Mountain bicycle frame testing as an example of practical implementation of hybrid simulation using RTFEM

Mucha, Waldemar; Kuś, Wacław

doi:10.1063/1.5019144

Cited by 5 publications

(12 citation statements)

References 5 publications

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“…2019, 9, x FOR PEER REVIEW 11 of 19 Figure 10. Finite element model of bicycle frame (reprinted from [23], with the permission of AIP Publishing). Table 3.…”

Section: Numerical Verification Of Implementing Ann In Hybrid Simulatmentioning

confidence: 99%

“…Table 3. Cross-section parameters of the bicycle frame (reprinted from [23], with the permission of AIP Publishing).…”

Section: Numerical Verification Of Implementing Ann In Hybrid Simulatmentioning

confidence: 99%

“…1 -3 287 33507 4-5 158 22190 6-8, 13 221 92112 9-11 231 104925 12 130 12287 14 210 75663 Figure 10. Finite element model of bicycle frame (reprinted from [23], with the permission of AIP Publishing). Table 3.…”

Section: Element Number Cross-section Area (Mm 2 ) Moment Of Inertia mentioning

confidence: 99%

“…Table 3. Cross-section parameters of the bicycle frame (reprinted from [23], with the permission of AIP Publishing). It was assumed that in hybrid simulation, the time courses of the values of F1 and F2 were described as follows:…”

Section: Element Number Cross-section Area (Mm 2 ) Moment Of Inertia mentioning

confidence: 99%

“…The author researched a method for model order reduction in hybrid simulation-mode superposition [22,23]. The method has proven the above assumptions, however, only for mechanical systems that meet certain requirements.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks in Hybrid Simulation

Mucha

2019

Applied Sciences

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Featured Application: Performing effective and efficient hybrid simulations of mechanical systems in real-time by implementing a model order reduction technique to the computational algorithm. The efficiency of the computations is understood as the ability to test systems of a higher number of degrees of freedom while maintaining high accuracy, without increasing the time step. Effective simulation is a simulation that allows acquiring correct results while maintaining the time regime.Abstract: Hybrid simulation is a technique for testing mechanical systems. It applies to structures with elements hard or impossible to model numerically. These elements are tested experimentally by straining them by means of actuators, while the rest of the system is simulated numerically using a finite element method (FEM). Data is interchanged between experiment and simulation. The simulation is performed in real-time in order to accurately recreate the dynamic behavior in the experiment. FEM is very computationally demanding, and for systems with a great number of degrees of freedom (DOFs), real-time simulation with small-time steps (ensuring high accuracy) may require powerful computing hardware or may even be impossible. The author proposed to swap the finite element (FE) model with an artificial neural network (ANN) to significantly lower the computational cost of the real-time algorithm. The presented examples proved that the computational cost could be reduced by at least one number of magnitude while maintaining high accuracy of the simulation; however, obtaining appropriate ANN was not trivial and might require many attempts. subsystem, which are measured in the form of a vector r P i+1 , where subsequent elements of the vector correspond to degrees of freedom (DOFs) of the discretized FE model [4,5]. Appl. Sci. 2019, 9, x FOR PEER REVIEW 2 of 19 are measured in the form of a vector r P i+1, where subsequent elements of the vector correspond to degrees of freedom (DOFs) of the discretized FE model [4,5].

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“…2019, 9, x FOR PEER REVIEW 11 of 19 Figure 10. Finite element model of bicycle frame (reprinted from [23], with the permission of AIP Publishing). Table 3.…”

Section: Numerical Verification Of Implementing Ann In Hybrid Simulatmentioning

confidence: 99%

“…Table 3. Cross-section parameters of the bicycle frame (reprinted from [23], with the permission of AIP Publishing).…”

Section: Numerical Verification Of Implementing Ann In Hybrid Simulatmentioning

confidence: 99%

Section: Element Number Cross-section Area (Mm 2 ) Moment Of Inertia mentioning

confidence: 99%

Section: Element Number Cross-section Area (Mm 2 ) Moment Of Inertia mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of Artificial Neural Networks in Hybrid Simulation

Mucha

2019

Applied Sciences

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Application of dynamic condensation for model order reduction in real-time hybrid simulations

Mucha

2023

Meccanica

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This paper presents an algorithm for model order reduction in real-time hybrid simulations. The bottleneck of hybrid simulations are usually the finite element computations that must be performed in real time. The common approach to deal with this inconvenience is to involve powerful computing hardware. In the following paper opposite approach is presented—a new algorithm is proposed for reducing the model order. This allows to perform hybrid simulations more efficiently while maintaining high accuracy. The algorithm is based on dynamic condensation where the degrees of freedom are divided into masters and slaves, and system matrices are transformed in a way that only masters are kept. The transformation process is complex however, it is performed offline, therefore a relatively high computational effort is made a priori to make real-time calculations more efficient. The dynamic condensation algorithm was adapted to the requirements of hybrid simulations. The proper selection of masters is crucial for accuracy therefore, a novel approach based on evolutionary optimization is implemented. Numerical and experimental examples are provided. The examples prove that by implementing the proposed algorithms the following effects are achieved: (a) the time step of hybrid simulation can be significantly decreased when using implicit integration (increasing the accuracy of the measured dynamic behavior), (b) explicit integration can be sometimes implemented where it was previously not possible in real time, (c) smaller hardware resources can be involved (all computations in real-time hybrid simulations during the experiments were performed on a small microcontroller).

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Multi-rate Real Time Hybrid Simulation operated on a flexible LabVIEW real-time platform

Waldbjoern

Maghareh

et al. 2021

Engineering Structures

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Mountain bicycle frame testing as an example of practical implementation of hybrid simulation using RTFEM

Cited by 5 publications

References 5 publications

Application of Artificial Neural Networks in Hybrid Simulation

Application of Artificial Neural Networks in Hybrid Simulation

Application of dynamic condensation for model order reduction in real-time hybrid simulations

Multi-rate Real Time Hybrid Simulation operated on a flexible LabVIEW real-time platform

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