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Bond graph

A bond graph is a graphical representation of the energy flows though and between physical dynamical systems including those in the electrical, mechanical, hydraulic, thermal and chemical domains. It is used to model and analyse systems relevant to engineering and to systems biology.

Analogies
The importance of analogies between physical domains was noted by Lord Kelvin and James Clerk-Maxwell. The bond graph can be thought of as a systematic approach to analogies. As an introduction to the key features of bond graphs, the figure shows the bond graph of two analogous systems: one electrical and one mechanical. A brief description is given here and expanded in the following sections. • The bond graph C-component represents either the electrical capacitor C or the mechanical spring K. • The bond graph I-component represents either the electrical inductor L or the mechanical mass M. • The bond graph R-component represents either the electrical resistor R or the mechanical damper D. • The harpoon symbol is a bond transferring energy; the harpoon direction corresponds to positive energy flow and is a sign convention. The conjugate variables are displayed on each bond for the purposes of illustration. • The 1-junction represents the series connection of the electrical circuit where the same current i flows in each component and the connection of the three components in the mechanical system which share a common velocity. Thus the bond graph components share the same flow f, but the efforts are different, corresponding to the voltages and forces of the electrical and mechanical components respectively. Energy is conserved at the junction by requiring that the three efforts add to zero. The bond graph uses three classes of analogy: analogies between variables, analogies between components and analogies between component connections; these are discussed in the following sections. Analogies between variables The bond graph use energy flow, or power, as the basis for abstracting analogies between different physical domains. The conventional bond graph symbol for effort is e, and that for flow is f. \Phi_C, \Phi_I ~\text{and}~ \Phi_R may be linear or nonlinear functions relating the variables as: q = \Phi_C(e),~~ p = \Phi_I(f) ~~\text{and}~~ e = \Phi_R(f) in the linear case: q = C e, ~~ p = I f, ~~ e = R f where C,~I,~\text{and}~R are scalar constants representing generalised capacitance, inertia and resistance respectively. Because e and f are conjugate variables, they are carried on a single bond and therefore the three components C, I and R are connected to a single bond and thus have a single energy port though which energy flows. The components, and impinging bond are shown in the figure. By convention, bonds point into these three components. Notation The colon (:) notation is sometimes used to refer to the components; thus, for example I:M, C:K and R:D could be used in the bond graph of the mass-spring-damper system to emphasise the link between the bond graph components and their physical analogues. Analogies between connections Electrical circuit diagrams have two sorts of connection: parallel and series or common voltage and common current; they distribute, but do not store or dissipate energy. The bond graph analogy of the common voltage and common current connections are the 0-junction (common effort) and 1-junction (common flow) respectively; they both distribute, but do not store or dissipate energy. All bonds impinging on a 0-junctions have the same effort. As energy is distributed, not dissipated, it follows that the sum of energy inflows (indicated by bonds pointing in) must equal the sum of energy outflows (indicated by bonds pointing out). Hence, if the common effort is e, the power flow constraint implies that the sum of the m inflows f^{in}_i must equal the sum of the n outflows f^{out}_j: \sum_{i=1}^m f^{in}_i = \sum_{j=1}^n f^{out}_j Using the same argument, the efforts impinging on a 1-junction are constrained by: \sum_{i=1}^m e^{in}_i = \sum_{j=1}^n e^{out}_j Analogies between external connections Systems such as that the simple electrical and mechanical systems in the figure have no connection to the environment. Such connections may include external voltages and external forces (that is efforts) and external currents and external velocities (that is flows). Similarly, external measurements of efforts and flows are also important. For the purposes of building hierarchical system, it is convenient