WeChat Mini Program
Old Version Features

ANDES, the High Resolution Spectrograph for the ELT: Model-Based Systems Engineering Approach

MODELING, SYSTEMS ENGINEERING, AND PROJECT MANAGEMENT FOR ASTRONOMY XI, PT 1(2024)

INAF Osservatorio Astron Brera

Cited 0|Views3
Abstract
ANDES (ArmazoNes high Dispersion Echelle Spectrograph) is one of the second-phase instruments planned for the Extremely Large Telescope (ELT) of ESO. ANDES will provide high-resolution spectroscopy in the visible and near-infrared wavelengths, enabling a wide range of scientific investigations, such as characterizing exoplanet atmospheres, testing fundamental physics, and measuring the cosmic expansion. In this paper, we present the general strategy of the Model-Based Systems Engineering (MBSE) approach that we have used to design the instrument during the Phase B-One, which covers the system architecture review (SAR) successfully completed at end 2023. We describe how we have applied the Cameo Systems Modeler tool to create and manage the system model in compliance with the SysML standard to perform requirements and interfaces management, structure verification and validation, and trade-off analysis. We also emphasize that ANDES is used as a test case for the application of the MBSE methodology in the astronomical field, in order to create a standard of procedures to perform all the actions and tasks that serve to satisfy all the steps in the various design phases of an ESO project. In fact, the initial phases require specific tasks, such as the analysis of requirements, the flow-down of specifications to the subsystems, the tracing of interfaces, the analysis of budgets. Since there is no tool that specifically encompasses all these capabilities in the astronomical field, it is necessary to define a robust methodology that can be taken as an example for future astronomical instrumentation. We discuss the benefits and challenges of using MBSE for ANDES, as well as the lessons learned and best practices that can be useful for other astronomical instrument projects.
More
Translated text
Key words
ANDES,ELT,ESO,spectrograph,system engineering,MBSE,SysML
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined