Advanced CFD techniques are improving the accuracy of high-speed craft performance prediction. By applying a modified High-Resolution Interface Capturing method to better model air-water interactions during planing, researchers reduced resistance estimation errors. Validated against experimental data, the approach offers valuable guidance for propulsion design, efficiency optimisation, and next-generation vessel development.

PID-based anti-sway control is improving crane safety and performance during load handling operations. By combining structural analysis, dynamic modelling, and controller optimisation, researchers significantly reduced lateral oscillations and settling times. The approach offers practical insights for safer cargo movement, enhanced operational stability, and smarter automation across industrial lifting environments today.

Advanced simulation is enhancing understanding of ship slamming loads and impact dynamics. Using an incompressible smoothed particle hydrodynamics model with fluid-structure coupling, researchers accurately captured pressure peaks, hydrodynamic forces, and flow behaviour while reducing numerical noise. The methodology offers a powerful tool for predicting structural loads and improving vessel design.

Autonomous navigation is moving closer to commercial reality with a collision-avoidance system designed for large vessels operating in busy waterways. Using a COLREGs-compliant heading-set approach integrated into autopilot controls, the framework safely manages complex multi-ship encounters, enabling reliable route recovery, enhanced safety, and greater confidence in Maritime Autonomous Surface Ship operations.

A new simulation framework is strengthening naval resilience against underwater explosion threats. Combining explicit dynamic modelling, Doubly Asymptotic Approximation, and Underwater Shock Analysis techniques, researchers identified critical blast locations that generate severe whipping responses. The methodology delivers valuable insights into hull integrity, survivability, and risk-informed design for next-generation naval vessels.

Real-time corrosion detection is transforming offshore asset inspection. By combining YOLOv8n object detection, ResNet-50 feature extraction, XGBoost classification, and GAN-generated training data, the system delivers high-speed, accurate identification of corrosion from live video feeds. The approach promises safer operations, reduced inspection costs, and more proactive maintenance of critical infrastructure.

Active fin technology is showing strong potential to improve vessel stability across varying sea conditions. Using advanced hydrodynamic modelling, researchers evaluated how controlled fin movements influence roll and pitch behaviour, finding significant motion reductions and shifts in natural frequencies. The results highlight important design considerations, particularly the varying effectiveness of active fins at different operating speeds.

Advanced simulation capabilities are enhancing confidence in ship performance predictions under realistic sea conditions. A newly developed 3D numerical wave tank captures large-amplitude vessel motions at forward speed, overcoming key modelling challenges and numerical instabilities. Validated against established results, the approach offers a powerful tool for safer, more efficient ship design and analysis.

New advances in hydrodynamic modelling are improving the prediction of added wave resistance across diverse ship types. Using a three-dimensional boundary element method with forward-speed corrections, researchers achieved strong agreement with experimental data. The capability offers valuable insights for energy efficiency, performance optimisation, and more reliable vessel design decisions today.

Deep learning is reshaping computational fluid dynamics by offering mesh-free alternatives to traditional Navier-Stokes solvers. By evaluating CNNs, Physics-Informed Neural Networks, Transfer Learning, and hybrid approaches, the research highlights emerging capabilities to model complex flows, accelerate simulations, and uncover hidden fluid dynamics while reducing computational barriers in high-dimensional engineering applications.

India’s push to expand inland waterway transport is accelerating demand for zero-emission passenger vessels. This overview examines battery-electric and hydrogen fuel-cell technologies, their operational limitations, and the engineering challenges facing Indian shipyards. The findings highlight critical pathways, obstacles, and opportunities in advancing decarbonised inland mobility and sustainable growth today ahead.

Deep learning is advancing autonomous vessel navigation through an enhanced TD3 reinforcement learning framework. By combining epsilon-greedy exploration, Line-of-Sight guidance, and optimized reward design, the approach delivers smoother rudder control and more reliable path following. The findings highlight AI-driven opportunities for safer, more efficient maritime autonomy in complex operating environments.