Multibody system dynamics interface modelling for stable multirate co-simulation of multiphysics systems

Peiret, Albert; González, Francisco Javier Miranda; Kövecses, József; Teichmann, Marek

doi:10.1016/j.mechmachtheory.2018.04.016

Cited by 35 publications

(26 citation statements)

References 25 publications

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“…Since the updated values of inputs u 1 and u 2 are not available during this time, data extrapolation may be needed. For the purposes of this study, we implement a polynomial extrapolation and a reduced interface model [24]. The former method uses Lagrange polynomials, that is, it is oblivious to the actual system dynamics, whereas the latter makes a physics-based assumption on the mechanical system dynamics for the update of subsystem inputs, as will be explained next.…”

Section: Co-simulation Setupmentioning

confidence: 99%

“…This can be done by means of polynomial extrapolation. It is also possible to build an interface model (IM), to provide a prediction of the evolution of the dynamics of M between communication points [24]. Such an interface model expresses the dynamics of the multibody system M in terms of the variables that describe its interface to S. Let us assume that the dynamics at the interface can be described with a set of p generalized velocities w i such that…”

Section: Co-simulation Via Interface Modelsmentioning

confidence: 99%

“…6. Such a system was used in [24] to assess the behavior of interface models in the co-simulation of multiply actuated systems. The set of variables used to describe the system motion remains unchanged.…”

Section: Double-actuator Modelmentioning

confidence: 99%

“…Co-simulation has been applied successfully to a wide range of applications that involve mechanical systems in multiphysics contexts, such as automotive [12], railway [21,22], biomechanics [23], and heavy machinery [24] simulation. Hydraulically actuated mechanisms are a particularly interesting case of these, as they are present in a large number of industry and research applications.…”

Section: Introductionmentioning

confidence: 99%

“…Several monolithic approaches to solve together the dynamics of mechanical and hydraulic components can be found in the literature [25][26][27]. Hydraulics and mechanics have also been coupled by means of co-simulation schemes [24,25] and publications exist that deal with particular applications of this approach [28,29]. However, the systematic study of the coupling method and the effect of selecting a given co-simulation scheme and set of coupling parameters on the efficiency and accuracy of the numerical integration has received comparatively less attention [30].…”

Section: Introductionmentioning

confidence: 99%

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On the cosimulation of multibody systems and hydraulic dynamics

et al. 2020

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The simulation of mechanical devices using multibody system dynamics (MBS) algorithms frequently requires the consideration of their interaction with components of a different physical nature, such as electronics, hydraulics, or thermodynamics. An increasingly popular way to perform this task is through co-simulation, that is, assigning a tailored formulation and solver to each subsystem in the application under study and then coupling their integration processes via the discrete-time exchange of coupling variables during runtime. Co-simulation makes it possible to deal with complex engineering applications in a modular and effective way. On the other hand, subsystem coupling can be carried out in a wide variety of ways, which brings about the need to select appropriate coupling schemes and simulation options to ensure that the numerical integration remains stable and accurate. In this work, the co-simulation of hydraulically actuated mechanical systems via noniterative, Jacobi-scheme co-simulation is addressed. The effect of selecting different cosimulation configuration options and parameters on the accuracy and stability of the numerical integration was assessed by means of representative numerical examples.

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Section: Co-simulation Setupmentioning

confidence: 99%

Section: Co-simulation Via Interface Modelsmentioning

confidence: 99%

Section: Double-actuator Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the cosimulation of multibody systems and hydraulic dynamics

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An efficient high‐precision recursive dynamic algorithm for closed‐loop multibody systems

2019

Numerical Meth Engineering

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As most closed-loop multibody systems do not have independent generalized coordinates, their dynamic equations are differential/algebraic equations (DAEs). In order to accurately solve DAEs, a usual method is using generalized α-class numerical methods to convert DAEs into difference equations by differential discretization and solve them by the Newton iteration method. However, the complexity of this method is O(n 2 ) or more in each iteration, since it requires calculating the complex Jacobian matrix. Therefore, how to improve computational efficiency is an urgent problem. In this paper, we modify this method to make it more efficient. The first change is in the phase of building dynamic equations. We use the spatial vector note and the recursive method to establish dynamic equations (DAEs) of closed-loop multibody systems, which makes the Jacobian matrix have a special sparse structure. The second change is in the phase of solving difference equations. On the basis of the topology information of the system, we simplify this Jacobian matrix by proper matrix processing and solve the difference equations recursively. After these changes, the algorithm complexity can reach O(n) in each iteration. The algorithm proposed in this paper is not only accurate, which can control well the position/velocity constraint errors, but also efficient. It is suitable for chain systems, tree systems, and closed-loop systems. KEYWORDSclosed-loop multibody systems, constrained multibody systems, differential/algebraic equation (DAE), efficient multibody algorithm, generalized α method, recursive multibody algorithm Int J Numer Methods Eng. 2019;118:181-208.wileyonlinelibrary.com/journal/nme One approach to solving DAEs is converting DAEs into index 1 or index 2 form equations. These equations are similar to ODEs and can be solved by ODE integration schemes, such as the Runge-Kutta method, 2,3 the Adams method, or the backward difference formulas method. 4 This approach is simple and could be very efficient. For example, the articulated-body algorithm (ABA) proposed by Featherstone 5,6 and the spatial operator algebra (SOA) proposed by Rodriguez et al 7-10 can reach O(n) complexity. With the application of the computer parallel technology, Featherstone 11,12 presented the divide-and-conquer algorithm (DCA), which promotes efficiency to the O(log(n)) complexity. Some other efficient algorithms can be found in the literature. [13][14][15][16][17][18] By converting the DAEs into ODEs, the above algorithms may be efficient in dealing with the closed-loop system, but it will cause serious position and velocity constraint violation problems and inaccurate results. 19 Although there are some methods to correct the constraint violation problems, such as the Baumgarte method, 19,20 the Moore-Penrose generalized inverse method, 21 and the geometrical projection method, 22 these methods may have some defects. For example, the Baumgarte method can only deal with small constraint violation problems, 23 and the Moore-Penrose generalized inverse me...

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Improved explicit co-simulation methods incorporating relaxation techniques

Yuan

et al. 2019

Arch Appl Mech

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Multibody system dynamics interface modelling for stable multirate co-simulation of multiphysics systems

Cited by 35 publications

References 25 publications

On the cosimulation of multibody systems and hydraulic dynamics

On the cosimulation of multibody systems and hydraulic dynamics

An efficient high‐precision recursive dynamic algorithm for closed‐loop multibody systems

Improved explicit co-simulation methods incorporating relaxation techniques

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