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Applied Thermal Engineering
Article . 2022 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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http://dx.doi.org/10.1016/j.ap...
Article
License: Elsevier TDM
Data sources: Sygma
Applied Thermal Engineering
Article . 2022 . Peer-reviewed
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Parametric investigation and optimal selection of the hybrid turbocharger system for a large marine four-stroke dual-fuel engine

Authors: Figari, Massimo; Theotokatos, Gerasimos; Coraddu, Andrea; Stoumpos, Sokratis; Mondella, Teresa;

Parametric investigation and optimal selection of the hybrid turbocharger system for a large marine four-stroke dual-fuel engine

Abstract

Hybrid turbochargers can become an attractive solution for new built and retrofitted ship power plants, as their use can result in increasing the plant efficiency and reducing emissions. This study aims at computationally investigating the hybrid turbocharger effects on a large marine dual-fuel four-stroke engine performance and emissions characteristics as well as determining its electrical generator optimal size for the case of a ship power plant considering an actual operating profile. An existing model of a large marine four-stroke dual-fuel engine of the zero/one-dimensional type, which was developed in the commercial software GT-Power, is extended to include the hybrid turbocharger sub-model. This model is subsequently employed to carry out a parametric investigation considering a wide range for the hybrid turbocharger electric motor power. The derived results are analysed to identify the variations of the investigated dual fuel engine performance and emissions parameters in the whole engine operating envelope at both the diesel and gas modes, whilst taking into account the engine and its components operational limits. For the considered annual load profile, the results demonstrate that the optimal nominal size of the hybrid turbocharger electric motor power is 300 kW and leads to an annual energy surplus between 2 to 3% of the annually delivered engine mechanical energy. This study benefits the quantification of the hybrid turbocharger impact on large marine dual fuel four-stroke engines as well as the ship energy efficiency, thus providing useful decision support to facilitate the shipboard implementation of this technology.

Countries
United Kingdom, Italy
Keywords

VM, Naval architecture. Shipbuilding. Marine engineering, 621, 600, 620, Actual operating profile; Hybrid turbocharger; Impact quantification; Marine Dual fuel engine; Optimal size selection; Simulation

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    20
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Average
Top 10%
Green
hybrid