Parameter Identification for Multibody Dynamic Systems

1997 ◽  
Author(s):  
Radu Serban ◽  
Jeffrey S. Freeman ◽  
Dan Negrut
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Identification ◽  
Dynamic Systems ◽  
Time History ◽  
Optimization Procedure ◽  
Optimal Choice ◽  
Least Square ◽  
Unknown Parameters ◽  
Multibody Dynamic ◽  
Dynamic Sensitivity Analysis

Abstract This paper presents a parameter identification technique for multibody dynamic systems, based on a nonlinear least-square optimization procedure. The procedure identifies unknown parameters in the differential-algebraic multibody system model by matching the acceleration time history of a point of interest with given data. Derivative information for the optimization process is obtained through dynamic sensitivity analysis. Direct differentiation methods are used to perform the sensitivity analysis. Examples of the procedure are presented, applying the technique both to perfect data; i.e. data produced by the assumed model with the optimal choice of parameters, and to experimental data; i.e. data measured on the real system and thus subject to noise and modelling imperfections.

Direct Differentiation Methods for the Design Sensitivity of Multibody Dynamic Systems

1996 ◽  
Author(s):  
Radu Serban ◽  
Jeffrey S. Freeman
Keyword(s):  
Sensitivity Analysis ◽  
Dynamic Systems ◽  
Design Sensitivity ◽  
Differential Algebraic Equations ◽  
Mechanism Analysis ◽  
Algebraic Equations ◽  
First Order ◽  
Direct Differentiation ◽  
Multibody Dynamic ◽  
Standard Techniques

Abstract Methods for formulating the first-order design sensitivity of multibody systems by direct differentiation are presented. These types of systems, when formulated by Euler-Lagrange techniques, are representable using differential-algebraic equations (DAE). The sensitivity analysis methods presented also result in systems of DAE’s which can be solved using standard techniques. Problems with previous direct differentiation sensitivity analysis derivations are highlighted, since they do not result in valid systems of DAE’s. This is shown using the simple pendulum example, which can be analyzed in both ODE and DAE form. Finally, a slider-crank example is used to show application of the method to mechanism analysis.

Direct Sensitivity Analysis of Spatial Multibody Systems With Joint Friction Using Index-1 Formulation

2021 ◽  
Author(s):  
Adwait Verulkar ◽  
Corina Sandu ◽  
Daniel Dopico ◽  
Adrian Sandu
Keyword(s):  
Sensitivity Analysis ◽  
Dynamic Systems ◽  
Multibody Systems ◽  
Friction Model ◽  
Design Parameters ◽  
Adjoint Sensitivity ◽  
Joint Friction ◽  
Multibody Dynamic ◽  
Model Dynamics ◽  
Direct Sensitivity

Abstract Sensitivity analysis is one of the most prominent gradient based optimization techniques for mechanical systems. Model sensitivities are the derivatives of the generalized coordinates defining the motion of the system in time with respect to the system design parameters. These sensitivities can be calculated using finite differences, but the accuracy and computational inefficiency of this method limits its use. Hence, the methodologies of direct and adjoint sensitivity analysis have gained prominence. Recent research has presented computationally efficient methodologies for both direct and adjoint sensitivity analysis of complex multibody dynamic systems. The contribution of this article is in the development of the mathematical framework for conducting the direct sensitivity analysis of multibody dynamic systems with joint friction using the index-1 formulation. For modeling friction in multibody systems, the Brown and McPhee friction model has been used. This model incorporates the effects of both static and dynamic friction on the model dynamics. A case study has been conducted on a spatial slider-crank mechanism to illustrate the application of this methodology to real-world systems. Using computer models, with and without joint friction, effect of friction on the dynamics and model sensitivities has been demonstrated. The sensitivities of slider velocity have been computed with respect to the design parameters of crank length, rod length, and the parameters defining the friction model. Due to the highly non-linear nature of friction, the model dynamics are more sensitive during the transition phases, where the friction coefficient changes from static to dynamic and vice versa.

