CSD&M : Day 1

In today’s environment characterized by the increasing complexity of systems, ensuring digital consistency across various engineering groups and fields is of paramount importance. We introduce SAME (System Architecture Model Exchange), an innovative framework, developed by Liebherr Aerospace Toulouse, designed to streamline the transfer of system architectures from MBSE environments (such as Cameo or Capella) to MBA analysis tools (like Simulink or Dymola). SAME represents a significant leap forward in harmoniously merging Model-Based Systems Engineering (MBSE) with Model-Based Analysis (MBA), thereby facilitating efficient collaboration and informed decision-making within the intricate domain of system development processes. In this paper, we will present the tool’s architecture, its data model, its capabilities, and detail the model transfer process we have implemented.

Model reconstruction is a method used to drive the development of complex system development processes in model-based systems engineering. Currently, during the iterative design process of a system, there is a lack of an effective method to manage changes in development requirements, such as development cycle requirements and cost requirements, and to realize the reconstruction of the system development process model. To address these issues, this paper proposes a model reconstruction method to support the development process model. Firstly, the KARMA language, based on the GOPPRR-E meta- modeling method, is utilized to uniformly formalize the process models constructed based on different modeling languages. Secondly, a model reconstruction framework is introduced. This framework takes a structured development requirements based natural language as input, employs natural language processing techniques to analyze the development requirements text, and extracts structural and optimization constraint information. Then, after structural reorganization and algorithm optimization, a development process model that meets the development requirements is obtained. Finally, as a case study, the development process of the aircraft onboard maintenance system is reconstructed. The results demonstrate that this method can significantly enhance the design efficiency of the development process.

In the field of systems engineering, Model-Based Systems Engineering (MBSE) plays a crucial role in developing a common understanding among diverse stakeholders through model-guided system design and implementation. Despite various effective MBSE methods, limitations persist, such as rigidity and insufficient early simulation capabilities. Addressing these gaps, we introduce the SAMAREQ method, which leverages the strengths of existing methods while providing greater flexibility and adaptability. This paper will provide an overview of the existing methods and the rationale for the SAMAREQ proposal and illustrate some concrete benefits of this tool-based method through a specific case study: the Health Agriculture Unmanned Aircraft Vehicle.

The term human-in-the-loop was used at least as early as the 1950s in the aerospace context to describe how humans could control increasingly automated functions in aircraft. Even though the term has been in use for 70 years, it has recently become a major focus of research particularly as it is related autonomous systems. While the term was gaining popularity, there was a great deal of confusion on what was meant by human-in-the-loop systems. For example, the term has been used to describe the system architectures of spam email filters, semi-autonomous vehicles [4], and missile defense systems. Even today, the term has no widely accepted singular definition outside of the broad idea of automation or autonomy working together with a human or humans to accomplish some task. This paper synthesizes disparate classifications of human-AI systems into one system architecture-based framework that can be used to classify different human-AI control loops. We then extended the current literature’s understanding of human-AI systems by proposing future iterations of human-AI control that have not been considered. Finally, we began to understand the ethical implications of implementing these architectures in safety critical systems that require tracking (autonomous systems should be responsive to the moral reasoning of human designers and operators) and tracing (decisions made by autonomous systems can be traced back to a person or team of people) to be present.

This paper introduces a Digital Engineering thread based on an industrial tool chain aiming at accelerating Model-based systems engineering (MBSE). The single source of truth is made of a set of models, each dedicated to complementary discipline; the central piece relies on the new SysML v2 standard for which Ansys is developing a new tool, web-based and collaborative, that is presented in this paper. The Digital Engineering thread is presented through a simple example developed for the demonstration.

