Simulation-driven robust optimization of the design of zero emission vessels

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Abstract

The International Maritime Organization (IMO) Decarbonization Roadmap for curbing and eliminating Greenhouse Gas (GHG) emissions by 2030 and 2050, respectively, is a “herculean” task in its own respect. If it is now combined with fundamental changes in trade dynamics, volatile market conditions, tighter shipping financing platforms with sustainability-linked interest rates and international safety regulations setup, a completely new framework for commercial ship design characterized by strict and often contradicting requirements emerge In parallel, zero carbon fuels available (readily or in the future) require extensive technological modifications and technical leaps in the current arrangements ship propulsion plants (with little to no existing reference) characterized by elevated consumption figures due to low energy density leading to an overshoot in voyage expense costs and the Total Cost of Ownership (TCO), respectively. Considering such a tight design space, holistic approaches with lifecycle considerations aiming at robust designs are deemed necessary. Pursuant to this roadmap, the authors have developed a design methodology fully integrated within the CAE software CAESES™ that encompass all aspects of ship design (stability, strength, powering and propulsion, safety, economics) and has an inherent dynamic voyage simulation module, enabling the user to simulate the response in variations of the geometrical, design variables of the vessel under uncertainty. The methodology has been extended to model the design and propulsion plant of an Ammonia powered Large Bulk carrier and deployed in global ship design optimization studies and utility-based ranking and selection process.
Original languageEnglish
Article number4731
Number of pages29
JournalEnergies
Volume16
Issue number12
DOIs
Publication statusPublished - 15 Jun 2023

Keywords

  • holistic ship design optimization
  • simulation driven design
  • zero emission ships
  • design for efficiency
  • use of big data
  • design under uncertainty
  • vessel digital twin

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