to define energy ports though which energy can flow between systems. These five possibilities correspond to five bond graph components: • \text{S}_e, an effort source analogous to applying external voltages and external forces • \text{S}_f, a flow source analogous to applying external currents and external velocities • \text{D}_e, an effort sensor (detector) analogous to measuring voltage and forces • \text{D}_f, a flow sensor (detector) analogous to measuring currents and velocities • \text{SS}, a source/sensor component which acts both as \text{S}_e-\text{D}_f and as \text{S}_f-\text{D}_e pairs as well as an energy port for external connections. For example, the mass-spring-damper system can be augmented with an force F applied to the mass and a measurement of the mass velocity v using a single SS components. The colon notation has been included; the SS:io refers to the fact that the force and velocity pair could be considered as system input and output. Analogies between energy transducers The conjugate effort and flow variables have different units in each energy domain thus models in one domain cannot be directly be connected by bonds to a different energy domain. However, because power has the same units (J/s or W) in each domain, the two-port power transducing components TF and GY can be used to provide such connections. As the two components transmit, but do not store or dissipate power, it follows that the power associated with the conjugate variables of the left-hand and right-hand bonds must be the same: e_2 f_2 = e_1 f_1 Each component has a modulus m associated with it. In the case of the TF component: e_2 = m e_1 ~~\text{and}~~ f_1 = m f_2 In the case of the GY component: e_2 = m f_1 ~~\text{and}~~ e_1 = m f_2 For example, a frictionless, massless piston of area A converts hydraulic power to mechanical power so that the hydraulic pressure P is related to mechanical force F by: F = AP and hydraulic flow V to piston velocity v by: V = Av Thus the bond graph analogy is the TF component with modulus m=A where e_1 = P,~~ f_1=V and e_2 = F,~~ f_2=v. For example, an ideal DC motor converts electrical power into rotational mechanical power so that the mechanical torque Tis related to the electrical current i by: T = k i and back EMF (voltage) E to angular velocity \Omega by E = k \Omega Thus the bond graph analogy is the GY component with modulus m=k where e_1=E, ~~ f_1 = i and e_2 = T, ~~ f_2=\Omega. In both cases, non-ideal transduction behaviour can be modelled by including C ,I and R bond graph components in the model. == Bond graphs in systems biology ==
Bond graphs in [[systems biology]]
Bond graphs have been used to model systems relevant to the life sciences, including physiology and biology. In particular, the use of bond graphs to model biophysical systems was introduced by Aharon Katchalsky, George Oster, and Alan Perelson in the early 1970s. More recently, these ideas were used in the context of Systems Biology to provide an energy-based approach to modelling the biochemical reaction systems of cellular biology and to modelling the entire physiome. The bond graph approach has a number of features which make it a good basis for building large computational models of the physiome. • It is energy based, which implies that: • the models are physically-plausible • detailed balance (Wegscheider's conditions) for reaction kinetics are automatically satisfied • energy flow, usage and dissipation can be directly considered • It is modular: bond graph components can themselves be bond graphs • Energy transduction between physical domains is simply represented Variables The bond graph variables for biochemical systems are: • Displacement: quantity of chemical species measured in moles, symbol x (mol) • Flow: rate of change of chemical species, symbol v (mol/s) • Effort: chemical potential, or Gibbs energy, per mole of a chemical species, symbol \mu (J/mol) Note that the product of effort and flow (μv) is, as always in the bond graph formulation, power (J/s). Components As detailed below, the main features of the components used to model biochemical systems are: the R and C components are nonlinear, there is no I component required and the R component is replaced by a two-port Re component. membrane transporters, cardiac action potential, and the mitochondrial electron transport chain. Chemomechanical transduction Consider a long rigid molecule such as actin where a sub unit of length \delta (m) is added at a rate of v (mol/sec). Then the tip velocity V is given by: V = \delta N_A v where N_Ais the Avogadro constant. Thus the modulus m = \delta N_A (m/mol) and \mu = m F = \delta N_A F where F is the corresponding force at the tip. These formulae have been used as the bond graph TF component can be potentially used with modular bond graph models of cellular systems. • Glucose transport • Cardiac Cellular Electrophysiological Modeling • Gene regulatory networks • Actin filament polymerization • Photosynthesis • Simplified E. coli • Mitochondrial Electron Transport Chain • Action potential == Causality ==
