This presentation was made at CAASE18, The Conference on Advancing Analysis & Simulation in Engineering. CAASE18 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, to share experiences, discuss relevant trends, discover common themes, and explore future issues.

Resource Abstract

As the fields of Model-based Systems Engineering (MBSE) and Computer Aided Engineering (CAE) continue to grow in order to support increasingly connected and complex product development, there is an increasing need to connect these two disciplines together. The role of MBSE models has evolved to support analysis in addition to visual diagrams, as evidenced by SysML v2 efforts. CAE models, such as dynamic systems simulations, are increasingly focused on system level interactions. Therefore there is an opportunity to use MBSE as a platform to connect different disciplines together such as requirements, architecture, simulation models, etc. However, the diverse set of tools and proprietary data formats / data models / APIs makes such integration hard. Traditional solutions rely on point to point integrations between tools and development of custom workflows. Moreover, the data is siloed within tools and is not easily linked, increasing the chance for inconsistency. Instead of such custom integrations, we propose to use open web standards such as linked data, ontologies, triple store databases to extract data and data models from tools in a neutral format. Along with the neutral format we propose to use open source software for revision control and continuous integration to create workflows that automate the execution of such interconnected models. One example is linking requirements with dynamic systems simulations. The key pieces in this environment are the APIs provided by tools and common data models (also referred to as ontologies) that exist irrespective of the tool used. In this way we will conclude with a future vision in which tool vendors are encouraged to provide APIs that make it easier to access the underlying data models and end users (i.e. companies) are empowered to own their data instead of being siloed in a multitude of tools or monolithic platform solutions from large vendors.