Heterogeneous simulation and interoperability of tools applied to the design, integration and development of safety critical systems

A key issue of the assessment of the Model Based Systems Engineering (MBSE) is the integration between the requirement, functional and physical analyses. It turns out into a full capability of correlation and data exchange among the tools currently available to manage those three activities and, in particular, into a tight cooperation between the functional modeling and the physical one, being based on several methods of engineering, widely applied since longtime (mathematical, analytical, numerical and experimental). A successful accomplishment of this task within the frame of the development of the MBSE represents a milestone for both the methodology and the tools of the Systems Engineering. The application of models and simulations to support the engineering activities has spread over different domains and is strictly related to the decision making process applied to finalize an effective system design. Many kind of models are often performed to develop the systems currently populating the wide scenario of complex and smart products. When the product is a result of a material processing, some geometrical models allow describing shape and properties of the manufactured product, whose behavior is then predicted by resorting to some numerical discretization funded on a set of equations to be solved. Those models mainly describe the real nature of system, not only as is designed but even as is manufactured, thus allowing the required verification and validation activities. Due to this motivation those models belong the so–called physical modeling, whose key targets are both a mathematical modeling and a quantitative evaluation of performance. According to the MBSE the above described activity is never sufficient to completely define the details of the system under design and development. Moreover, to face the inherent complexity of new systems, being characterized by a number of functions, components and interfaces, a clear traceability from requirement to numbered part is needed. A bright allocation of each requirement to the system functions first, and to its logical blocks then, is definitely a key issue of the proposed approach. Those two main goals require a preliminary functional modeling activity, never characterized by numbers, while is dominant a prediction of system operation, behavior, interaction with other systems and stakeholders, and even a preliminary definition of well assessed requirements to motivate a consequent set of proposed layouts, based on some selected technolog

Progettazione di sistemi complessi tramite il ‘Systems Engineering’ e interoperabilità tra modelli funzionali e numerici

La crescente richiesta del mercato verso prodotti intelligenti, capaci di adattarsi autonomamente a condizioni di esercizio variabili, motiva l’incremento di complessità di molti sistemi. Il ‘Systems Engineering’ si propone come efficace metodologia per affrontare il problema dello sviluppo e della gestione di sistemi complessi. I modelli che introduce si soffermano, infatti, sull’analisi dei requisiti e degli scenari operativi e sulla preliminare definizione di blocchi funzionali e di architetture del sistema che prescindono dai componenti utilizzabili, per permettere un’ottimizzazione della configurazione. Questo lavoro propone una panoramica del metodo applicato a due casi, di ambito meccatronico e aeronautico, esponendo come le attività di modellazione numerica e funzionale si integrano tra loro, anche a livello di software, per raggiungere la cosiddetta interoperabilità tra strumenti di lavoro

Digital Twin: towards the integration between System Design and RAMS assessment through the Model–Based Systems Engineering

The design of a safety-critical system requires an effective prediction of its reliability, availability, maintainability and safety (RAMS). Anticipating the RAMS analysis at the concept design helps the designer in the trade-off of the system architecture and technologies, reduces cost of product development and the time to market. This action is rather difficult, because the RAMS analysis deals with the hazard assessment of system components, whose abstraction at concept level is never simple. Therefore, to integrate the system design and RAMS assessment, a clear path to follow is required. The paper investigates how the Model Based Systems Engineering (MBSE) supports this task and drives the system reliability allocation, through the functional and dysfunctional analyses. The implementation of the proposed approach needs to set up the tool chain. In the industrial context it must be compatible with practices, standards and tools currently used in product development. Defining a suitable process of integration of tools used for the System Design and the Safety Engineering is a need of industry. Therefore, this task is also discussed, in this paper, dealing with some examples of industrial test case

Identifying the smartness of a mechatronic coiler through the ‘Systems Engineering’

Among the requirements of a mechatronic system those related to its smart functions are crucial for an effective design. Smartness is associated to the system capability of self–adapting when the operating conditions change and usually it resorts to the action of the control system. The occurrence of the ‘Systems Engineering’ greatly improved a suitable definition of the system smartness, by identifying functions, architecture and hierarchy of the control units applied to drive the system operation. This paper briefly summarizes how the requirements related to the smartness of an industrial mechatronic system could be defined. A laying head for coiling the steel rod at the end of the rolling mill was used as an example and properties of its active magnetic suspension were investigated through the typical tools of the Systems Engineerin

Identifying the smartness of a mechatronic coiler through the ‘Systems Engineering’

Among the requirements of a mechatronic system those related to its smart functions are crucial for an effective design. Smartness is associated to the system capability of self–adapting when the operating conditions change and usually it resorts to the action of the control system. The occurrence of the ‘Systems Engineering’ greatly improved a suitable definition of the system smartness, by identifying functions, architecture and hierarchy of the control units applied to drive the system operation. This paper briefly summarizes how the requirements related to the smartness of an industrial mechatronic system could be defined. A laying head for coiling the steel rod at the end of the rolling mill was used as an example and properties of its active magnetic suspension were investigated through the typical tools of the Systems Engineerin