Causality
Causality is a word with many uses and connotations. In the context of bond graphs, however, it has a limited, precise but important meaning and allows the bond graph model of a system to be converted to various other forms including a (nonlinear) state-space representation. or the daes can be solved using an appropriate dae solver. Alternative causality. The sequential causal assignment procedure (SCAP) is designed to derive state-space equations, suitable for computation or control systems analysis. However, there are other forms of causal analysis designed to address other issues, including: • bicausality • derivative causality • alternative equation formulations, including those of Hamilton and Lagrange State-space equations A bond graph system representation contains the constitutive equations of each component, embedded in the structure of bond and junctions. The question of how to manipulate a set of equations into a form suitable for analogue computation was posed, and partially answered by Lord Kelvin. This approach underlies the conversion of a bond graph model to a state-space representation suitable for digital computation. The bond graph uses the notion of (bond graph) causality to provide a systematic and constructive way to investigate whether a state-space representation exists, and, if so, what is that representation; this causality approach is well suited to computational implementation and has an intuitive representation on the bond graph itself using the causal stroke notation. A causal bond graph can be put into state-space form if: This procedure is illustrated using the simple mass-spring-damper system bond graph which, although linear, exemplifies the key ideas. In particular, system inversion is performed by reversing the causality on the source-sensor component; thus the role of input and output is reversed. In this particular case, reversing the causality of the SS component implies that the causality of the I component must also be reversed as only one component can impose flow onto the 1-junction. Thus only one component, the C component, remains in integral causality. Thus the zero dynamics are first order which, in this simple case, corresponds to the fact that the transfer function of the original system has a first order numerator. Following the causal strokes, the remaining state q can be written as in terms of the input vof the inverse system \dot{q} = v p is no longer a state, but can be written as: p = mv the output F of the inverse system is: F = k p + d v + \dot{p} = k p + d v + m \dot{v} This approach relies on the system input and output existing on the same SS component - the input and output are said to be colocated. If this is not so, the concept of bicausality must be used. == Bicausality ==
Bicausality
Although (standard) causality provides a powerful tool to investigate the inverse of a dynamical system described by a bond graph, it is restricted to co-located source-sensor pairs where the input and output reside on a single SS component. To remove this restriction, the concept of bicausality was introduced and applied to various inversion problems. Bicausality has also been used in the context of fault detection, analysis of dynamical system structural properties, control system design and nonlinear analysis. Source sensor (SS) components The SS (source-sensor) component has two possible causal configurations corresponding to Se/Df (effort source, flow sensor) and De/Sf (effort sensor, flow source). As illustrated in the figure, the SS component has two bicausal configurations corresponding to Se/Sf (effort source, flow source) and De/Df (effort sensor, flow sensor). This is represented graphically using causal half-strokes where the half-stroke on the harpoon side of the bond corresponds to flow and the half-stroke on the other side of the bond corresponds to effort. The rules for the bicausality of junctions are the same as those for causality: only one bond imposes an effort onto a 