current projects

Current and tidal Foil Turbines

Ph.D. Project

There is currently a huge demand for clean, reliable and sustainable energy; flowing fluid can potentially generate this clean power. Both industrial and academic groups worldwide are working to improve traditional offshore wind, tidal and oceanic turbines.

The aim of this research is to evaluate the performance and increase the efficiency of current- and tidal wave energy harvesters with a specific focus on a flapping foil hydrokinetic turbine in swing arm mode. The more commonly used rotary turbines are efficient during continuous smooth attached flow over the blades. However, oscillating foil turbines generate a very large leading-edge vortex that separates the flow early causing high instantaneous forces. As a result, they create higher lift force than conventional turbines. This type of design is used by bees to generate lift. Bees have small wings compared to their body. It is also used by fish who need extra propulsion- or maneuvering power. Furthermore, foil turbines can be placed in shallower areas and are less hostile to marine wildlife.

Wave Energy Harvesting

Ph.D. Project

The project aims to evaluate the performance and power extraction of the wave energy converter (WaveStar) at various sea sates. Computational fluid dynamics simulation (CFD) is used to assess the device performance using the OpenFOAM open source CFD toolbox. Further studies are implemented to compare between different CFD techniques to predict the dynamic behavior of the device. The model will be extended to study design optimization of wave energy farm array configuration and mooring system for coupled and uncoupled wave energy converters for energy efficiency maximization.

Increased performance of high speed craft

M.Sc. & B.E. Project

High Speed Craft have received widespread applications in recreational use as well as in military and search and rescue operations. Although it is a matured technology, there is a lack of experimental data for variations in hull forms and efforts are continuously being made to improve their efficiency, safety and maximum speed. This project aims to evaluate spray deflection technologies - spray rails and spray deflectors - in model scale using towing tank testing.

Using small tapered steps, the flow is detached and redirected to reduce the spray area, over all resistance reduction has been found in calm water but current wave tests are inconclusive.

Offshore Seaweed Farming

Group wide effort

Food shortage is one of the biggest challenges facing humanity in the 21st century. Currently, 1 in 9 people (821 million) are malnourished. By 2050 the world's population is expected to reach 9 billion. To lift people out of poverty and into the middle class, the availability of affordable, healthy and sustainable food is paramount. Thus the pressure to increase farmland or crop yields is enormous. The UN is calling for an increase in food production of 70% by 2050.

This projects aims to design and develop a sustainable, offshore farming system. There is a market need for bigger and better ocean farming systems to help mitigate the climate crisis as well as provide an alternative food source for humans. This project is run in conjunction with the University of Southampton.

Added Mass Effects

M.E. Project

Marine growth can have significant effects on the motions of large floating platforms. For seaweed farms, the mass added by additional growth represents a large percentage change in total mass. The effect of this change can have repercussions for the feasibility and reliability of small floating seaweed farms in terms of seakeeping and power generation.

This project will further investigate and model the response of moored ocean farm arrays in various configurations over a harvest or growth cycle. The work involves modelling of the system dynamics and experimental validation.

Wave energy Harvesting

M.E. Project

Developing offshore ocean farms has huge potential. However, providing power to continuously monitor and operate a remote, unmanned or autonomous offshore farm, is a challenge. This project, working towards the goal of realizing renewable, sustainable and self-sufficient ocean farming practices, will investigate the use of wave energy harvesting to provide power. The aim is to determine the recoverable power from a moored array configuration over a range of conditions, through the application of multiple wave energy harvesters to exploit the loads on the mooring and interconnecting lines. The project is anticipated to involve modelling of the system dynamics and experimental validation.

2050 Sustainable food vision (website)

Group wide project

We are partaking in the Rockefeller Foundation: Food System Vision Prize 2050

Autonomous Aquaculture: Changing how seaweed is grown and eaten.

We want to provide food to and change the eating habits of hungry crowds of New Yorkers by autonomously growing seaweed in the local offshore area.

2019/2018 Senior Design Project (Website)

B.E. Project

Stevens Senior Design teams have worked to develop working farm prototypes for two years. Currently, the team is in the design phase with prototype testing to start in the spring of 2020.

2018/2019 Senior Design Project (WebsitE)

Finished 2019

A proof of concept a prototype has been developed; containing a data monitoring system that will measure temperature, pressure, and weight of seaweed to ensure ideal growing conditions. Initial testing in the Davidson Laboratory Towing Tank took place during the spring of 2019.

FPSO Downtime ANALYSIS

Ph.D. Project

Despite continuous developments in the use of alternative sources of energy such as solar, nuclear power, biomass, etc., hydrocarbon is still the most important fuel in today’s world.  The Organization for Economic Cooperation Development (OECD) predicts continuous increase in the value of hydrocarbons in the coming years, and this implies that Floating Production Storage and Offloading vessel (FPSO’s) operations in ultra deepwater locations will continue to be relevant for a very long time. The major challenge faced by FPSO operations is extreme weather conditions which may prevent offloading operations and sometimes cause the FPSO to cut back on production. 

