Exergetic Port-Hamiltonian Systems for Multibody Dynamics

TL;DR

This paper introduces a hierarchical, compositional modeling framework for multibody systems using Exergetic Port-Hamiltonian Systems, simplifying complex system assembly and promoting model reuse across physical domains.

Contribution

It presents a novel EPHS-based approach for multibody dynamics, emphasizing hierarchical decomposition and geometric primitive components over traditional equation-based models.

Findings

Hierarchical decomposition reduces cognitive load in modeling.

Encapsulation facilitates subsystem reuse and management.

EPHS offers a physically interpretable alternative to Modelica.

Abstract

Multibody dynamics simulation plays an important role in various fields, including mechanical engineering, robotics, and biomechanics. Setting up computational models however becomes increasingly challenging as systems grow in size and complexity. Especially the consistent combination of models across different physical domains usually demands a lot of attention. This motivates us to study formal languages for compositional modeling of multiphysical systems. This article shows how multibody systems, or more precisely assemblies of rigid bodies connected by lower kinematic pairs, fit into the framework of Exergetic Port-Hamiltonian Systems (EPHS). This approach is based on the hierarchical decomposition of systems into their ultimately primitive components, using a simple graphical syntax. Thereby, cognitive load can be reduced and communication is facilitated, even with non-experts. Moreover, the encapsulation and reuse of subsystems promotes efficient model development and management. In contrast to established modeling languages such as Modelica, the primitive components of EPHS are not defined by arbitrary equations. Instead, there are four kinds of components, each defined by a particular geometric structure with a clear physical interpretation. This higher-level approach could make the process of building and maintaining large-scale models simpler and also safer.