[Paper Review] Co-simulation: State of the art

Cláudio Gomes, Casper Thule+3 more|arXiv (Cornell University)|Feb 1, 2017

Simulation Techniques and Applications138 references80 citations

TL;DR

This survey summarizes existing co-simulation approaches, presents a taxonomy of discrete-event, continuous-time, and hybrid co-simulation, and identifies core challenges and research opportunities for modular, stable, and accurate coupling of simulation units.

ABSTRACT

It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures. One possible solution for this challenge is to use a heterogeneous model-based approach where different teams can produce their conventional models and carry out their usual mono-disciplinary analysis, but in addition, the different models can be coupled for simulation (co-simulation), allowing the study of the global behavior of the system. Due to its potential, co-simulation is being studied in many different disciplines but with limited sharing of findings. Our aim with this work is to summarize, bridge, and enhance future research in this multidisciplinary area. We provide an overview of co-simulation approaches, research challenges, and research opportunities, together with a detailed taxonomy with different aspects of the state of the art of co-simulation and classification for the past five years. The main research needs identified are: finding generic approaches for modular, stable and accurate coupling of simulation units; and expressing the adaptations required to ensure that the coupling is correct.

Motivation & Objective

• Provide an overview of co-simulation approaches across domains.
• Bridge discrete-event, continuous-time, and hybrid co-simulation to enable multi-disciplinary collaboration.
• Identify main research challenges in modular coupling and correct adaptations.
• Propose a taxonomy and classification framework for state-of-the-art co-simulation frameworks.

Proposed method

• Classify co-simulation into discrete-event, continuous-time, and hybrid approaches.
• Describe simulation units as black-box entities and formalize co-simulation scenarios and orchestrators.
• Present a formal DEVS-based model for DE simulation units and detailing of co-simulation orchestration.
• Discuss challenges related to causality, determinism, dynamic structure, and distribution for DE, CT, and hybrid co-simulation.
• Review the role of standards like FMI in enabling interoperable co-simulation.

Experimental results

Research questions

• RQ1 What are the predominant approaches to co-simulation across discrete-event, continuous-time, and hybrid paradigms?
• RQ2 What are the key compositionality properties and challenges when coupling multiple simulation units?
• RQ3 How do standards and architectures (e.g., FMI) influence interoperability and IP protection in co-simulation?
• RQ4 What research opportunities exist to achieve generic, modular, stable, and accurate co-simulation of complex multi-domain systems?

Key findings

• Co-simulation enables global simulation of coupled systems by composing black-box simulators while protecting IP.
• A formal taxonomy differentiates DE, CT, and hybrid co-simulation and highlights their respective challenges and techniques.
• Challenges identified include causality, determinism, dynamic structure, algebraic loops, convergence, stability, and real-time constraints.
• The FMI standard and related frameworks are influential in enabling cross-tool collaboration and addressing interoperability.
• Multi-domain CPS-like applications remain limited in scale, underscoring the need for generic coupling approaches and improved orchestration.
• Research opportunities include developing modular, stable, and accurate coupling methods and formal verification for orchestrators.