Home LiteratureArticle Details
PMID: 42470831 Published · aheadofprint English

CFD-based approaches for low-carbon shipping: A systematic review.

Marine pollution bulletin ·Vol. 233 ·No. Pt 1 ·2026-07-18

Ma R, Tang Z, Zhao H, Zhao J, Zhang R, Wang Z, Cao J, Xie Y, Huang L

Abstract

The shipping industry faces urgent pressure to decarbonize in response to tightening regulations and growing concerns over marine pollution. Computational Fluid Dynamics (CFD) has emerged as a pivotal technology, enabling a paradigm shift from empirical design to precision-driven optimization for energy efficiency. This systematic review critically evaluates CFD applications across key domains: hull form optimization, surface drag reduction, propulsion improvements, wind-assisted propulsion integration, engine emission abatement, and operational management strategies. Analysis quantifies CFD-driven impacts: hull optimization consistently achieves 5%-12% resistance reduction; surface technologies yield up to 10%-30% drag reduction; propeller and wake field improvements contribute 2-10% propulsive efficiency gains. Collectively, these advancements enable an integrated hull-propulsion-operation framework that delivers substantial lifecycle emission reductions. However, challenges persist in accurately modeling multi-physics coupling and achieving holistic system-level co-design, necessitating vessel-specific solutions and standardized energy efficiency assessment frameworks. Future research should synergize AI with CFD for accelerated surrogate-based optimization, employ digital twins for real-time lifecycle performance management, and advance zero-carbon fuel propulsion systems. This review outlines a CFD-informed pathway for shipping's sustainable decarbonization, highlighting both achieved impacts and emerging opportunities.

Keywords
Air lubrication systems Computational Fluid Dynamics Decarbonization Digital twin Propulsion optimization Resistance reduction
Article Info
Journal
Marine pollution bulletin
Abbr.
Mar Pollut Bull
ISSN
1879-3363
Published
2026-07-18
Language
English
Country/Region
England
NLM ID
0260231
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: [email protected]