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Optimization of the propulsion plant of a Liquefied Natural Gas transport ship

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Nov 2020 Spain Publisher:Elsevier BVJournal:Energy Conversion and Management, volume 224, page 113,398 (issn: 0196-8904,

Authors: Juan Carlos Ríos-Fernández; Andrés Meana-Fernández; Bernardo Peris-Pérez; Bernardo Peris-Pérez; Antonio José Gutiérrez-Trashorras; Juan Manuel González-Caballín; Santiago Rodríguez-Artime;

doi: 10.1016/j.enconman.2020.113398 , 10.5281/zenodo.14626002 , 10.5281/zenodo.14626003

handle: 10651/57854 , 10651/59640

Optimization of the propulsion plant of a Liquefied Natural Gas transport ship

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Abstract

Stricter emission regulations and variability of fuel prices pose the focus on the optimization of steam turbine based propulsion plants of Liquefied Natural Gas (LNG) ships. The efficiency of such a propulsion plant has been improved in this work by studying the introduction of reheating and preheating stages in the onboard regenerative Rankine cycle. A thermodynamic model of the propulsion plant has been developed from the facility diagrams, being validated afterwards with available experimental data from actual ship operations.

Country

Spain

Related Organizations

University of Oviedo
Spain
University of Seville
Spain

Keywords

LNG ship, Ocean transportation

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).	8
	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%