0-junction and only one bond imposes a flow onto a 1-junction. In the context of inversion, the bonds connected to I, C and components cannot be bicausal. These ideas are illustrated using an example with non-collocated source and sensor. Example: inversion of non-collocated source-sensor system The example is extended by adding a zero junction and a further SS component. In the mechanical case, this corresponds to inserting a force sensor and velocity source between the spring and the ground; in the electrical case it corresponds to adding a current source and voltage sensor in parallel to the capacitor. The example is simplified by setting the flow of the added flow source to zero as indicated on the bond graph. As in the collocated example, the mechanical system is considered and system input is taken to be the force F acting on the mass M. However, the output is taken to be the force F_s of the spring which is not collocated with the applied force F. The corresponding state-space system is the same except that the output matrix C is given by: C = \begin{pmatrix}k & 0\end{pmatrix} reflecting the change of the sensor. The transfer function relating input F to output F_sbecomes: {k \over {\left (k + d s + m s^2\right )}} The denominator remains unchanged, but the numerator is different reflecting the change of sensor. The system is inverted by making the output F_san input and the input F an output. Thus the left hand SS becomes both an effort and flow sensor and the right hand SS becomes both and effort and flow source with the corresponding causalities. As the I, C and R components must retain conventional causality, the bicausality propagates as shown. Both the I and C components are in derivative causality and thus the zero dynamics are zero order which, in this simple case, corresponds to the fact that the transfer function of the original system has a zero order numerator. As in the collocated case, the transfer function of the inverse system is the reciprocal of the transfer function of the system; again, the bond graph causality method can also be used to examine the zero dynamics of nonlinear systems. == Deriving the bond graph of mechanical, electrical and electromechanical systems ==
Deriving the bond graph of mechanical, electrical and electromechanical systems
Methods for deriving the bond graph of systems in various physical domains are explained in detail in the textbooks. A detailed derivation of a laboratory electromechanical system is given in a tutorial paper. This section has methods and worked examples for some simple systems . Electromagnetic The steps for solving an Electromagnetic problem as a bond graph are as follows: • Place an 0-junction at each node • Insert Sources, R, I, C, TR, and GY bonds with 1 junctions • Ground (both sides if a transformer or gyrator is present) • Assign power flow direction • Simplify These steps are shown more clearly in the examples below. Linear mechanical The steps for solving a Linear Mechanical problem as a bond graph are as follows: • Place 1-junctions for each distinct velocity (usually at a mass) • Insert R and C bonds at their own 0-junctions between the 1 junctions where they act • Insert Sources and I bonds on the 1 junctions where they act • Assign power flow direction • Simplify These steps are shown more clearly in the examples below. Simplifying The simplifying step is the same regardless if the system was electromagnetic or linear mechanical. The steps are: • Remove Bond of zero power (due to ground or zero velocity) • Remove 0 and 1 junctions with less than three bonds • Simplify parallel power • Combine 0 junctions in series • Combine 1 junctions in series These steps are shown more clearly in the examples below. Parallel power Parallel power is when power runs in parallel in a bond graph. An example of parallel power is shown below. Parallel power can be simplified, by recalling the relationship between effort and flow for 0 and 1-junctions. To solve parallel power, one will first want to write down all of the equations for the junctions. For the example provided, the equations can be seen below. (Please make note of the number bond the effort/flow variable represents). \begin{matrix} f_1=f_2=f_3 & & e_2=e_4=e_7 \\ e_1=e_2+e_3 & & f_2=f_4+f_7 \\ & & \\ e_3=e_5=e_6 & & f_7=f_6=f_8 \\ f_3=f_5+f_6 & & e_7+e_6=e_8 \end{matrix} By manipulating these equations one can arrange them such that one can find an equivalent set of 0- and 1-junctions to describe the parallel power. For example, because e_3=e_6 and e_2=e_7 