This paper proposes a service-oriented tool-chain with an emphasis on domain-specific views, simulation automation, and process management to support model-based system engineering of aero-engines. In the tool-chain, a domain-specific modeling approach is adopted to facilitate the descriptions of co-design workflows and the related development information. The relevant domain-specific models are the basis for automated creation of a Web-based process management system consolidating and controlling service-oriented technical resources (models, data, and tools). In particular, the system also provides support for automated orchestration of tool operations, model, and simulation configurations. In order to promote the model and tool interoperability, the tool-chain adopts open standards for integrations, including open services for lifecycle collaboration and functional mock-up interface. Finally, through a case study of simulation-based aero-engine performance analysis, we evaluate the flexibility and efficiency of this tool chain by comparing it with a traditional simulation process both qualitatively and quantitatively.

The complexity of automated driving poses challenges for providing safety assurance. Focusing on the architecting of an Autonomous Driving Intelligence (ADI), i.e. the computational intelligence, sensors and communication needed for high levels of automated driving, we investigate so called safety supervisors that complement the nominal functionality. We present a problem formulation and a functional architecture of a fault-tolerant ADI that encompasses a nominal and a safety supervisor channel. We then discuss the sources of hazardous events, the division of responsibilities among the channels, and when the supervisor should take over. We conclude with identified directions for further work.

In embarking towards Cyber-Physical Systems (CPS) withunprecedented capabilities it becomes essential to improve our understanding ofCPS complexity and how we can deal with it. We investigate facets of CPScomplexity and the limitations of Collaborating Information Processing Systems(CIPS) in dealing with those facets. By CIPS we refer to teams of humans andcomputer-aided engineering systems that are used to develop CPS. Furthermore,we specifically analyze characteristic differences among software and physicalparts within CPS. The analysis indicates that it will no longer be possible to relyonly on architectures and skilled people, or process and model/tool centeredapproaches. The tight integration of heterogeneous physical, cyber, CPS components,aspects and systems, results in a situation with interfaces and interrelationseverywhere, each requiring explicit consideration. The role of modelbasedand computer aided engineering will become even more essential, anddesign methodologies will need to deeply consider interwoven systems andsoftware aspects, including the hidden costs of software.

In embarking towards Cyber-Physical Systems (CPS) with unprecedented capabilities it becomes essential to improve our understanding of CPS complexity and how we can deal with it. We investigate facets of CPS complexity and the limitations of Collaborating Information Processing Systems (CIPS) in dealing with those facets. By CIPS we refer to teams of humans and computer-aided engineering systems that are used to develop CPS. Furthermore, we specifically analyze characteristic differences among software and physical parts within CPS. The analysis indicates that it will no longer be possible to rely only on architectures and skilled people, or process and model/tool centered approaches. The tight integration of heterogeneous physical, cyber, CPS components, aspects and systems, results in a situation with interfaces and interrelations everywhere, each requiring explicit consideration. The role of model-based and computer aided engineering will become even more essential, and design methodologies will need to deeply consider interwoven systems and software aspects, including the hidden costs of software.

Cyber-Physical Systems are inherently complex. Reducing the design complexity of such systems, is beneficial for scalability of the design. In this paper, a method for design optimization of these systems is proposed to facilitate the decomposition of the optimization problem for the whole system into smaller sub-problems and coordination of modular solutions to reach the desired optimum. The coordinated solutions are either identical to the optimal solution of the complex optimization problem for the system as a whole or within an acceptable error margin of it. To demonstrate the efficacy of the method, it is applied to a mechatronic case study. The results provide evidence for the potential feasibility of the methodology in terms of meeting the requirements on the solutions, while reducing the computational demand of the design process.

