Co-simulation: State of the art Cl´audio Gomes ([email protected]) Casper Thule ([email protected]) David Broman ([email protected]) Peter Gorm Larsen ([email protected]) Hans Vangheluwe ([email protected]) February 3, 2017 Contents 1 Introduction 3 1.1 Motivation . .3 1.2 Co-simulation . .4 1.3 Need for the Survey . .6 1.4 Outline ...........................................6 2 Modeling, Simulation, and Co-simulation 7 2.1 Dynamical Systems { Models of Real Systems . .7 2.2 Simulators { Computing the Behavior Trace . .8 2.3 Simulation Units - Mock-ups of Reality . 10 2.4 Compositional Co-simulation . 10 3 Discrete Event Based Co-simulation 11 3.1 DE Simulation units . 11 3.2 DE Co-simulation Orchestration . 13 3.3 Challenges . 19 3.3.1 Causality . 19 3.3.2 Determinism and Confluence . 19 3.3.3 Dynamic Structure . 20 3.3.4 Distribution . 20 4 Continuous Time Based Co-simulation 20 arXiv:1702.00686v1 [cs.SY] 1 Feb 2017 4.1 CT Simulation Units . 20 4.2 CT Co-simulation Orchestration . 22 4.3 Challenges . 27 4.3.1 Modular Composition { Algebraic Constraints . 27 4.3.2 Algebraic loops . 31 4.3.3 Consistent Initialization of Simulators . 33 4.3.4 Compositional Convergence { Error Control . 33 4.3.5 Compositional Stability . 36 1 4.3.6 Compositional Continuity . 39 4.3.7 Real-time Constraints . 40 5 Hybrid Co-simulation Approach 41 5.1 Hybrid Co-simulation Scenarios . 41 5.2 Challenges . 43 5.2.1 Semantic Adaptation . 43 5.2.2 Predictive Step Sizes . 43 5.2.3 Event Location . 43 5.2.4 Discontinuity Identification . 44 5.2.5 Discontinuity Handling . 44 5.2.6 Algebraic Loops, Legitimacy, and Zeno Behavior . 44 5.2.7 Stability . 45 5.2.8 Theory of DE Approximated States . 45 5.2.9 Standards for Hybrid Co-simulation . 45 6 Classification 45 6.1 Methodology . 46 6.2 Taxonomy . 46 6.3 State of the Art . 47 6.4 Discussion . 47 7 Concluding Remarks 48 A Historical Perspective of Co-simulation 77 A.1 One Formalism and Dynamic Iteration . 77 A.2 Two Formalisms: Digital and Analog Co-simulation . 78 A.3 Multi-abstraction/Multi-Formalism Co-simulation . 78 A.4 Black-box Co-simulation . 79 A.5 Real-time Co-simulation . 79 A.6 Many simulation units: Large Scale Co-simulation . 79 B State of the Art in Co-simulation Frameworks 79 C Co-Simulation Scenario Categorization 123 C.1 Non-Functional Requirements . 123 C.1.1 Fault Tolerance . 123 C.1.2 Configuration Reusability . 123 C.1.3 Performance . 123 C.1.4 IP Protection . 124 C.1.5 Parallelism . 125 C.1.6 Distribution . 126 C.1.7 Hierarchy . 126 C.1.8 Scalability . 127 C.1.9 Platform Independence . 127 C.1.10 Extensibility . 127 C.1.11 Accuracy . 127 2 C.1.12 Open source . 128 C.2 Simulator Requirements . 128 C.2.1 Information Exposed . 129 C.2.2 Causality . 132 C.2.3 Time Constraints . 134 C.2.4 Rollback Support . 136 C.2.5 Availability . 138 C.3 Framework Requirements . 140 C.3.1 Standard . 140 C.3.2 Coupling . 140 C.3.3 Number of Simulation Units . 142 C.3.4 Domain . 144 C.3.5 Dynamic structure . 146 C.3.6 Co-simulation Rate . 146 C.3.7 Communication Step Size . 148 C.3.8 Strong Coupling Support . 150 C.3.9 Results Visualization . 152 C.3.10 Communication Approach . 154 D List of Acronyms 157 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 op- portunities, 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. 1 Introduction 1.1 Motivation Truly complex engineered systems that integrate physical, software and network aspects, are emerg- ing [153, 185]. Due to external pressure, the development of these systems has to be concurrent and distributed, that is, divided between different teams and/or external suppliers, each in their own domain and each with their own tools. Each participant develops a partial solution to a constituent 3 system, that needs to be integrated with all the other partial solutions. The later in the process the integration is done, the less optimal it is [228]. Innovative and truly optimal multi-disciplinary solutions can only be achieved through a holistic development process [237] where the partial solutions developed independently are integrated sooner and more frequently. Furthermore, the traditional activities carried out at the partial solution level |such as requirements compliance check, or design space exploration| can be repeated at the global level, and salient properties spanning multiple constituent systems can be studied. Modeling and simulation can improve the development of the partial solutions (e.g., see Friedman and Ghidella [92]), but falls short in fostering this holistic development process [34]. To understand why, one has to observe that: a) models of each partial solution cannot be exchanged or integrated easily, because these are likely developed by different specialized tools; and b) externally supplied models have Intellectual Property (IP), hence cannot be cheaply disclosed to system integrators. 1.2 Co-simulation Co-simulation is proposed as a solution to overcome these important challenges. It consists of the theory and techniques to enable global simulation of a coupled system via the composition of simulators. Each simulator is a black box mock-up of a constituent system, developed and provided by the team that is responsible for that system. This allows each team/supplier to work on its part of the problem with its own tools without having the coupled system in mind, and eases the relationship between system integrators and suppliers: the latter provide virtual mock-ups of the solutions they are selling without revealing their IP; the former can perform early conformance checks and evaluate different designs from multiple competing suppliers. An alternative to co-simulation is co-modelling, were models are described in a unified language, and then simulated. There are advantages to this approach but each domain has its own particular- ities when it comes to simulation (e.g., see [63, 167, 246]) making it impractical to find a language and simulation algorithm that fits all. As part of the systematic review that led to the current document (see section 6.1 for de- tails), we took note of the approaches to co-simulation and the publications in applications of co-simulation. The approaches to co-simulation shaped the taxonomy in section 6.2 and the appli- cations of co-simulation shows that in the last five years, co-simulation has been applied in many different engineering domains, as fig. 1 shows. In concrete, the publications are: Automotive - [4, 26, 28, 29, 39, 44, 69, 74, 82, 132, 156, 168, 213, 237, 258, 264, 266] Electricity Production and Distribution - [3, 5, 33, 78, 95, 96, 111, 120, 139, 157{159, 163, 205, 224, 243, 248, 255, 267] HVAC - [73, 88, 114, 188, 192, 251] IC and SoC Design - [209] Maritime - [194, 195] Robotics - [143, 198, 199, 265] A closer look at the publications shows, however, that the average reported co-simulation scenario includes only two simulators, each a mock-up of a constituent system from a different domain. While this gives evidence that co-simulation.