This research aims to analyze the effects of weather downtime on FPSO operations by using existing global wave data, evaluate motions of FPSOs and shuttle tankers under scatter environment, improve FPSO operations by providing workability and downtime for FPSO based on local wave statistics. The ultimate goal of this research is to provide improvements to FPSO design that will eliminate or minimize the effect of waiting on weather to  ensure continuous production and save cost.

Design OF Autonomous Sailing Vessels

M.E. Project

The design of autonomous vessels is associated with unique opportunities and challenges. As naval architects, we rely on 10,000+ years of collective experience in the design of manned boats. However, with the emergence of unmanned surface vessels, there may be benefits in modifying early design phase procedures, as these vessels will have fundamentally different needs. 

There is a growing need for ocean based sensing in remote areas not accessible to most researches. With the opening of arctic waters and the changing weather patterns all over the globe, reaching new destinations to carry out research observations has become vital. The realization of an energy independent autonomous surface vessel, to act as a mobile research tool, has been targeted by the Maritime Robotics Laboratory at KTH.

Our lab in conjunction with the Design SPACE lab at Stevens Institute of Technology presents a new approach to system optimization aimed at autonomous vessels, using the Maribot Vane small-scale autonomous surface vessel as a case study, with the aim of (together with KTH) devlop a second generation of the vessel. A multidisciplinary, multiobjective, reliability-based design optimization (RBDO) problem was formulated for the conceptual design of the Maribot Vane, seeking to minimize the system cost and the probability of failure under anticipated operational conditions. The results will inform designers of such a ship platform about the trade-offs between cost and reliability, as well as the optimal selection of main particulars for the detailed design stage and finalization of the hull design.

ship health monitoring

B.E. Project

Every year a large number of ships sink due to structural failure. Ship Health Monitoring is a research project that aims to significantly reduce this number. Big data, AI and Machine Learning have opened up new possibilities for Smart applications in other fields such as Smart cars, Smart cities and Smart appliances. Shipping must soon follow.

By monitoring the structural health of a ship, fatigue cracks can be detected at a very early stage of their initiation and failure can be avoided. In order to cover a large structure such as a ship, a considerable number of sensors are needed and a long term monitoring of the structure is required. Accordingly, the interpretation of such a big dataset to uncover information about the health condition of the ship structure is challenging. For this, basic sensing technologies are used to allow focus on the development of state-of-the-art data analysis techniques.

This project is carried out together with Dr Belanger in the School of Business and Dr Williams in Mechanical Engineering Department.

Numerical modeling is combined with testing in the Davidson Laboratory towing tank to develop a Smart Personal Flotation Device (Life Vest.) The Smart PFD project has involved 12 students over the past three years. Currently, PFD models on the market suffer from accidental activation and perceived bulkiness causing users not to wear their vests. Current development focuses on optimizing staggering deployment of the individual air bladders with the goal to reduce time to “face up” and time to surface. A radar reflector is also being added to increase the chances of location. This project is carried out in collaboration with the Wearable Robotics Lab.

Big Data analytics in shipping

Finished 2018

The evolution towards “smart” vehicles, including ships, is well under way. It is driven by the promise of many valuable applications, but made possible, in large part, by the revolutions in technologies such as Big Data, Machine Learning, and Artificial Intelligence. We are a few decades into the production application of Big Data in industries such as Telecom, Finance, and Media; and nearly a decade into advanced development in areas such as Healthcare, Autonomous Vehicles, and Smart Grids. Shipping involves some unique challenges in networking, analytics, and implementation, but some of the fundamental tasks will be similar. We focus on the issue of Big Data, and on the essential role of understanding the data that exists, the data that will be needed, and, through careful metadata creation and management, the fitness of that data in terms of properties such as: quality, latency, structure, volume/velocity, provenance, and others. This project is carried out in collaboration with the School of Business; David Belanger.

Dissipative Potential Flow Modeling

Finished 2015

Steady ship motion in calm water is a classical problem in ship hydrodynamics. Potential flow modelling is a common method to predict the wave making resistance of ships. In its conventional form, the flow is assumed to be free from damping due to the inviscid assumption of potential flow. It has been argued by the founding fathers of ship resistance predictions that damping plays an important role in determining the wave making resistance. Despite this, viscosity is often omitted from present wave making resistance prediction methods. It is known that damping plays an important role in the formation of the wave pattern and it is therefore of interest to determine the effect on the resistance prediction by including a damping factor in a previously undampened model.