one can replace the variables in the equation e_1=e_2+e_3 resulting in e_1=e_6+e_7 and since e_6+e_7=e_8, we now know that e_1=e_8. This relationship of two effort variables equaling can be explained by an 0-junction. Manipulating other equations one can find that f_4=f_5 which describes the relationship of a 1-junction. Once the relationships have been determineds, one can redraw the parallel power section with the new junctions. The result for the example show is seen below. Examples Simple electrical system A simple electrical circuit consisting of a voltage source, resistor, and capacitor in series. The first step is to draw 0-junctions at all of the nodes: \begin{matrix} & 0 & & 0 & \\ & & & & \\ & & & & \\ & 0 & & 0 & \end{matrix} The next step is to add all of the elements acting at their own 1-junction: \begin{matrix} & & & & R & & & & \\ & & & & | & & & & \\ & & 0 & - & 1 & - & 0 & & \\ & & | & & & & | & & \\ S_e & - & 1 & & & & 1 & - & C \\ & & | & & & & | & & \\ & & \underline{0} & - & - & - & 0 & & \end{matrix} The next step is to pick a ground. The ground is simply an 0-junction that is going to be assumed to have no voltage. For this case, the ground will be chosen to be the lower left 0-junction, that is underlined above. The next step is to draw all of the arrows for the bond graph. The arrows on junctions should point towards ground (following a similar path to current). For resistance, inertance, and compliance elements, the arrows always point towards the elements. The result of drawing the arrows can be seen below, with the 0-junction marked with a star as the ground. Now that we have the Bond graph, we can start the process of simplifying it. The first step is to remove all the ground nodes. Both of the bottom 0-junctions can be removed, because they are both grounded. The result is shown below. Next, the junctions with less than three bonds can be removed. This is because flow and effort pass through these junctions without being modified, so they can be removed to allow us to draw less. The result can be seen below. The final step is to apply causality to the bond graph. Applying causality was explained above. The final bond graph is shown below. Advanced electrical system A more advanced electrical system with a current source, resistors, capacitors, and a transformer Following the steps with this circuit will result in the bond graph below, before it is simplified. The nodes marked with the star denote the ground. Simplifying the bond graph will result in the image below. Lastly, applying causality will result in the bond graph below. The bond with star denotes a causal conflict. Simple linear mechanical A simple linear mechanical system, consisting of a mass on a spring that is attached to a wall. The mass has some force being applied to it. An image of the system is shown below. For a mechanical system, the first step is to place a 1-junction at each distinct velocity, in this case there are two distinct velocities, the mass and the wall. It is usually helpful to label the 1-junctions for reference. The result is below. \begin{matrix} & & \\ & & \\ 1_\text{mass} & & \\ & & \\ & & \\ & & \\ 1_\text{wall} & & \end{matrix} The next step is to draw the R and C bonds at their own 0-junctions between the 1-junctions where they act. For this example there is only one of these bonds, the C bond for the spring. It acts between the 1-junction representing the mass and the 1-junction representing the wall. The result is below. \begin{matrix} & & \\ & & \\ 1_\text{mass} & & \\ 0 & - & C:\frac{1}{k} \\ 1_\text{wall} & & \end{matrix} Next one wants to add the sources and I bonds on the 1-junction where they act. There is one source, the source of effort (force) and one I bond, the mass of the mass both of which act on the 1-junction of the mass. The result is shown below. \begin{matrix} S_e:F(t) & & \\ 1_\text{mass} & - & I:m \\ 0 & - & C:\frac{1}{k} \\ 1_\text{wall} & & \end{matrix} Next power flow is to be assigned. Like the electrical examples, power should flow towards ground, in this case the 1-junction of the wall. Exceptions to this are R, C, or I bond, which always point towards the element. The resulting bond graph is below. Now that the bond graph has been generated, it can be simplified. Because the wall is grounded (has zero velocity), one can remove that junction. As such the 0-junction the C bond is on, can also be removed because it will then have less than three bonds. The simplified bond graph can be seen below. The last step is to apply