Dimensionality Reduction of High-Fidelity Machine Tool Models by Using Global Sensitivity Analysis

2021 ◽  
pp. 1-13
Author(s):  
Johannes Ellinger ◽  
Thomas Semm ◽  
Michael F. Zäh
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tool ◽  
Parameter Identification ◽  
Machine Tools ◽  
Global Sensitivity Analysis ◽  
Reference Model ◽  
Evaluation Criteria ◽  
Unknown Parameters ◽  
Global Sensitivity ◽  
Process Simulations

Abstract Models that are able to accurately predict the dynamic behavior of machine tools are crucial for a variety of applications ranging from machine tool design to process simulations. However, with increasing accuracy, the models tend to become increasingly complex, which can cause problems identifying the unknown parameters which the models are based on. In this paper, a method is presented that shows how parameter identification can be eased by systematically reducing the dimensionality of a given dynamic machine tool model. The approach presented is based on ranking the model's input parameters by means of a global sensitivity analysis. It is shown that the number of parameters, which need to be identified, can be drastically reduced with only limited impact on the model's fidelity. This is validated by means of model evaluation criteria and frequency response functions which show a mean conformity of 98.9 % with the full-scale reference model. The paper is concluded by a short demonstration on how to use the results from the global sensitivity analysis for parameter identification.

scholarly journals Sensitivity Analysis of a CPAM Inverse Algorithm for Composite Laminates Characterization

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Farshid Masoumi ◽  
Ahmad Ghasemi Ghalebahman
Keyword(s):  
Sensitivity Analysis ◽  
Composite Laminates ◽  
Numerical Solutions ◽  
Error Function ◽  
Simulated Data ◽  
Optimization Procedure ◽  
Unknown Parameters ◽  
Finite Element Package

Using experimental data and numerical simulations, a new combined technique is presented for characterization of thin and thick orthotropic composite laminates. Four or five elastic constants, as well as ply orientation angles, are considered as the unknown parameters. The material characterization is first examined for isotropic plates under different boundary conditions to evaluate the method’s accuracy. The proposed algorithm, so-called CPAM (Combined Programs of ABAQUS and MATLAB), utilizes an optimization procedure and makes simultaneous use of vibration test data together with their corresponding numerical solutions. The numerical solutions are based on a commercial finite element package for efficiently identifying the material properties. An inverse method based on particle swarm optimization algorithm is further provided using MATLAB software. The error function to be minimized is the sum of squared differences between experimental and simulated data of eigenfrequencies. To evaluate the robustness of the model’s results in the presence of uncertainty and unwanted noises, a sensitivity analysis that employs Gaussian disorder model is directly applied to the measured frequencies. The results with high accuracy confirm the validity and capability of the present method in simultaneous determination of mechanical constants and fiber orientation angles of composite laminates as compared to prior methods.

Design Sensitivity Analysis of Multibody Dynamic Systems for Parallel Processing

1989 ◽  
Author(s):  
T. Tak ◽  
S. S. Kim
Keyword(s):  
Sensitivity Analysis ◽  
Parallel Processing ◽  
Dynamic Systems ◽  
Large Scale ◽  
Design Sensitivity ◽  
Design Sensitivity Analysis ◽  
Parallel Computer ◽  
Sensitivity Equations ◽  
Multibody Dynamic ◽  
High Level

Abstract Design sensitivity analysis of large scale multibody systems is a computationally intensive process, which is well suited for implementation on a parallel computer. This paper presents a parallel processing oriented generalized design sensitivity analysis method for multibody dynamic systems. A direct differentiation method, which is more efficient than an adjoint variable method in a parallel processing environment due to the inherent parallelism, is applied to a recursive formulation for multibody dynamics to set up dynamic sensitivity equations. A high level of parallelism is achieved, exploiting the independence of each set of design sensitivity equations. To verify the formulation for design sensitivity analysis and to demonstrate the speedup on a parallel computer, an example is presented.

Sign in / Sign up

Export Citation Format

Share Document