Co-simulation has been proposed as a method for facilitating integrated simulation of multi-domain models of Cyber-physical Systems (CPS). To ensure that co-simulations are well-managed, concerns beyond technical mechanisms for co-simulation also need to be addressed during tool-chain development. In this paper, an evolution of two frameworks supporting co-simulation tool-chain development is first introduced. Drawing upon the empirical findings from an initial framework SPIT developed based on model-driven techniques, we develop a service-oriented framework, SPIRIT based on model-driven and tool-integration techniques. Moreover, we propose a 3D viewpoint based method to formalize concept models of co-simulation tool-chains. In order to evaluate the evolution, we use visualizations of related concept models to compare tool-chains developed based on these two frameworks. 

Model-based systems engineering (MBSE) is an emerging technique widely used in current industry. It is a leading way expected to become a next-generation standard practice in the systems engineering. Fundamental tenets of systems engineering can be supported by a model-based approach to minimize design risks and avoid design changes in late development stages. The models can be used to formalize, analyze, design, optimize, verify and validate target products which help developers to integrate engineering development, organization and product across domains. Though model-based development is well established in specific domains, such as software, mechanical system, electric systems, its role in integrated development from system aspect is still a big challenge for current Chinese industry. In this paper, a survey from volunteers who related with MBSE is taken by questionnaires. The purpose of this survey is to highlight the usage and status of MBSE in current Chinese industry and address roughly the understandings of MBSE concepts among system developers in China based on the answers about usages, advantages, barriers, concerns, trends of MBSE, particularly the perspective of tool-chain development.

This paper proposes a SPIRIT framework supporting model-based systems engineering (MBSE) tool-chain development of advanced cyber-physical systems (CPS) with emphasis on tool integration, process management, automated verification and validation. The core features of the developed MBSE tool-chain include domain-specific modeling to describe CPS development, service-oriented deployment of technical resources (data, model and tool operations) and process management through IT platforms. The framework has two purposes: to support tool-chain development with a systems engineering approach; to promote interoperability of the whole developed tool-chain through a service-oriented approach. The framework covers social, process, information and technical aspects aiming to integrate various related MBSE techniques with tool-chain development. Based on the framework, an MBSE tool-chain prototype is developed, and the flexibility and interoperability are evaluated through a case study.

Robots and autonomous systems have become a part of our everyday life, therefore guaranteeing their safety is crucial. Among the possible ways to do so, monitoring is widely used, but few methods exist to systematically generate safety rules to implement such monitors. Particularly, building safety monitors that do not constrain excessively the system’s ability to perform its tasks is necessary as those systems operate with few human interventions. We propose in this paper a method to take into account the system’s desired tasks in the specification of strategies for monitors and apply it to a case study. We show that we allow more strategies to be found and we facilitate the reasoning about the trade-off between safety and availability. 

Cyber-physical systems (CPS) are integrations of computational and physical processes. They represent a new generation of systems that interact with humans and expand the capabilities of the physical world through computation, communication, and control. At the same time, actions and interventions associated with this complex systems can have highly unpredictable and unintended consequences. Furthermore, today’s practices of CPS design and implementation are not able to support the level of complexity required to detect these consequences. 

One methodology to approach this complex problem space is systems thinking (ST). Systems thinking emerges as both a worldview and a process in the sense that it informs one's understanding regarding a system and can be used as a problem-solving approach. Systems thinking is an abstraction-oriented analysis approach, specifically designed for heterogeneous complex systems.

At the same time, another methodology, design thinking (DT), has enjoyed significantly increased visibility and importance over the last decade. Design thinking is a creative problem-solving approach, which puts human to the center and focuses first on the needs and experiences of the user.

This paper aims to illustrate the possibility to use design thinking and systems thinking methodologies together to better deal with the complexity related problems during CPS design and implementation. The study proposes visual analytics as an integrative tool between these two methodologies, by (1) analyzing and understanding CPS development process through systems thinking, and (2) innovating and transforming the process through design thinking. To this end, an example use case is described and the application of the blended methodology explained step by step in relation to the use case. Visual analytics and data visualization are discussed in several steps and the possible benefits highlighted.