Novel Algorithm for Modelling Complex Physical Systems with Bond-Graph and Python Integration

This work explores the synergistic integration between Python and Bond Graph modeling for a wide range of engineering systems, encompassing mechanical, hydraulic, electrical, thermal, and chemical domains. Employing 20-sim for Bond Graph modeling and simulation, and Python for parameter optimization, our objective is to empower engineers in the design, analysis, and diagnostic processes of complex engineering systems through the prism of modeling and simulation. The convergence of Bond Graph and Python represents a burgeoning field that amalgamates the robust Bond Graph modeling and simulation techniques with the versatility and user-friendliness inherent to the Python programming language. Bond Graphs provide a visual framework to depict intricate dynamic systems and elucidate their energy flows, while Python offers an extensive ecosystem of libraries and tools for numerical computation, data analysis, and visualization. This work not only surveys diverse approaches and tools for harmonizing Bond Graph modeling with Python programming but also delineates the profound advantages of this integration. These advantages encompass augmented simulation capabilities, enriched model analysis, and the facilitation of experimentation. Moreover, this study delves into the intricate challenges of bridging the gap between Bond Graph formalisms and the computational environment of Python. Drawing inspiration from notable applications, including the modeling of mechanical, electrical, and biological systems, as well as control system design and optimization, this work underscores the remarkable potential of Bond Graph and Python integration. Additionally, it provides an insightful review of the existing software and libraries that facilitate this integration, thereby equipping researchers and engineers to implement Bond Graph-based models and simulations within Python environments.