A Recursive Formulation for Real-Time Dynamic Simulation

1988 ◽  
Author(s):  
D.-S. Bae ◽  
R. S. Hwang ◽  
E. J. Haug
Keyword(s):  
Real Time ◽  
Dynamic Simulation ◽  
Recursive Algorithm ◽  
Equations Of Motion ◽  
Design Tool ◽  
Real Time Simulation ◽  
Time Dynamic ◽  
Time Simulation ◽  
Design Variation ◽  
Multi Body

Abstract A new recursive algorithm for real-time, interactive dynamic simulation, animated graphics, and design variation analysis is presented for mechanical systems with closed loops. State vector kinematic relations that represent translational and rotational motion are defined, to simplify the formulation and to relieve computational burden. Recursive equations of motion are first derived for a single loop multi-body system. Faster than real-time performance is demonstrated for a closed loop robot, using an Alliant FX/8 multiprocessor. The algorithm is extended to multi-loop, multi-body systems for parallel processing real-time simulation in companion papers [1,2]. Performance of the algorithm on a shared memory multi-processor is compared with that achieved with other dynamic simulation algorithms. A vehicle example is used to demonstrate efficiency of the algorithm for real-time simulation and graphics rendering in a network environment, for use as an interactive design tool.

A Recursive Formulation for Real-Time Dynamic Simulation of Mechanical Systems

1991 ◽  
Vol 113 (2) ◽  
pp. 158-166 ◽  
Author(s):  
Dae-Sung Bae ◽  
Ruoh-Shih Hwang ◽  
Edward J. Haug
Keyword(s):  
Real Time ◽  
Dynamic Simulation ◽  
Recursive Algorithm ◽  
Equations Of Motion ◽  
Mechanical Systems ◽  
Time Dynamic ◽  
Time Simulation ◽  
Kinematic Loops ◽  
Multi Body ◽  
Recursive Equations

A new recursive algorithm for real-time dynamic simulation of mechanical systems with closed kinematic loops is presented. State vector kinematic relations that represent translational and rotational motion are defined to simplify the formulation and to relieve computational burden. Recursive equations of motion are first derived for a single loop multi-body system. Faster than real-time performance is demonstrated for a closed loop manipulator, using an Alliant FX/8 multiprocessor. The algorithm is extended to multi-loop, multi-body systems for parallel processing real-time simulation in companion papers [1, 2] where performance of the algorithm on a shared memory multi-processor is compared with that achieved with other dynamic simulation algorithms.

Parallel Processing for Real-Time Dynamic System Simulation

1988 ◽  
Author(s):  
R. S. Hwang ◽  
D.-S. Bae ◽  
E. J. Haug ◽  
J. G. Kuhl
Keyword(s):  
Parallel Processing ◽  
Real Time ◽  
Equations Of Motion ◽  
Companion Paper ◽  
Time Dynamic ◽  
Closed Loop Systems ◽  
Time Simulation ◽  
Efficiency And Effectiveness ◽  
Generalized Force ◽  
Recursive Dynamics

Abstract A parallel processing algorithm based on the recursive dynamics formulation presented in a companion paper [1] is developed for multiprocessor implementation. Lagrange multipliers associated with cut-joint constraints for closed loop systems are eliminated systematically, resulting to a minimal set of equations of motion. Concurrent generation of the system inertia matrix and the generalized force vector is exploited. A new computational structure for dynamic analysis is proposed for real-time parallel processing. Real-time simulation of a vehicle is performed to illustrate efficiency and effectiveness of the algorithm, even for interactive man-in-the-loop simulation.