As system complexity increases, product system development necessitates frequent cross-organization, cross-domain and multi-disciplinary collaboration. However, these factors create communication barriers among stakeholders. The diversity of model types further complicates the relationships between humans and models. To facilitate the collaborations in model-based context, we propose a model management reference model to help establish and manage the connections between stakeholders and models. The definition of the reference model begins with the identification of key concepts and their relationships within product system model management, which would be organized into three distinct views. Following this, we outline a systematic process for developing the model management reference model. The reference model describes the common information of model management concepts and provides a structured template for managing product system models. To demonstrate its practical application, we present a case study involving an UAV system. An example of the UAV system model management view, derived from the reference model, is presented, along with an analysis of potential model management issues and requirements it reflects.

As system complexity increases, product system development necessitates frequent cross-organization, cross-domain and multi-disciplinary collaboration. However, these factors create communication barriers among stakeholders. The diversity of model types further complicates the relationships between humans and models. To facilitate the collaborations in model-based context, we propose a model management reference model to help establish and manage the connections between stakeholders and models. The definition of the reference model begins with the identification of key concepts and their relationships within product system model management, which would be organized into three distinct views. Following this, we outline a systematic process for developing the model management reference model. The reference model describes the common information of model management concepts and provides a structured template for managing product system models. To demonstrate its practical application, we present a case study involving an UAV system. An example of the UAV system model management view, derived from the reference model, is presented, along with an analysis of potential model management issues and requirements it reflects.

Model Based System Engineering is one of the main enablers to improve the designing phase of a system. However, it provides limited capabilities to perform tests and simulations. To enhance those capabilities a Capgemini MBSE team explored the possibility of linking MBSE models to Petri nets, being mathematical models. This created a chain from requirements to mathematical models, enabling requirements to be verified. This investigation has been performed in the frame of the AUTOME R&I project which targeted to improve the energy usage during the railway operations of a railway system. This project was launched in 2022 with a developer’s team that codes a simulator and was joined by a MBSE team in 2023. The AUTOME R&I project provided a frame to explore the link that the MBSE team wanted to build, in a sector where companies are using Model Based System Engineering to perform the design of their systems.

Biomedical Engineering is a complex and diverse field of research, combining multi-disciplinary engineering design and standards with input and collaboration from healthcare professionals. When considering the design of assistive devices, the input and collaboration from all stakeholders emerges as an important factor in the success of the application. Existing Systems Engineering tools and techniques can assist with the synchronization and management of technological stakeholders needs. The incorporation, however, of feedback provided by the end-users, patients or health care providers, has eluded standardization in the field. This work presents a preliminary attempt at mapping the field using such tools, as well as introducing certain concepts from the field to Biomedical engineering professionals and researchers in the context of a workshop.

As an emerging aircraft, electric vertical takeoff and landing (eVTOL) aircraft have high system integration and complex control systems. Through virtual design and verification, enterprises can avoid detours in the development process, shorten the design cycle, and reduce development costs. The project investigated virtual integration methods and tools based on the Model-Based Systems Engineering (MBSE) methodology, applied to the design process of eVTOL. A multidisciplinary model library suitable for eVTOL simulation was built using Modelica and Simulink. The Functional Mock-up Interface (FMI) standard protocol was thoroughly studied, and a simulation tool was developed to integrate heterogeneous models in Functional Mock-Up Units (FMUs) and .dll formats, enabling joint simulation of multidisciplinary models from different modeling tools. An eVTOL virtual test environment was constructed by planning flight mission profiles and integrating aircraft operating context. Finally, through the analysis of virtual test results, the feasibility and correctness of virtual testing method were verified.

The effective requirement management is an important part of the Unmanned Aerial Vehicle (UAV) design. However, traditional document-based requirement management method is faced with challenges in requirements traceability and verification, making it difficult to meet the challenges of rapid changes in technology and operational needs. In this paper, a model-based requirement management method is proposed to improve the effectiveness of the UAV design. Firstly, the integrated set of needs is gotten based on the modeling of the operational scenario. Then, the design input requirements transformed from integrated set of needs are shown in the requirement model, which is also an important input to the architecture design. Finally, after verifying the design scheme, the design output specifications are obtained to guide the subsequent implementation of the UAV. Here, the proposed method is applied to the emergency rescue UAV design for demonstration purpose.