A methodology for developing simulation models of complex systems

2007 ◽  
Vol 202 (3-4) ◽  
pp. 385-396 ◽  
Author(s):  
Craig A. Aumann
Keyword(s):  
Complex Systems ◽  
Simulation Models

Unknown Parameter Excitation and Estimation for Complex Systems With Dynamic Performances

2021 ◽  
Vol 143 (9) ◽  
Author(s):  
Yi-Ping Chen ◽  
Kuei-Yuan Chan
Keyword(s):  
Complex Systems ◽  
Simulation Models ◽  
Credible Interval ◽  
Specific Model ◽  
Model Parameters ◽  
Real Model ◽  
Dynamic Case ◽  
Dynamic Performances ◽  
Validation Stage ◽  
Setting Parameters

Abstract Simulation models play crucial roles in efficient product development cycles, therefore many studies aim to improve the confidence of a model during the validation stage. In this research, we proposed a dynamic model validation to provide accurate parameter settings for minimal output errors between simulation models and real model experiments. The optimal operations for setting parameters are developed to maximize the effects by specific model parameters while minimizing interactions. To manage the excessive costs associated with simulations of complex systems, we propose a procedure with three main features: the optimal excitation based on global sensitivity analysis (GSA) is done via metamodel techniques, for estimating parameters with the polynomial chaos-based Kalman filter, and validating the updated model based on hypothesis testing. An illustrative mathematical model was used to demonstrate the detail processes in our proposed method. We also apply our method on a vehicle dynamic case with a composite maneuver for exciting unknown model parameters such as inertial and coefficients of the tire model; the unknown model parameters were successfully estimated within a 95% credible interval. The contributions of this research are also underscored through multiple cases.

Complex systems and simulation models

Cybernetics ◽  
1977 ◽  
Vol 12 (6) ◽  
pp. 862-870
Author(s):  
N. P. Buslenko
Keyword(s):  
Complex Systems ◽  
Simulation Models

scholarly journals Second-order control of complex systems with correlated synthetic data

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Juste Raimbault
Keyword(s):  
Complex Systems ◽  
Financial Time Series ◽  
Transportation Network ◽  
Synthetic Data ◽  
Simulation Models ◽  
Generation Model ◽  
Urban Growth Model ◽  
Test Models ◽  
Spatial Systems ◽  
Synthetic Datasets

AbstractThe generation of synthetic data is an essential tool to study complex systems, allowing for example to test models of these in precisely controlled settings, or to parametrize simulation models when data is missing. This paper focuses on the generation of synthetic data with an emphasis on correlation structure. We introduce a new methodology to generate such correlated synthetic data. It is implemented in the field of socio-spatial systems, more precisely by coupling an urban growth model with a transportation network generation model. We also show the genericity of the method with an application on financial time-series. The simulation results show that the generation of correlated synthetic data for such systems is indeed feasible within a broad range of correlations, and suggest applications of such synthetic datasets.

scholarly journals Model-Oriented Programming as a Consequence of the Structural Theory of Multi-Сomponent Complex Systems

2021 ◽  
Vol 15 ◽  
pp. 1-12
Author(s):  
Yury I. Brodsky ◽  
Leonid V. Kruglov
Keyword(s):  
Complex Systems ◽  
Wide Class ◽  
High Performance ◽  
Object Oriented ◽  
Simulation Models ◽  
Structural Theory ◽  
Programming Paradigm ◽  
Key Features ◽  
Object Oriented Approach ◽  
Oriented Approach

The paper offers a new programming paradigm, which implements CAD methods in programming, The level of encapsulation in model-oriented programming is higher than in the object-oriented approach. The key features of the MO-programming are declarative style (no imperative programs), and focusing on the distributed and high-performance calculations. The method proposed is based on the structural theory of multi-component complex systems, and is applicable for a rather wide class of tasks including the elaboration of simulation models of such systems.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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