Three different hull forms, monohull, catamaran, and SWATH (small waterplane area twin hull), of similar displacement and principal dimensions are designed for the systematic comparisons of their seakeeping performances in sea state 3 and 4, which is considered as the typical operational limit of CTVs (crew transfer vessels). The RAOs (response amplitude operators) of the three vessels for various wave headings were calculated from potential theory and BEM (boundary element method) in the frequency domain. In parallel, the time-domain simulations including viscous drag effects were also conducted for their motions in sea state 3 and 4 by using an independent in-house program, CHARM3D, which compared well against the frequency-domain results. When comparing seakeeping performance in sea state 3 and 4, the SWATH vessel outperforms both the catamaran and the monohull. The SWATH's 6DOF motion amplitudes are about half of those of catamaran and monohull since its heave-roll-pitch natural frequencies are much lower than the peak frequencies of incident waves in sea state 3 and 4. Therefore, we found that a SWATH can be operated up to a higher sea state 4 while a monohull and a catamaran can be used up to sea state 3. Our simulations also examined the three CTVs positioned about 10 m behind a monopile wind turbine structure of 10 m-diameter, by using our in-house two-body CHARM3D hydrodynamic interaction simulation programs. The two-body simulation results show minor shield effects in motions when operating behind the monopile. Again, SWATH shows the best seakeeping performance compared to other vessels.
Liquid sloshing in partially filled tanks induces time-varying loads and center-of-gravity (CG) shifts that can degrade stability and control. We present an experiment-driven framework that integrates laboratory measurements, computational fluid dynamics (CFD), and machine learning (ML) to accurately quantify and predict CG dynamics under sloshing excitation. The framework (i) reconstructs CG trajectories from synchronized load-cell and pressure measurements, (ii) validates a volume-of-fluid (VOF) OpenFOAM model against experiments using CG-centric metrics-peak-to-peak (P2P) amplitude, root-mean-square (RMS), and dominant frequency-with confidence intervals, and (iii) trains data-driven surrogate models that generalize within this jointly validated domain. To ensure transparent benchmarking, CFD validation is performed at water depths of 2, 4, and 6 cm (i.e., D/L approximate to {0.033, 0.067, 0.100}). Surrogate modeling (ML) is supported by a broader dataset covering depths from 3.0 to 7.2 cm, enabling interpolation across intermediate fill levels and testing generalization. Direct experiment-CFD overlays confirm agreement within 5-10% in RMS and peak-to-peak metrics. The ML surrogates, particularly the LSTM sequence model (two stacked layers, horizon H = 1000), achieve mean absolute errors around 1-2.5 mm across unseen fill levels, while simpler models (linear regression (LR), random forest (RF), and gradient boosting (GB)) remain competitive only in low-variability regimes. Results demonstrate that CG trajectory prediction and frequency content can be captured with high fidelity across fill levels, with ML surrogates providing substantial speedups for design-time trade studies. The surrogate's limits relative to CFD are clarified, and representative overlays (experiment vs. CFD vs. surrogate) provide direct visual and quantitative comparison, enhancing clarity, transparency, and reproducibility.
Two-planar tubular DKT-joints are widely used in offshore jacket structures, where accurate assessment of stress concentration factors (SCFs) is vital for evaluating fatigue performance. However, SCFs in DKT-joints with internal ring stiffeners have not been thoroughly studied, and no specific design equations currently exist for determining SCFs under axial loading which is typically the dominant load in these joints. In practice, engineers often rely on SCF data from uniplanar KT-joints to estimate values for multi-planar joints. This approach, however, overlooks the influence of out-of-plane braces, potentially resulting in significant inaccuracies. This study addresses this gap by analyzing SCFs on the chord side of ring-stiffened two-planar tubular DKT-joints under axial brace loading. The investigation is based on 118 finite element (FE) models, validated through both experimental data and existing numerical results. A comprehensive parametric FE study was conducted, followed by nonlinear regression analyses to derive four new equations that accurately predict SCFs. These proposed formulas offer a reliable tool for fatigue design applications.
This study experimentally evaluated the serviceability of multi-unit floating structures designed for marine city applications and verified the effect of wave-dissipating modules on wave motion reduction. Serviceability indicators were defined as vertical acceleration and inclination (pitch and roll), reflecting both the stable operation of topside facilities and human activity and discomfort (motion sickness). The experimental results showed that the installation of wave-dissipating modules reduced vertical RMS accelerations by approximately 3439% and pitch/roll inclinations by 21-42%. These improvements were consistently observed not only under 1-year return period wave conditions but also under extreme 100-year return period waves, thereby confirming the reliability of the proposed design. This study demonstrates that wave-dissipating modules are an effective design strategy to enhance the habitability and serviceability of floating infrastructures for marine cities. Furthermore, it highlights the need to extend conventional serviceability evaluation frameworks, which have been focused on industrial facilities, to ergonomics-based criteria that reflect the dual requirements of technical stability and humanoriented serviceability in marine city environments. These findings are expected to make an important contribution to the development of next-generation offshore infrastructures, such as marine cities and offshore renewable energy hubs, where both technical stability and human-oriented serviceability must be simultaneously ensured.
This research focuses on predicting the speed of ocean currents in the Sunda Strait by employing a Long Short-Term Memory (LSTM) model based on historical data. The approach includes data preprocessing, normalization of features using MinMaxScaler, segmentation of the data into training and testing sets, and the development of layered LSTM model architecture. The dataset comprises longitude, latitude, current velocity, and time information from 2022 to 2024. The findings indicate that the LSTM model can predict ocean current speeds with a Root Mean Squared Error (RMSE) of 13.66 cm/s, a mean absolute error (MAE) of 9.06 cm/s, and a determination coefficient (R2) of 0.87. The demonstration illustrated the typical design of ocean current speed fluctuations; however, forecasting unusual variations remains challenging. In summary, the LSTM model represents a practical approach for predicting ocean currents based on historical data, aiming to enhance prediction accuracy. This model will support navigation efforts and marine resource management in the Sunda Strait region.
