pipeline with linear resistance can be approximated and solved numerically using different modal approaches. Modal models can be obtained either by using rational transfer functions (RTF) in the Laplace domain solution or by using separation of variables (SOV) techniques. The pipeline models have four possible input–output configurations: pressure inputs at both ends, flow rate inputs at both ends, and the two cases of mixed inputs. In this paper, modal bond graph representations for pipeline sections are reviewed, and new bond graphs are proposed for combinations of solution method and input–output configurations not yet presented in the literature. This includes bond graph representations for the two mixed input cases developed using the SOV technique, and bond graphs for the other two cases, pressure inputs or flow rate inputs, constructed on the basis of RTF solutions. Through numerical simulations of hydraulic single lines, the obtained models are compared to alternative models already established in the literature. It is shown that the modal models developed by the RTF and SOV methods have the same accuracy when the same number of modes is used. For both of these approaches, correction methods to maintain a high accuracy when truncating high-order modes are described, and also adapted to the bond graph form. Finally, simulation results for various line configurations are illustrated.

Dynamical Models for Multidimensional Structures Using Bond Graphs

1980 ◽  
Vol 102 (3) ◽  
pp. 180-187 ◽  
Author(s):  
D. L. Margolis
Keyword(s):  
Finite Element ◽  
Dynamic System ◽  
Finite Element Methods ◽  
Bond Graph ◽  
Dynamical Models ◽  
Bond Graphs ◽  
Long Wavelength ◽  
Lawrence Livermore Laboratory

Bond graphs are used to construct finite mode, long wavelength models of multidimensional structures. These structures are, in some cases, either too large or constructed from so many physical pieces that complete modeling using finite element methods is prohibited. Bond graph development for this type of dynamic system is given. The technique is demonstrated for the Lawrence Livermore Laboratory Shiva-Nova laser spaceframe.

Error analyses of a Multi-Input-Multi-Output cantilever beam test

2021 ◽  
pp. 107754632110337
Author(s):  
Arup Maji ◽  
Fernando Moreu ◽  
James Woodall ◽  
Maimuna Hossain
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Cantilever Beam ◽  
Transfer Functions ◽  
Linear Superposition ◽  
Transfer Function Matrix ◽  
Error Analyses ◽  
Pseudo Inverse ◽  
Function Matrix

Multi-Input-Multi-Output vibration testing typically requires the determination of inputs to achieve desired response at multiple locations. First, the responses due to each input are quantified in terms of complex transfer functions in the frequency domain. In this study, two Inputs and five Responses were used leading to a 5 × 2 transfer function matrix. Inputs corresponding to the desired Responses are then computed by inversion of the rectangular matrix using Pseudo-Inverse techniques that involve least-squared solutions. It is important to understand and quantify the various sources of errors in this process toward improved implementation of Multi-Input-Multi-Output testing. In this article, tests on a cantilever beam with two actuators (input controlled smart shakers) were used as Inputs while acceleration Responses were measured at five locations including the two input locations. Variation among tests was quantified including its impact on transfer functions across the relevant frequency domain. Accuracy of linear superposition of the influence of two actuators was quantified to investigate the influence of relative phase information. Finally, the accuracy of the Multi-Input-Multi-Output inversion process was investigated while varying the number of Responses from 2 (square transfer function matrix) to 5 (full-rectangular transfer function matrix). Results were examined in the context of the resonances and anti-resonances of the system as well as the ability of the actuators to provide actuation energy across the domain. Improved understanding of the sources of uncertainty from this study can be used for more complex Multi-Input-Multi-Output experiments.

Bond Graph Sign Conventions

1975 ◽  
Vol 97 (2) ◽  
pp. 184-188 ◽  
Author(s):  
A. S. Perelson
Keyword(s):  
Graph Model ◽  
Bond Graph ◽  
Equivalence Classes ◽  
Parameter System ◽  
Bond Graphs ◽  
Lumped Parameter ◽  
Oriented Object

The lack of arbitrariness in the choice of bond graph sign conventions is established. It is shown that an unoriented bond graph may have no unique meaning and that with certain choices of orientation a bond graph may not correspond to any lumped parameter system constructed from the same set of elements. Network interpretations of these two facts are given. Defining a bond graph as an oriented object leads to the consideration of equivalence classes of oriented bond graphs which represent the same system. It is also shown that only changes in the orientation of bonds connecting 0-junctions and 1-junctions can lead to changes in the observable properties of a bond graph model.

scholarly journals Analysing and simulating energy-based models in biology using BondGraphTools

2021 ◽  
Author(s):  
Peter Cudmore ◽  
Michael Pan ◽  
Peter J. Gawthrop ◽  
Edmund J. Crampin
Keyword(s):  
Systems Biology ◽  
Mathematical Models ◽  
Bond Graph ◽  
Experimental Measurements ◽  
Bond Graphs ◽  
Graph Models ◽  
Conservation Of Energy ◽  
Conservation Of Mass ◽  
Physical Systems ◽  
Complex Interactions

AbstractLike all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.

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