causality, the final bond graph can be seen below. Advanced linear mechanical A more advanced linear mechanical system can be seen below. Just like the above example, the first step is to make 1-junctions at each of the distant velocities. In this example there are three distant velocity, Mass 1, Mass 2, and the wall. Then one connects all of the bonds and assign power flow. The bond can be seen below. Next one starts the process of simplifying the bond graph, by removing the 1-junction of the wall, and removing junctions with less than three bonds. The bond graph can be seen below. There is parallel power in the bond graph. Solving parallel power was explained above. The result of solving it can be seen below. Lastly, apply causality, the final bond graph can be seen below. State equations Once a bond graph is complete, it can be utilized to generate the state-space representation equations of the system. State-space representation is especially powerful as it allows complex multi-order differential system to be solved as a system of first-order equations instead. The general form of the state equation is \dot{\mathbf{x}} (t) = \mathbf{A} \mathbf{x}(t) + \mathbf{B}\mathbf{u}(t) where \mathbf{x} (t) is a column matrix of the state variables, or the unknowns of the system. \dot{\mathbf{x}}(t) is the time derivative of the state variables. \mathbf{u}(t) is a column matrix of the inputs of the system. And \mathbf{A} and \mathbf{B} are matrices of constants based on the system. The state variables of a system are q(t) and p(t) values for each C and I bond without a causal conflict. Each I bond gets a p(t) while each C bond gets a q(t). For example, if one has the following bond graph one would have the following \dot{\mathbf{x}}(t), \mathbf{x} (t), and \mathbf{u}(t) matrices: \dot{\mathbf{x}}(t) = \begin{bmatrix} \dot{p}_3(t) \\ \dot{q}_6(t) \end{bmatrix} \qquad \text{and} \qquad \mathbf{x}(t) = \begin{bmatrix} p_3(t) \\ q_6(t) \end{bmatrix} \qquad \text{and} \qquad \mathbf{u}(t) = \begin{bmatrix} e_1(t) \end{bmatrix} The matrices of \mathbf{A} and \mathbf{B} are solved by determining the relationship of the state variables and their respective elements, as was described in the tetrahedron of state. The first step to solve the state equations is to list all of the governing equations for the bond graph. The table below shows the relationship between bonds and their governing equations. }\; \overset{\textstyle}{\underset{\textstyle}}\ R \overset{\textstyle}{\underset{\textstyle}}\ I \overset{\textstyle}{\underset{\textstyle}}\ C \overset{\textstyle}{\underset{\textstyle}\ TR\ \ \overset{\textstyle}{\underset{\textstyle}{\!\!\!-\!\!\!-\!\!\!-\!\!\!\rightharpoonup}}|\ \\ ^{r:1} \end{matrix} e_2 = \frac{1}{r} e_1 |\ \overset{\textstyle}{\underset{\textstyle}{\!\!\!-\!\!\!-\!\!\!-\!\!\!\rightharpoonup }}\ TR\ \ | \ \overset{\textstyle}{\underset{\textstyle}{\!\!\!-\!\!\!-\!\!\!-\!\!\!\rightharpoonup}}\ \\ ^{r:1} \end{matrix} f_2 = r f_1 | \ \overset{\textstyle}{\underset{\textstyle}{\!\!\!-\!\!\!-\!\!\!-\!\!\!\rightharpoonup } }\ GY \overset{\textstyle}{\underset{\textstyle}{\!\!\!-\!\!\!-\!\!\!-\!\!\!\rightharpoonup}}|\ \\ ^{g:1} \end{matrix} e_2 = g f_1 \ \overset{\textstyle}{\underset{\textstyle}{\!\!\!-\!\!\!-\!\!\!-\!\!\!\rightharpoonup }}| \ GY \overset{\textstyle}{\underset{\textstyle} "♦" denotes preferred causality. For the example provided, the governing equations are the following. • e_1 = \text{input} • e_3 = e_1 - e_2 - e_4 • f_1 = f_2 = f_4 = f_3 • e_2 = R_2 f_2 • f_3 = \frac{1}{I_3} \int e_3 \, dt = \frac{1}{I_3} p_3 • f_5 = f_4 \cdot r • e_4 = e_5 \cdot r • e_5 = e_7 = e_6 • f_6 = f_5 - f_7 • e_6 = \frac{1}{C_6} \int f_6 \, dt = \frac {1}{C_6} q_6 • f_7 = \frac{1}{R_7} e_7 These equations can be manipulated to yield the state equations. For this example, one is trying to find equations that relate \dot{p}_3(t) and \dot{q}_6(t) in terms of p_3(t), q_6(t), and e_1(t). To start, one should recall from the tetrahedron of state that \dot{p}_3(t) = e_3(t)starting with equation 2, one can rearrange it so that e_3 = e_1 - e_2 - e_4. e_2 can be substituted for equation 4, while in equation 4, f_2 can be replaced by f_3due to equation 3, which can then be replaced by equation 5. e_4 can likewise be replaced using equation 7, in which e_5can be replaced with e_6 which can then be replaced with equation 10. Following these substituted yields the first state equation which is shown below. \dot{p}_3(t) = e_3(t) = e_1(t) - \frac {R_2}{I_3} p_3(t) - \frac{r}{C_6} q_6(t) The second state equation