Real-Time Dynamic Data-Driven Simulation of Continuum Systems

2004 ◽  
Author(s):  
J. G. Michopoulos ◽  
C. Farhat ◽  
E. N. Houstis
Keyword(s):  
Real Time ◽  
Behavior Modeling ◽  
Time Data ◽  
Physical Systems ◽  
Real Time Simulation ◽  
Time Dynamic ◽  
Time Simulation ◽  
Speed Accuracy ◽  
Continuum Systems ◽  
Behavioral Monitoring

Recent proliferation of sensor networks in various application areas has promoted real-time behavioral monitoring of various physical systems. The opportunity to use sensor generated data dynamically for improving speed, accuracy, and general performance of predictive behavior modeling simulation is of paramount importance. The present paper identifies enabling modeling methods and computational strategies that are critical for achieving real-time or near real-time simulation response of very large and complex systems. It also discusses our choices of these technologies in the context of sample multidisciplinary computational mechanics applications and describes two examples to demonstrate the feasibility of integrating real-time data with real-time simulation.

Modeling Issues for the Intelligent Simulation of Multibody Dynamics

1996 ◽  
Author(s):  
Javier Cuadrado ◽  
Jesús Cardenal ◽  
Eduardo Bayo
Keyword(s):  
Real Time ◽  
Multibody Dynamics ◽  
Multibody Systems ◽  
Equations Of Motion ◽  
Form Versus ◽  
Real Time Simulation ◽  
Simulation Tools ◽  
Modeling Process ◽  
Time Simulation ◽  
Intelligent Simulation

Abstract Current simulation tools for multibody dynamics are not problem dependent, they use the same modeling process to all cases regardless of their characteristics. In addition, real-time simulation of small multibody systems is achievable by existing simulation tools, however, real-time simulation of large and complex systems is not possible with existing methods. This is a challenge that needs to be addressed before further advances in mechanical simulation with hardware-in-the-loop and man-in-the-loop, as well as virtual prototyping are made possible. This paper addresses the issue of how the modeling process-dependent versus independent coordinates, and descriptor form versus state-space form of the equations of motion-affects the dynamic simulation of multibody systems and how it may be taken into account and added to the concept of intelligent simulation. With this new concept all the factors involved in the simulation process — modeling, equations, solution, etc. — are chosen and combined depending upon the characteristics of the system to be simulated. It is envisioned that this concept will lead to faster and more robust real-time simulators.

Real-time analysis of mobile machines using sparse matrix technique

2016 ◽  
Vol 230 (4) ◽  
pp. 615-625 ◽  
Author(s):  
Ezral Baharudin ◽  
Asko Rouvinen ◽  
Pasi Korkealaakso ◽  
Marko K Matikainen ◽  
Aki Mikkola
Keyword(s):  
Real Time ◽  
Sparse Matrix ◽  
Equations Of Motion ◽  
Numerical Procedure ◽  
Augmented Lagrangian Methods ◽  
Time Dynamic ◽  
Real Time Analysis ◽  
Simulation Techniques ◽  
Time Simulation ◽  
High Level

The use of modern multibody simulation techniques enables the description of complex products, such as mobile machinery, with a high level of detail while still solving the equations of motion in real time. Using the appropriate modelling and implementation techniques, the accuracy of real-time simulation can be improved considerably. Conventionally, in multibody system dynamics, equations of motion are implemented using the full matrices approach that does not consider the sparsity feature of matrices. With this implementation approach, numerical efficiency decreases when sparsity increases. In this study, a numerical procedure based on semi-recursive and augmented Lagrangian methods for real-time dynamic simulation is introduced. To enhance computing efficiency, an equation of motion is implemented by employing the sparse matrix technique.

scholarly journals The VOCO multi-body software in the context of real-time simulation

2013 ◽  
Vol 51 (4) ◽  
pp. 570-580 ◽  
Author(s):  
Hugues Chollet ◽  
Michel Sébès ◽  
Jean Louis Maupu ◽  
Jean Bernard Ayasse
Keyword(s):  
Real Time ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Multi Body

scholarly journals Real-time simulation of elasto-kinematic multi-body vehicle models

2016 ◽  
pp. 255-260
Author(s):  
Matthijs Klomp ◽  
Peter Sundström ◽  
Albin Johnsson
Keyword(s):  
Real Time ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Multi Body
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