Architecture To Geometry - Integrating System Models With Mechanical Design

Abstract

Model-Based Systems Engineering is founded on the principle of a unified system model that can coordinate architecture, mechanical, electrical, software, verification, and other discipline-specific models across the system lifecycle. This vision of a Total System Model as the digital blueprint (or digital twin) of a system, federating models in multiple vendor tools and configuration-controlled repositories, has gained tremendous support from the practitioners. A software platform, Syndeia, developed by Intercax, provides capabilities for seamless model-based communication between systems engineering and X (where X = mechanical/electrical, simulation, PLM, ALM, project management, and other disciplines), replacing the existing document-centric approaches. This paper elaborates research and development performed by NASA JPL and Intercax for integrating system architecture models (SysML) and mechanical design models (CAD) with applications to the Europa Clipper Mission. Specifically, this paper demonstrates (1) seeding of mechanical design models from system specifications (SysML) as a starting point for mechanical design, (2) model-based connections between system and mechanical design parameters, including compare and bi-directional synchronization, (3) abstracting system architecture from mechanical assemblies for transitioning existing/old projects to a model-based systems approach, and (4) use of persistent, fine-grained connections between system architecture and mechanical design models for continuous verification and communication between the two disciplines. The paper also covers organizational, cultural, and technical challenges that need to be addressed for seamless integration between system architecture models and mechanical/electrical design models, as well as other disciplines.

What Is a Digital Twin? – Definitions and Insights From an Industrial Case Study in Technical Product Development

“…Here, two groups can be found in literature. One describing a Digital Twin as a self-sufficient virtual image, thus there is no exchange of information between the virtual and the real space (Schluse and Rossmann, 2016;Bajaj et al, 2016). The other group regards the need to exchange information between the two spaces (Huber and Kaiser, 2015;Baltes and Freyth, 2017;Shafto et al, 2010;Schleich et al, 2017).…”

A Review of the Roles of Digital Twin in CPS-Based Production Systems

“…A cradle-to-grave model of an aircraft structure's ability to meet mission requirements, including submodels of the electronics, the flight controls, the propulsion system, and other subsystems 3 [20] 2012 Ultra-realistic, cradle-to-grave computer model of an aircraft structure that is used to assess the aircraft's ability to meet mission requirements 4 [23] 2013 Coupled model of the real machine that operates in the cloud platform and simulates the health condition with an integrated knowledge from both data driven analytical algorithms as well as other available physical knowledge An integrated multi-physics, multi-scale, probabilistic simulation of an as-built system, enabled by Digital Thread, that uses the best available models, sensor information, and input data to mirror and predict activities/performance over the life of its corresponding physical twin 16 [33] 2016 A unified system model that can coordinate architecture, mechanical, electrical, software, verification, and other disciplinespecific models across the system lifecycle, federating models in multiple vendor tools and configuration-controlled repositories…”

“…a. Optimization of system behaviour during design phase [30,33,43,46,47]; b. Optimization of product lifecycle, knowing the past and present states, it is possible to predict and optimize the future performances [22,45]. Many of the mentioned uses were targeted at improving maintenance activities, such as condition-based maintenance, diagnostics and prognostics activities.…”

“…I. For most of the authors, the DT is a model that represents the system that different types of simulations can be based upon [19][20][21]25,27,28,31,33,39,[44][45][46][47][48], II.…”

“…Irrespective of the connection between the two concepts, the authors mention different purposes and characteristics of the simulations and the DT. In the aerospace, the most mentioned simulations replicate the continuous time history of flights (with historical data, maintenance history information), generating enormous databases of simulations to understand what the aircraft has experienced and to forecast future maintenance needs and interventions, with the use of Finite Element Methods (FEM), Computational Fluid Dynamics (CFD), Montecarlo and Computer-Aided Engineering (CAE) applications-based simulations [19][20][21]27,33,46,48]. Some authors highlight the fact that these simulations should be connected with on-board devices and sensors to have a continuous synchronization with the field condition [21].…”