Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving

The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-d...

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Bibliographic Details
Published in:Processes 2019-06, Vol.7 (6), p.334
Main Authors: Hou, Kexi, Li, Yaohui
Format: Article
Language:English
Subjects:
Algebra
Algorithms
Complex variables
Decomposition
Design optimization
Differential equations
Domains
Heavy duty trucks
Iterative methods
Libraries
Mathematical models
Optimization
Ordinary differential equations
Parameters
Simulation
Variables
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Description
Summary:The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of parameters, constraints, and objective functions of the optimization model. The simulation during the optimization iterative process needs only to resolve the variables on the MSG, and therefore this method could decrease the simulation time greatly during every iterative step of the optimization process. As an example, the parameter optimization on economy of fuel for a heavy truck was realized to demonstrate the efficiency of this solving strategy. This method has been implemented in MWorks—a Modelica-based simulation platform.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr7060334