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Advanced Studies in Marine Structures—2nd Edition

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A Special Issue of Journal of Marine Science and Engineering (ISSN 2077-1312) belonging to the section "Ocean Engineering".

Deadline for manuscript submissions: 1 January 2027 | Viewed by 750

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Editor

Prof. Dr. Jianhua Zhang

E-Mail Website
Guest Editor

College of Aerospace Engineering, Harbin Engineering University, Harbin 150001, China
Interests: offshore wind turbines; offshore floating photovoltaic systems; structural dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Marine Science and Engineering is delighted to introduce this Special Issue, “Advanced Studies in Marine Structures—2nd Edition”, which will build on the success of the previous edition.

Marine structures play a critical role in global economic growth, the energy transition and environmental sustainability. As demands for offshore renewable energy, deep-sea exploration and resilient coastal infrastructures intensify, advancing the design, analysis and maintenance of marine systems has become imperative.

This Special Issue addresses pressing challenges such as climate change-induced extreme weather, aging infrastructure and the need for eco-friendly solutions in marine engineering. By focusing on innovations in structural integrity, material science and computational modeling, it aims to enhance the safety, efficiency and longevity of ships, offshore platforms, subsea pipelines and renewable energy systems (e.g., wind turbines, wave energy converters). The integration of emerging technologies—such as artificial intelligence, digital twins and additive manufacturing—into marine structural studies offers transformative potential for predictive maintenance, real-time monitoring and adaptive design. Furthermore, research on sustainable materials and life-cycle assessment aligns with global decarbonization goals, reducing environmental impacts while ensuring cost-effectiveness.

This Special Issue aims to bridge academia and industry, fostering solutions for next-generation marine systems in a rapidly evolving maritime sector.

Prof. Dr. Jianhua Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

marine structures
offshore engineering
structural integrity
hydrodynamic
sustainable design
computational mechanics
risk assessment
renewable energy systems
additive manufacturing in marine applications
AI-driven simulations
digital twins

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Published Papers (2 papers)

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Research

37 pages, 7713 KB

Open AccessArticle

Gray Langurs Optimizer-Optimized Feature Mode Decomposition for Adaptive Denoising of Multi-Source Monitoring Data from Floating Offshore Wind Turbines

by Xiang Ji, Lei Han and Yan Zhang

J. Mar. Sci. Eng. 2026, 14(17), 1627; https://doi.org/10.3390/jmse14171627 - 2 Sep 2026

Viewed by 193

Abstract

Feature Mode Decomposition (FMD) adaptively decomposes signals into band-limited modes through an adaptive finite impulse response (FIR) filter bank optimized via correlated kurtosis (CK) maximization, yet its denoising performance is highly sensitive to four hyperparameters—the number of decomposition modes

n_{m}

, the [...] Read more.

n_{m}

, the filter length L, the CK shift order M, and the characteristic-period scaling

T_{scale}

—whose manual tuning is impractical for multi-channel floating offshore wind turbine monitoring deployments. We propose GLO-FMD, an adaptive denoising framework coupling the Gray Langurs Optimizer (GLO) with FMD. GLO autonomously optimizes the FMD parameters, thereby aligning the CK objective with structural modal periods rather than impulsive fault periods. Although the search space spans

(n_{m}, L, M, T_{scale})

, the CK shift order M is fixed at 2 and

T_{scale}

is estimated automatically from the dominant autocorrelation peak; consequently, only

(n_{m}, L)

are actively optimized. The optimized FMD decomposes multi-axis tower-base signals into band-limited modes through iterative CK-maximizing FIR filter optimization; each mode identifies a dominant periodic component, and the original signal is zero-phase band-pass filtered around the identified frequencies to preserve physical phase during reconstruction. Validation employs (i) semi-synthetic signals reproducing the measured tower-base structure (a smooth

0.15

Hz structural mode plus an impulse-excited

3.77

Hz resonance) with exactly known ground truth—a best-case benchmark by construction that isolates denoising capability from reference uncertainty—and (ii) real strapdown inertial sensor data acquired at 8 Hz from the tower-base interface of a floating offshore wind turbine at an operational site in Chinese coastal waters, over a six-day measurement campaign (18–23 April 2023). Six kinematic channels spanning triaxial acceleration (north, up, east) and triaxial velocity (north, up, east) are analyzed, with 200-s (1600-sample) continuous windows extracted for algorithmic evaluation. On the semi-synthetic data, GLO-FMD achieves a

