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Best Practice - Digital Twins for Maritime Applications

Language

English

Course format On-site
Date 2021-01-11 - 2021-05-07

Course content

This course give an introduction to digitalization technologies applied in industry, from automobile and aviation to maritime operation. We will focus on introducing date driven methods that are used for creating and simplifying models for prediction/classification. Some case studies related to model ship motion will also be presented, so that the students have a complete experience for analysis and modeling of raw data.

  • Digitalization introduction (State of the art)
  • Fundamentals of Digital Twins: Physical Entities, Virtual Models and Services (simulation, verification, monitoring, diagnosis, prognostics, and health management)
  • Data analytical overview, including data purification, down sampling and denoising
  • Local & global sensitivity analysis approaches, such as Garson, EFAST and Sobol method
  • Maritime Case studies

Learning outcomes

After passed exam the student should have advanced knowledge within the academic field of digital twin, including entities, models and services.

The student should have knowledge about methods in the field of digital twin from a maritime point of view. Has thorough knowledge of how to find and model the variety of digital twin services. They should be able to apply knowledge and communicate results.

The student should be able to perform basic digital twin applications, including understanding of physical entities, developing of virtual model and planning of digital services.

The student should be able to communicate extensive independent work and terminology of the academic field of digital twin applied to maritime systems.

Prerequisites

Admission to a programme of study is required:

  • Naval Architecture (850MD)
  • Naval Architecture (850ME)
  • Product and System Design (840MD)
  • Product and System Design (845ME)
Recommended previous knowledge

Basic knowledge of programming, modelling, simulation and data analyses.

Files/Documents

ISCED Categories

Scientific modelling