Parallel mathematical models of dynamic objects

TL;DR

This paper introduces a method to transform serial mathematical models of dynamic objects into parallel models using partial fraction decomposition, significantly reducing computation time while maintaining accuracy.

Contribution

It develops a novel algorithm for parallelizing dynamic object models, enhancing simulation speed and efficiency without sacrificing model stability or accuracy.

Findings

Parallel models are stable and accurate.

Calculation time reduced by over 20%.

Method leverages multi-core CPU processing.

Abstract

The paper deals with the developing of the methodological backgrounds for the modeling and simulation of complex dynamical objects. Such backgrounds allow us to perform coordinate transformation and formulate the algorithm of its usage for transforming the serial mathematical model into parallel ones. This algorithm is based on partial fraction decomposition of the transfer function of a dynamic object. Usage of proposed algorithms is one of the ways to decrease calculation time and improve PC usage while a simulation is being performed. We prove our approach by considering the example of modeling and simulating of fourth order dynamical object with various eigenvalues. This example shows that developed parallel model is stable, well-convergent, and high-accuracy model. There is no defined any calculation errors between well-known serial model and proposed parallel one. Nevertheless, the proposed approach's usage allows us to reduce calculation time by more than 20% by using several CPU's cores while calculations are being performed.