can likewise be solved, by recalling that \dot{q}_6(t) = f_6(t). The second state equation is shown below. \dot{q}_6(t) = f_6(t) = \frac{r}{I_3} p_3(t) - \frac{1}{R_7 \cdot C_6} q_6(t) Both equations can further be rearranged into matrix form. The result of which is below. \begin{bmatrix} \dot{p}_3(t) \\ \dot{q}_6(t) \end{bmatrix} = \begin{bmatrix} - \frac{R_2}{I_3} & - \frac{r}{C_6}\\ \frac{r}{I_3} & -\frac{1}{R_7 \cdot C_6} \end{bmatrix} \begin{bmatrix} p_3(t) \\ q_6(t) \end{bmatrix} + \begin{bmatrix} 1 \\ 0 \end{bmatrix} \begin{bmatrix} e_1(t) \end{bmatrix} At this point the equations can be treated as any other state-space representation problem. == International conferences on bond graph modeling (ECMS and ICBGM) ==
International conferences on bond graph modeling (ECMS and ICBGM)
A bibliography on bond graph modeling may be extracted from the following conferences : • ECMS-2013 27th European Conference on Modelling and Simulation, May 27–30, 2013, Ålesund, Norway • ECMS-2008 22nd European Conference on Modelling and Simulation, June 3–6, 2008 Nicosia, Cyprus • ICBGM-2007: 8th International Conference on Bond Graph Modeling And Simulation, January 15–17, 2007, San Diego, California, U.S.A. • ECMS-2006 20TH European Conference on Modelling and Simulation, May 28–31, 2006, Bonn, Germany • IMAACA-2005 International Mediterranean Modeling Multiconference • ICBGM-2005 International Conference on Bond Graph Modeling and Simulation, January 23–27, 2005, New Orleans, Louisiana, U.S.A. – Papers • ICBGM-2003 International Conference on Bond Graph Modeling and Simulation (ICBGM'2003) January 19–23, 2003, Orlando, Florida, USA – Papers • 14TH European Simulation symposium October 23–26, 2002 Dresden, Germany • ESS'2001 13th European Simulation symposium, Marseilles, France October 18–20, 2001 • ICBGM-2001 International Conference on Bond Graph Modeling and Simulation (ICBGM 2001), Phoenix, Arizona U.S.A. • European Simulation Multi-conference 23-26 May, 2000, Gent, Belgium • 11th European Simulation symposium, October 26–28, 1999 Castle, Friedrich-Alexander University, Erlangen-Nuremberg, Germany • ICBGM-1999 International Conference on Bond Graph Modeling and Simulation January 17–20, 1999 San Francisco, California • ESS-97 9TH European Simulation Symposium and Exhibition Simulation in Industry, Passau, Germany, October 19–22, 1997 • ICBGM-1997 3rd International Conference on Bond Graph Modeling And Simulation, January 12–15, 1997, Sheraton-Crescent Hotel, Phoenix, Arizona • 11th European Simulation Multiconference Istanbul, Turkey, June 1–4, 1997 • ESM-1996 10th annual European Simulation Multiconference Budapest, Hungary, June 2–6, 1996 • ICBGM-1995 Int. Conf. on Bond Graph Modeling and Simulation (ICBGM'95), January 15–18, 1995, Las Vegas, Nevada. ==See also==
Systems for bond graph
Many systems can be expressed in terms used in bond graph. These terms are expressed in the table below. Conventions for the table below: • P is the active power; • \hat{X} is a matrix object; • \vec{x} is a vector object; • x^{\dagger} is the Hermitian conjugate of ; it is the complex conjugate of the transpose of . If is a scalar, then the Hermitian conjugate is the same as the complex conjugate; • D_t^n is the Euler notation for differentiation, where: D_t^n f(t) = \begin{cases} \displaystyle\int_{-\infty}^t f(s)\, ds,& n=-1\\[2pt] f(t),& n=0\\[2pt] \dfrac{\partial^n f(t)}{\partial t^n}, & n>0 \end{cases} • \begin{cases} \langle x \rangle^{\alpha} := |x|^{\alpha}\sgn(x)\\ \langle{a} \rangle = k\langle b\rangle ^{\beta}\implies \langle b\rangle = \left(\frac{1}{k}\langle a\rangle \right)^{1/\beta} \end{cases} • Vergent-factor: \phi_L = \begin{cases} \textrm{Prismatic}:\ \dfrac{\textrm{length}}{\textrm{cross-sectional}\ \textrm{area}}\\ \textrm{Cylinder}:\ \dfrac{\ln\left(\frac{\mathrm{radius_{out}}}{\mathrm{radius_{in}}}\right)}{2\pi \cdot \textrm{length}}\\ \textrm{Sphere}:\ \dfrac{1}{4\pi \left(\mathrm{radius_{in}} \parallel \mathrm{-radius_{out}}\right)} \end{cases} Other systems: • Thermodynamic power system (flow is entropy-rate and effort is temperature) • Electrochemical power system (flow is chemical activity and effort is chemical potential) • Thermochemical power system (flow is mass-rate and effort is mass specific enthalpy) • Macroeconomics currency-rate system (displacement is commodity and effort is price per commodity) • Microeconomics currency-rate system (displacement is population and effort is GDP per capita) ==References==
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