System-level simulation (SLS) is a collection of practical methods used in the field of systems engineering, in order to simulate with a computer the global behavior of large cyber-physical systems. Cyber-physical systems (CPS) are systems composed of physical entities regulated by computational elements (e.g. electronic controllers). System-level simulation is mainly characterized by:
a level of detail adapted to the practical simulation of large and complex cyber-physical systems (e.g. plants, aircraft, industrial facilities) the possibility to use the simulation even if the system is not fully specified, i.e. simulation does not necessarily require a detailed knowledge of each part of the system. This makes it possible to use the simulation for conception or study phases, even at an early stage in this process These two characteristics have several implications in terms of modeling choices (see further). System-level simulation shares some other characteristics with CPS simulation in general:
SLS involves multi-physics models (thermo-fluidic, mechanical, electrical, etc.) SLS is frequently cross-disciplinary, i.e., it is frequently the result of a collaboration between people with different expertises SLS is generally built upon a hierarchy of models: an organized modeling is usually necessary to make the whole model envisagable; the conceptual decomposition of the system into sub-systems is related to the notion of system of systems SLS is mainly about computing the evolution over time of the physical quantities that characterize the system of interest, but other aspects can be added like failure modeling or requirement verification.
Motivations and benefit The main motivation for SLS is the application of the holistic principle to computer simulation, which would state that simulating the system as a whole tells more than simulating parts of the system separately. Indeed, simulating the different parts of a complex system separately means neglecting all the possible effects of their mutual interactions. In many applications, these interactions cannot be ignored because of strong dependencies between the parts. For instance, many CPSs contain feedbacks that cannot be broken without modifying the system behavior. Feedbacks can be found in most modern industrial systems, which generally include one or more control systems. Another example of benefits from system-level simulations is reflected in the high degree of accuracy (e.g., less than 1% cumulative validation error over 6 months of operation) of such simulations in the case of a solar thermal system. On the other hand, simply connecting existing simulation tools, each built specifically to simulate one of the system parts, is not possible for large systems since it would lead to unacceptable computation times. SLS aims at developing new tools and choosing relevant simplifications in order to be able to simulate the whole cyber-physical system. SLS has many benefits compared to detailed co-simulation of the system sub-parts. The results of a simulation at the system level are not as accurate as those of simulations at a finer level of detail, but with adapted simplifications it is possible to simulate at an early stage, even when the system is not yet fully specified. Early bugs or design flaws can then be detected more easily. SLS is also useful as a common tool for cross-discipline experts, engineers, and managers and can consequently enhance the cooperative efforts and communication. Improving the quality of exchanges reduces the risk of miscommunication or misconception between engineers and managers, which are known to be major sources of design errors in complex system engineering. More generally SLS must be contemplated for all applications whenever only the simulation of the whole system is meaningful, while the computation times are constrained. For instance, simulators for plant operators training must imitate the behavior of the whole plant while the simulated time must run faster than real time.
Modeling choices Cyber-physical systems are hybrid systems, i.e., they exhibit a mix of discrete and continuous dynamics. The discrete dynamics mostly originate from digital sensing or computational sub-systems (e.g. controllers, computers, signal converters). The adopted models must consequently be capable of modeling such a hybrid behavior. It is common in SLS to use 0D —sometimes 1D— equations to model physical phenomena with space variables, instead of 2D or 3D equations. The reason for such a choice is the size of the simulated systems, which are generally too large (i.e. having too many elements or too much space extension) for the simulation to be computationally tractable. Another reason is that 3D models require the detailed geometry of each part to be modeled. This detailed knowledge might not be known to the modeler, especially if the modeling is done at an early step in the development process. The complexity of large CPSs makes them difficult to describe and visualize. A representation that can be arranged so that its structure looks like the structure of the original system greatly aids legibility and ease of comprehension. Therefore, acausal modeling is generally preferred to causal modeling with block diagrams. Acausal modeling is also preferred because component models can be reused, contrary to models developed as block diagrams.
Domains of application System-level simulation is used in various domains like:
building engineering for heating, ventilating and air conditioning simulation automotive engineering power plants (solar, combined-cycle) MEMS naval architecture aircraft architecture offshore oil production
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