A Design Process to Parameterize a Real-Time Simulation Model of a Commercial Vehicle
This paper introduces a method for building a real-time simulation model with adjustable user-selected parameters. The proposed design process model consists of eight steps with four decision points. Parameterization is a technique enabling real-time simulation with different combinations of parameters. Currently, there is no unique way to incorporate user input and switch between model combinations. The proposed method is presented in the form of a flowchart. Based on the data, a 3D design of the model was constructed. Two alternative approaches were introduced to construct a parameterized real-time simulation model with user inputs. The approach used was selected based on the number of parameterized specifications. The feasibility of each case was analyzed analytically and by simulation. Finally, a version of the model was selected based on the given initial requirements. To illustrate the developed approach, an excavator model was selected for parameterization. In the excavator model, two parts are considered to have adjustable parameters: the bucket and the hydraulics. Each part has three options that can be selected by users. The approach enables easy adaptability of user-generated parameter inputs, thus permitting evaluation of multiple scenarios, while simultaneously maintaining realistic representation.
Copyright © 2019 The Authors - Published by Praise Worthy Prize under the CC BY-NC-ND license.
A. A. Shabana, Dynamics of multibody systems (5th edition, Cambridge University Press, Vol. 171, 2020).
C. Pappalardo, D. Guida, On the computational methods for solving the differential-algebraic equations of motion of multibody systems, Machines, Vol. 6, n. 2, pp. 20, 2018.
C. Pappalardo, D. Guida, On the use of two-dimensional Euler parameters for the dynamic simulation of planar rigid multibody systems, Archive of Applied Mechanics, Vol. 87, n. 10, pp. 1647-1665, 2017.
J. J. Laflin, K. S. Anderson, M. Hans, Enhancing the performance of the DCA when forming and solving the equations of motion for multibody systems, Multibody Dynamics, (Springer, 2016, 19-31).
M. A. Naya, D. Dopico, J. A. Pérez, J. Cuadrado, Real-time multi-body formulation for virtual-reality-based design and evaluation of automobile controllers, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, Vol. 221, n. 2, pp. 261-276, 2007.
C. Schwarz, M. Bachinger, M. Stolz, D. Watzenig, Tool-driven design and automated parameterization for real-time generic drivetrain models, In: MATEC Web of Conference, EDP Science, Vol. 28, 2015, pp 03001.
J. Ros, A. Plaza, X. Iriarte, JM. Pintor, Symbolic multibody methods for real-time simulation of railway vehicles, Multibody System Dynamics, Vol. 42, n. 4, pp. 469-493, 2018.
A. Schmitt, H. Grossert, R. Seifried, Evaluation and experimental validation of efficient simulation models for optimization of an electrical formula car, ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers Digital Collection, 2018.
R. W. Du Val, A real-time multi-body dynamics architecture for rotorcraft simulation, The Challenge of Realistic Rotorcraft Simulation: Proceedings, London, UK, Vol. 1, 2001.
E. M. Dede, J. Lee, T. Nomura, Governing equations for electromechanical systems, Multiphysics Simulation, (Springer, London, 2014, 21-39)
M. J. Tavernini, B. A. Niemoeller, P. T. Krein, Real-time low-level simulation of hybrid vehicle systems for hardware-in-the-loop applications, In: 2009 IEEE Vehicle Power and Propulsion Conference, IEEE, 2009, pp. 890-895.
Tutunji, T., Salah, M., Jarrah, A., Ahmad, A., Alhamdan, R., Modeling and Identification of a Four-Bar Linkage Mechanism Driven by a Geared DC Motor, (2015) International Review of Mechanical Engineering (IREME), 9 (3), pp. 296-306.
A. G. Schmitt, Real-time simulation of flexible multibody systems in vehicle dynamics, Ph.D. dissertation, Technische Universität Hamburg, 2019.
C. Quesada, D. Gonzáles, I. Alfaro, E. Cueto, A. Huerta, F. Chinesta, Real-time simulation techniques for augmented learning in science and engineering, The Visual Computer, Vol. 32, n. 11, pp. 1465-1479, 2016.
