Ship Handling & Manoeuvring
Nomoto modelling, heading response, turning behaviour, simulator development, and evaluation of vessel control characteristics.
About the Laboratory
The Ship Safety and Ship Handling Simulator Laboratory supports research and education in ship manoeuvring, marine accident analysis, navigational risk, passenger-vessel safety, and computational approaches to safer marine-system design. The laboratory uses modelling, simulation, numerical analysis, and data-driven methods to investigate how vessels can be designed and operated more safely.
An emerging research direction explores how ship geometry — including superstructure form — can reduce wind-induced aerodynamic loads, heeling moments, and vulnerability to capsizing in severe weather. This research-stage work uses theoretical and computational methods to build design knowledge and screening tools, with future expansion toward higher-fidelity CFD, physical experiments, and industry collaboration.
The laboratory also provides a platform for student design research. International competition results highlight academic design capability, sustained student engagement, and a growing portfolio of computational marine design. These achievements provide a strong foundation for future prototype development, validation studies, and broader professional application.
Research Themes
Maritime Safety & Risk
Collision probability, grounding risk, bridge-passing performance, navigational-route assessment, and quantitative risk analysis.
Accident Investigation
Systematic examination of marine casualties, human and organizational factors, accident causation, and safety recommendations.
Passenger & Ferry Safety
Safe and affordable ferry design, evacuation, survivability, accessibility, passenger comfort, and public-health considerations.
Computational Design & Optimization
Research-stage design exploration using numerical solvers, surrogate models, machine learning, adaptive sampling, and optimization under engineering constraints.
Submarine & Underwater Safety
Submarine accidents, failure mechanisms, survivability, emergency response, and lessons for underwater systems.
Current Research Directions
Ship Safety · Emerging Research Direction
Wind-Resilient Ship Superstructure Design
Exploring whether superstructure geometry can reduce wind forces and wind-induced heeling, thereby improving a vessel's theoretical safety margin in severe weather.
Active research · theoretical & computationalStorm-wind safety problem
Baseline ship & superstructure
Alternative geometries
Wind load & stability evaluation
AI-assisted design exploration
The central question is how changes in above-water geometry may alter aerodynamic loading, heeling moment, and stability response, and how those relationships can guide safer superstructure concepts.
- Targets safer research-stage ship concepts designed to improve resilience in more severe wind environments.
- Links aerodynamic loading with intact-stability and capsizing considerations.
- Provides a future pathway toward surrogate modelling and AI-assisted geometry search.
Research progress: the current work has established the safety problem, computational questions, and design methodology. The next development stages include higher-fidelity CFD, experimental studies, and collaboration toward practical marine design applications.
AI-Assisted Marine Design · 2026
ANN-Based Hydrodynamic Shape Optimization
A resource-efficient surrogate-model framework for research-stage hydrofoil-section design using numerical data, artificial neural networks, adaptive sampling, and constrained optimization.
First Prize · IHS Mandles Prize 2026NACA0012 baseline
Hicks–Henne deformation
XFOIL samples
ANN + adaptive sampling
Constrained SLSQP search
The study compares one-shot Sobol sampling with optimization-based adaptive sampling under the same final sample budget. The adaptive strategy concentrates new evaluations near predicted optimum regions rather than distributing them uniformly.
- Objective: maximize foil lift while constraining drag and sectional area.
- Designed as an economical research-stage screening approach for resource-conscious computational studies.
- Provides a pathway toward higher-fidelity CFD, cavitation and structural assessment, and experimental studies.
Development pathway: the results establish a computational proof-of-concept for a single baseline foil and fixed flow condition using XFOIL. Future stages can extend the framework through higher-fidelity CFD, cavitation and structural assessment, and experimental studies for craft-level design.
Unifying direction. These projects reflect a broader research interest in safer and smarter ocean-engineering systems: combining engineering physics, simulation, and data-driven tools to explore design spaces more efficiently. AI supports prediction, screening, and optimization, while future physical validation strengthens the pathway toward practical engineering application.
Ship-Handling Simulation and AI-Based Collision Avoidance
Interactive Simulation Project
Google Maps-Based Ship-Handling Simulator
Figure 3. GUI of the Marine Traffic Simulator
The simulator combines the Google Maps API with Nomoto’s K–T manoeuvring model to visualize and control a ship’s planar motion in real time.
