2016.P.1.2. Applying MBSE in space observation projects: European Extremely Large Telescope – World’s Biggest Eye on the Sky
Edita Mileviciene (1)
- No Magic Europe, Lithuania
MBSE, SysML, space observation, ESO, E-ELT
The most ambitious project of the European Southern Observatory’s (ESO) is the construction of the European Extremely Large Telescope (E‐ELT) which will be by far the world’s largest optical and near‐infrared telescope, and will provide images 15 times sharper than those from the Hubble Space Telescope. Such a project poses continuous challenges to systems engineering due to its complexity in terms of requirements, operational modes, long operational lifetime, interfaces, and number of components. Since 2008, the Telescope Control System (TCS) team has adopted a number of Model Based Systems Engineering (MBSE) practices in order to cope with the various challenges ahead.
This is one of the largest publicly available MBSE information sources. This includes: the complex, interdisciplinary real world sample model1recommendations, findings, issues, Open‐Source MBSE Plugin for creating the model structure, extracting model variants, and supporting model based document generation based on DocBook, and multiple publications.
The E‐ELT is one of the most influential projects for SysML standards and MBSE tools development. One of the project’s and the supporting team’s goals was feedback for the MBSE tool vendors and OMG SysML standards. This resulted in hundreds of formal requests (hundreds of tickets in the No Magic, Inc., support system, tens of scientific and industrial papers, MBSE guidance, and inputs to SysML standard update) and informal requests, both types of which clarified the standard and significantly moved forward the MBSE support in tools.
In this presentation we will overview MBSE application for this project as the core method to manage the complexity.
We will identify major MBSE usage aspects. We will answer why and how MBSE was used for telescope modeling.
- Download the poster in PDF format here (9MB)