J. O. Grady, System requirements analysis (2nd edition, Elsevier, 2014).
A. Bahill, Terry and Madni, M. Azad M, Discovering system requirements, In Tradeoff Decisions in System Design, (Springer, 2017, 373-457).
D. M. Buede, W. D. Miller, The engineering design of systems: models and methods (3rd edition, John Wiley & Sons, 2016).
O. Goury, C. Duriez, Fast, generic, and reliable control and simulation of soft robots using model order reduction, IEEE Transactions on Robotics, Vol. 34, n. 6, pp. 1565-1576, 2018.
J. Chenevier, D. Gonzalez, J. V. Aguado, F. Chinesta, Reduced-order modeling of soft robots, PloS One, Vol. 13, n. 2, pp. 1-15, 2018.
M. Kirchner, P. Eberhard, Simulation model of a gear synchronisation unit for application in a real-time HiL environment, Vehicle System Dynamics, Vol. 55, n. 5, pp. 668-680, 2017.
A. Schmit, R. Seifried, Comparison of various models and integration method for real-time simulation of complex vehicle models with structural flexibility, Proceedings of ISMA2016 International Conference on Noise and Vibration Engineering, 2016, pp. 3599-3606.
G. Rubenstein, D. M. Moy, A. Sridharan, I. Chopra, A python-based framework for real-time simulation using comprehensive analysis, In: 72nd Annual Forum of the American Helicopter Society, West Palm Beach, FL, 2016, pp. 1-15.
P. M. A. Slaats, Recursive formulations in multibody dynamics, Ph.D. dissertation, Technische Universiteit Eindhoven, Instituut Vervolgopleidingen, 1991.
A. Callejo, Y. Pan, J. L. Ricón, J. Kövecses, J. Garcia de Jalon, Comparison of semi-recursive and subsystem synthesis algorithms for the efficient simulation of multibody systems, Journal of Computational and Nonlinear Dynamics, Vol. 12, n. 1, 2017.
J. C. Samin, O. Brüls, J. F. Collard, L. Saas, P. Fisette, Multiphysics modeling and optimization of mechatronic multibody systems, Multibody System Dynamics, Vol. 18, n. 3, pp. 345-373, 2007.
J. Cuadrado, J. Cardenal, E. Bayo, Modeling and solution methods for efficient real-time simulation of multibody dynamics, Multibody System Dynamics, Vol. 1, n. 3, pp. 259-280, 1997.
J. Watton, Fluid power systems: modeling simulation, analog and microcomputer control (Prentice-Hall, Inc, 1989).
X. Yang, Y. Shen, Runge-kutta method for solving uncertain differential equations, Journal of Uncertainty Analysis and Applications, Vol. 3, n. 1, pp. 17, 2015.
Benbih, H., Gueraoui, K., Driouich, M., Taibi, M., Saidi Hassani Alaoui, M., Modeling and Numerical Simulation of the Motion of a Solid Particle in a Fluid Flow, (2017) International Review of Mechanical Engineering (IREME), 11 (9), pp. 677-682.
S. E. Shladover, Review of the state of development of advanced vehicle control system (AVCS), Vehicle System Dynamics, Vol. 24, n. 6-7, pp. 551-595, 1995.
T. Gordon, M. Howell, F. Brandao, Integrated control methodologies for road vehicles, Vehicle System Dynamics, Vol. 40, n. 1-3, pp. 157-190, 2003.
M. González, A. Luaces, D. Dopico, J. Cuadrado, A 3D physics-based hydraulic excavator simulator, In: ASME-AFM 2009 World Conference on Innovative Virtual Reality, American Society of Mechanical Engineers Digital Collection, 2009, pp. 75-80.
R. Ramya, K. Selvi, K. Murali, Real-time simulation and performance analysis of multimachine power systems using dSPACE simulator, Simulation, Vol. 92, n. 1, pp. 63-75, 2016.
J. H. Jung, Power hardware-in-loop simulation (PHILS) of photovoltaic power generation using real-time simulation techniques and power interfaces, Journal of Power Source, Vol. 285, pp. 137-145, 2015.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2020 Praise Worthy Prize