The Determination of Multivariable Nonlinear Models for Dynamic Systems

1998 ◽  
pp. 231-278 ◽  
Author(s):  
S.A. Billings ◽  
S Chen
Keyword(s):  
Dynamic Systems ◽  
Nonlinear Models

Model Verification of Mixed Dynamic Systems

1978 ◽  
Vol 100 (2) ◽  
pp. 266-273 ◽  
Author(s):  
J. D. Chrostowski ◽  
D. A. Evensen ◽  
T. K. Hasselman
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Dynamic Systems ◽  
Statistical Parameter ◽  
Model Verification ◽  
Passive Networks ◽  
Parameter Values ◽  
Basic Configuration ◽  
General Method

A general method is presented for using experimental data to verify math models of “mixed” dynamic systems. The term “mixed” is used to suggest applicability to combined systems which may include interactive mechanical, hydraulic, electrical, and conceivably other types of components. Automatic matrix generating procedures are employed to facilitate the modeling of passive networks (e.g., hydraulic, electrical). These procedures are augmented by direct matrix input which can be used to complement the network model. The problem of model verification is treated in two parts; verification of the basic configuration of the model and determination of the parameter values associated with that configuration are addressed sequentially. Statistical parameter estimation is employed to identify selected parameter values, recognizing varying degrees of uncertainty with regard to both experimental data and analytical results. An example problem, involving a coupled hydraulic-mechanical system, is included to demonstrate application of the method.

Determination of Significance of Stress Stiffening Effects in Flexible Multibody Dynamic Systems

1992 ◽  
Author(s):  
Jeha Ryu ◽  
Sang Sup Kim ◽  
Sung-Soo Kim
Keyword(s):  
Dynamic Simulation ◽  
Dynamic Systems ◽  
Stiffness Matrix ◽  
Multibody Systems ◽  
Flexible Body ◽  
Multibody Dynamic ◽  
Flexible Multibody ◽  
Multibody Dynamic System ◽  
Modal Stress

Abstract This paper presents a criterion for determining whether or not a flexible multibody dynamic system reveals stress stiffening effects. In the proposed criterion, the eigenvalue variation that results from adding the modal stress stiffness matrix to the conventional linear modal stiffness matrix is examined numerically before actual dynamic simulation. If the variation is sufficiently large for any flexible body, then stress stiffening effects are said to be significant and must be included in dynamic simulation of flexible multibody systems. Since the criterion uses the most general stress stiffness matrix, which can be represented as a function of applied and constraint reaction loads as well as of a system of 12 inertial loads, this criterion is applicable to any general flexible multibody dynamic systems. Several numerical results are presented to show the effectiveness of the proposed criterion.

scholarly journals Physiologically Based Structure of Mean Residence Time

2012 ◽  
Vol 2012 ◽  
pp. 1-4
Author(s):  
Mária Ďurišová
Keyword(s):  
Pharmacokinetic Parameter ◽  
Residence Time ◽  
Dynamic Systems ◽  
Mean Residence Time ◽  
Structural Identification ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Physiologically Based

A mean residence time (MRT) is an important pharmacokinetic parameter. To the author's knowledge, however, a physiologically based structure of MRT (thereafter MRT structure) has not been published so far. Primarily this is because MRT structures cannot be identified by traditional pharmacokinetic methods used for the determination of MRT. Therefore, tools from the theory of linear dynamic systems were used for the structural identification of MRT in this study. The MRT structure identified is physiologically meaningful. Accordingly, it seems that the MRT structure identified may contribute to already established knowledge about MRT.

scholarly journals Heuristic Determination of Resolving Controls for Exact and Approximate Controllability of Nonlinear Dynamic Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Asatur Zh. Khurshudyan
Keyword(s):  
Dynamic Systems ◽  
Approximate Controllability ◽  
State Constraints ◽  
Nonlinear Dynamic Systems ◽  
Algebraic Equations ◽  
Control Functions ◽  
Nonlinear Algebraic Equations ◽  
Free Parameters ◽  
Nonlinear State

Dealing with practical control systems, it is equally important to establish the controllability of the system under study and to find corresponding control functions explicitly. The most challenging problem in this path is the rigorous analysis of the state constraints, which can be especially sophisticated in the case of nonlinear systems. However, some heuristic considerations related to physical, mechanical, or other aspects of the problem may allow coming up with specific hierarchic controls containing a set of free parameters. Such an approach allows reducing the computational complexity of the problem by reducing the nonlinear state constraints to nonlinear algebraic equations with respect to the free parameters. This paper is devoted to heuristic determination of control functions providing exact and approximate controllability of dynamic systems with nonlinear state constraints. Using the recently developed approach based on Green’s function method, the controllability analysis of nonlinear dynamic systems, in general, is reduced to nonlinear integral constraints with respect to the control function. We construct parametric families of control functions having certain physical meanings, which reduce the nonlinear integral constraints to a system of nonlinear algebraic equations. Regimes such as time-harmonic, switching, impulsive, and optimal stopping ones are considered. Two concrete examples arising from engineering help to reveal advantages and drawbacks of the technique.

scholarly journals Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

2017 ◽  
Vol 3 (10) ◽  
pp. 831 ◽  
Author(s):  
Akbar Esmailzadeh ◽  
Sina Ahmadi ◽  
Reza Rooki ◽  
Reza Mikaeil
Keyword(s):  
Nonlinear Models ◽  
Accurate Determination ◽  
Well Logging ◽  
Oil Reservoir ◽  
Well Test ◽  
Sonic Log ◽  
Reservoir Permeability ◽  
Factor Log ◽  
Resistivity Log

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.

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