9.6

–

10.2

dB SNR improvement over default wavelet thresholding against the known ground truth, and the GLO optimization is essential for reliability—the default FMD configuration is unstable across noise realizations, whereas the optimized parameters recover the clean components consistently. GLO-FMD also achieves pseudo-reference-relative SNR gains of 5.3–7.8 dB over default wavelet thresholding across all six real-data channels. Bootstrap resampling over 12 independent segments confirms statistical significance (

p < 0.001

, Cohen’s

d > 8

), and a no-reference smoothness index provides complementary evaluation independent of the pseudo-reference assumption. Multi-day consistency analysis yields coefficients of variation below 5%, demonstrating short-term consistency across the environmental conditions represented in the six-day dataset. The online denoising stage requires approximately 1.5 s per channel, supporting potential deployment on edge-computing hardware at the turbine controller level. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Structures—2nd Edition)

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33 pages, 2403 KB

Open AccessArticle

Multi-Level Kinematic Spectral Response of a Floating Offshore Wind Turbine: Baseline Analysis Using Field Measurement Data

by Xiang Ji, Lei Han and Yan Zhang

J. Mar. Sci. Eng. 2026, 14(17), 1624; https://doi.org/10.3390/jmse14171624 - 2 Sep 2026

Viewed by 330

Abstract

Floating offshore wind turbines (FOWTs) experience coupled aero–hydro–servo-elastic excitations that produce structurally distinct kinematic responses at different measurement heights. While field monitoring campaigns increasingly deploy multi-level inertial sensors, the quantitative spectral partitioning of response energy across measurement levels and its relationship to operational and environmental conditions remain poorly characterised for operational FOWTs. This study presents a systematic multi-level spectral decomposition of operational FOWT structural response using synchronised tower-base and nacelle strapdown inertial measurements acquired at 8 Hz over a six-day campaign (18–23 April 2023) at a semi-submersible FOWT in Chinese coastal waters. Six kinematic channels—three translational acceleration components and three translational velocity components—from each sensor are decomposed into four physically defined frequency bands: drift (0.005–0.05 Hz), wave (0.05–0.30 Hz), structural (0.30–0.50 Hz), and rotor (0.50–0.80 Hz). Three derived scalar metrics—band energy ratio (BER), Wave-to-Structural Dominance Ratio (WSDR), and Structural Amplification Factor (SAF)—are defined, with their complete computation specifications and parameter sensitivity analysis provided to ensure reproducibility. Across 36 ten-minute windows spanning diverse conditions (mean wind 4.2–12.1 m/s,

H_{s}

0.8–3.1 m), results reveal a pronounced and consistent spectral separation: the tower base is strongly wave-dominated (

{BER}_{wave}

= 75.9%, coefficient of variation CV = 15.0% across days), whereas the nacelle exhibits substantially elevated structural-band energy (

{BER}_{struct}

= 10.3%, 4.72-fold amplification relative to tower base, 95% CI [3.63, 5.81]) and rotor-band energy (11.8%, 3.77-fold amplification). The WSDR at the tower base (mean 200.9, 95% CI [101.4, 300.4]) exceeds that at the nacelle (mean 48.4, 95% CI [13.1, 83.7]) by a factor of 4.1×. One-way analysis of variance (ANOVA) reveals that nacelle structural-band BER is significantly modulated by SCADA operational regime (

F = 5.20

p = 0.024

η^{2} = 0.16

) and by significant wave height (

p = 0.031

), while tower-base wave-band BER is primarily driven by

H_{s}

(

p = 0.018

). Comparison with baseline features—root-mean-square acceleration, spectral peak frequency, and traditional broad-band energy ratio—demonstrates that the band-resolved BER provides finer discrimination between excitation mechanisms than aggregate metrics. Importantly, no structural damage events occurred during the monitoring period; therefore, the reported stability of these features is interpreted as a baseline characterisation under normal operational conditions, which could support future anomaly detection efforts but does not constitute validation of damage detection capability. A comprehensive limitations assessment is provided, covering single-turbine validation, frequency-band sensitivity, regime sample imbalance, and generalisability constraints. Full article