Forthcoming Articles

International Journal of Ocean Systems Management

International Journal of Ocean Systems Management (IJOSM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Ocean Systems Management (4 papers in press)

Regular Issues

  • Review on performance degradation research and maintenance of gearboxes in offshore wind turbines   Order a copy of this article
    by Yulin Li, Rong Yuan, Yang Ou, Fan Wu, Lei Zhou, Bingcheng Li 
    Abstract: Gearboxes are core energy transmission components of offshore wind turbines, and their operating status directly affects turbine reliability and economy. Long-term exposure to harsh offshore environments high humidity, high salt spray and strong vibrations easily leads to gearbox performance degradation and even failure. Therefore, research on its degradation mechanism and maintenance strategy is highly significant. This paper reviews offshore wind turbine gearboxes, covering failure types, fault diagnosis methods and preventive maintenance. Offshore environments and natural disasters affect performance degradation. Existing studies have optimised diagnosis algorithms and proposed various maintenance strategies. Vibration-based diagnosis is common but inaccurate and vulnerable to interference. Integrating big data and machine learning can effectively improve accuracy and efficiency for offshore wind turbine gearbox operation and maintenance.
    Keywords: offshore wind turbines; gearboxes; performance degradation; fault diagnosis.
    DOI: 10.1504/IJOSM.2026.10078041
     
  • An Intelligent Computing-Based Multi-Scale Efficient Reliability Topology Optimisation Method and its Application in Marine Composite Structures   Order a copy of this article
    by Xinkai Guo, Wen Sun 
    Abstract: The structural integrity of marine composite structures is frequently compromised by manufacturing-induced heterogeneities, such as void coalescence and fiber misalignment, which introduce spatially correlated uncertainties that deterministic design methods fail to capture. This study establishes a cross-scale reliability-based topology optimization framework driven by intelligent computing. The methodology constructs a seamless information bridge between microscopic material states and macroscopic structural performance via a Kriging surrogate model, which predicts effective stiffness tensors with a coefficient of determination exceeding 0.93, thereby replacing expensive homogenization routines. Topological evolution is governed by a novel dynamic hybrid particle swarm optimization algorithm that ingests adjoint sensitivity fields to guide the heuristic search. Numerical validation on subsea support components and ship deck stiffeners demonstrates that the framework reduces computational time by 34.4% compared to traditional penalization methods while strictly satisfying reliability indices () through a cubic penalty formulation.
    Keywords: structural integrity; marine composite structures; reliability-based topology optimization; dynamic hybrid particle swarm optimization algorithm.
    DOI: 10.1504/IJOSM.2026.10078294
     
  • Research on the reliability of Ocean Engineering Structure based on Improved Adaptive Kriging Model   Order a copy of this article
    by Kaifeng Dang, Yongfeng Yu, Yingying Dang, Yongjun Luo 
    Abstract: In ocean engineering structural reliability analysis, adaptive surrogate models significantly reduce computational burdens. Based on the Kriging model, this paper proposes a novel adaptive active learning function to simplify model complexity and diminish the stochastic uncertainty of random variables. Combined with Monte Carlo sampling, the proposed adaptive Kriging-U with Influence function and penalty factor (AK-UIP) algorithm accurately constructs the limit state and evaluates failure probability using minimal sample points. Additionally, to address the misjudgement of predicted sample symbols, a robust stopping criterion based on maximum error prediction samples (SCB-MEPS) is introduced, which is applicable to various learning functions. Verified through relevant examples, these proposed methods effectively improve both the efficiency and accuracy of structural reliability analysis in ocean engineering.
    Keywords: Ocean engineering; Adaptive Kriging model; Structure reliability analysis; U function; Stopping Criterion.
    DOI: 10.1504/IJOSM.2026.10078472
     
  • A Review of Fire Safety Engineering in Offshore Platform Safety Engineering   Order a copy of this article
    by Qingyang Deng 
    Abstract: This paper provides an overview of the recent advancements and challenges in fire safety engineering for offshore platforms. With the increasing complexity and importance of offshore operations ensuring fire safety has become a critical aspect of offshore platform safety engineering. This review discusses the latest research, technologies, and practices in fire prevention, detection, suppression, and evacuation on offshore platforms, highlighting key developments from the past three years.
    Keywords: Offshore platform safety; fire safety engineering; fire prevention; detection and suppression.
    DOI: 10.1504/IJOSM.2026.10078840
     

 

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