Parametric Modelling Method based on Knowledge Based Engineering

The LNG Bunkering Vessel Case

Master Thesis (2019)
Author(s)

N.D. Charisi (TU Delft - Mechanical Engineering)

Contributor(s)

Austin Kana – Mentor (TU Delft - Ship Design, Production and Operations)

JJ Hopman – Mentor (TU Delft - Marine and Transport Technology)

Nikos Papapanagiotou – Mentor (C-Job Naval Architects)

Wouter Beelaerts Van Blokland – Graduation committee member (TU Delft - Transport Engineering and Logistics)

Faculty
Mechanical Engineering
Copyright
© 2019 Nicole Charisi
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Nicole Charisi
Graduation Date
18-12-2019
Awarding Institution
Delft University of Technology
Programme
Marine Technology | Ship Design
Faculty
Mechanical Engineering
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Abstract

This thesis aims in the development of a parametric modelling method for the preliminary ship design based on Knowledge Based Engineering (KBE). The research work was conducted in cooperation with C-Job Naval Architects. Nowadays, parametric models can be seen as the core of the design practice since they facilitate the exploration of the preliminary design space, and thus, multiple design solutions can be assessed. The present work proposes a parametric modelling method based on knowledge building blocks instead of geometric entities, and thus, KBE was identified as a suitable tool to deploy for its development. The starting point of the design method is the identification of the design requirements and the main drivers analysis of the vessel type. The next step is associated with the determination of the High Level Primitives (HLPs), which are the building blocks of the design containing product knowledge. The HLPs are qualitatively and mathematically described. Finally, in order to form the vessel’s parametric model, the HLPs are combined and tuned to fit the design problem. The proposed method was applied to the LNG bunkering vessel in a case study. A real design case of the company and three more design cases are described and assessed.

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