- Uses three linked JavaScript modules: trajectory, Google Maps, and control codes
- Solves the Nomoto K–T response using the fourth-order Runge–Kutta method
- Accepts ship dimensions, speed, initial heading, rudder command, and nondimensional K′ and T′
- Displays animated position, instantaneous heading, rate of turn, and live response graphs
Artificial Intelligence Research
Collision Avoidance for Autonomous Ships
The collision-avoidance work links algorithmic decision support for autonomous ships with evidence from inland-waterway collision characteristics in Bangladesh.
- Compares route-planning and collision-avoidance approaches for autonomous navigation
- Uses simulator-based scenarios to examine encounter handling and decision support
- The 2010 accident study identifies collision as a major inland-waterway safety problem
- Cargo vessels were frequently involved, and inadequate lighting, reporting, and collision-avoidance devices were key concerns
Featured Research
Simulation
Ship Handling Simulation
A marine traffic simulator on Google Maps using the Nomoto K–T model and real-time geographic visualization.
Read representative publicationCollision Avoidance
Collision Avoidance
Collision characteristics of marine vehicles in Bangladesh, providing an evidence base for collision-prevention measures.
Read representative publicationSafety Science
Marine Accident Investigation
A systems-theoretic analysis of inland passenger-ship safety and the interactions among regulatory, organizational, and operational controls.
Read representative publicationPassenger Safety
Passenger Ship Safety
A model for airborne SARS-CoV-2 transmission on the upper deck of a passenger ship and the implications for onboard prevention.
Read representative publicationEnvironment
Environmental Sustainability
A sustainable passenger-ship waste-management model incorporating biogas-composting and plastic segregation.
Read representative publicationArtificial Intelligence
AI-Assisted Marine Engineering Design
Data-driven modelling for marine engineering, including ship-design optimization, hydrodynamic surrogate models, repair-time estimation, and autonomous-navigation decision support.
Read representative publication2026 Mandles Prize: International Recognition in AI-Assisted Design
International Hydrofoil Society
First Prize — 2026 Mandles Prize for Hydrofoil Excellence
US$2,500 First Prize
Selected for the BUET paper “Efficient Hydrodynamic Shape Optimization Using Artificial Neural Network-Based Surrogate Model with Adaptive Sampling Strategy.”
The recognition highlights student competence in hydrofoil engineering, computational design, surrogate modelling, and optimization, and marks a strong research-stage achievement with clear potential for further development.
What the research demonstrated
The study parameterized a NACA0012 foil with eight Hicks–Henne design variables, generated numerical lift/drag data with XFOIL, trained an ANN surrogate, and used SLSQP to search for improved foil geometry under drag and area constraints.
173final samples in each comparison
58.68%numerical lift increase with adaptive sampling
1×10⁶Reynolds number used in the study
- Adaptive sampling placed additional numerical evaluations near predicted optimum regions.
- The optimized result maintained the study's drag and sectional-area constraints.
- The framework provides efficient preliminary screening and a focused starting point for higher-fidelity validation.
Research context: the reported numerical improvement applies to the study conditions, including a 5° angle of attack. This result provides a promising basis for future higher-fidelity analysis, experimental verification, and broader hydrofoil design studies.
International Student Design Recognition: WFSA
Student Design for Safer Ferries
Students associated with the laboratory have achieved sustained recognition in the Worldwide Ferry Safety Association International Student Design Competition for a Safe and Affordable Ferry. These are academic design projects that integrate ship design, stability and safety assessment, manoeuvring analysis, sustainable propulsion, and context-specific transport needs.
Five Recognized Entries · 2021–2026
BUET teams received Third Place in 2021, Honourable Mention in 2023, Second Place in 2024, First Place (Championship) in 2025, and Third Place in 2026.
2021
Third Place
Cisne Rosa Express, a 300-passenger Ro-Pax ferry for the Amazon River, Brazil.
2023
Honourable Mention
Green Falcon Express, a 100-passenger electric ferry for the Pasig River, Philippines.
2024
Second Place
Black Pearl, a 200-passenger Ro-Pax ferry for the Niger River route in Nigeria.
2025
First Place
Najia Spirit, a 200-passenger electric ferry for the Lagos Inland Waterway, Nigeria.
2026
Third Place
A 200-passenger ferry for the Niger River route between Lokoja and Onitsha, Nigeria.
Lab In-Charge
Dr. Zobair Ibn Awal
ডঃ জোবায়ের ইবনে আওয়াল
Exploring safer, smarter, and bio-inspired marine systems through engineering, simulation, and artificial intelligence
Professor
Department of Naval Architecture & Marine Engineering (NAME)
Bangladesh University of Engineering & Technology (BUET)
Dhaka 1000, Bangladesh