MsC Internship assignment: "The Mystery of the Green Passenger"
- Branche / Vakgebied
- Research and Development
- Vereiste taal
- Engels, Nederlands
For MARIN Academy we are looking for a student for the following MSc internship/assignment (6-9
The Mystery of the Green Passenger Machine Learning and Physics-Based Models for Quantitative
Analysis and Reduction of Biofouling Impact on Maritime Transport Efficiency
For millennia, maritime navigation has been a cornerstone of human civilization. Oceans continue to
serve as vital arteries for global trade, necessitating the construction, development, and maintenance of
modern vessels. An issue that significantly impacts ship efficiency is biofouling - the accumulation of
marine organisms on a vessel's hull when submerged in water over a period. The initial colonization of
microbes gives rise to a slime layer, eventually facilitating the attachment of larger organisms such as
barnacles, mussels, and seaweeds. The resulting biofilm presents a significant increase in viscous drag
on the vessel, in extreme scenarios culminating in a resistance augmentation of up to 50%. Despite the
magnitude of biofouling's impact, quantitative assessment of the phenomenon remains a challenge.
Establishing an effective model for biofouling is thus of paramount importance to the maritime industry,
promising insights into maintenance schedules, operational profiles, and more. Comprehensive
understanding and prediction of this process could yield substantial reductions in both economic
expenditure and environmental detriment. Traditional physics-based models offer the capability to
forecast biofouling growth and its influence on ship resistance. However, the advent of machine learning
has provided a fresh lens to view this issue. These data-driven models have entered the biofouling
domain recently and have already demonstrated promising results, indicating their potential in
advancing our understanding and management of marine biofouling.
This project aims to develop a physics-based and a machine-learning-based model to study the growth
and added resistance of biofouling on ships. Because both approaches require much investigation and
different skillsets, a dual master’s graduation is proposed.
1. Conduct a comprehensive literature review on biofouling, emphasizing growth modeling and recent
advancements in data-driven methodologies for assessment.
2. Develop a machine-learning model to quantitatively describe and predict the growth of biofouling on
ship hulls and its subsequent impact on ship resistance, leveraging available experimental data sourced
from scholarly literature and potential third-party contributions
3. Construct a physics-based model that can depict the biofouling growth process and its effects on ship
resistance, allowing for comparisons and insights.
4. Validate both the machine learning and physics-based models utilizing available data.
5. Investigate the impact of biofouling on ship emissions and efficiency. Synthesize insights derived from
the models to propose evidence-based recommendations for improving maritime practices.
6. Present a comprehensive review of the findings to the Maritime Research Institute Netherlands
(MARIN), including recommendations and further research avenues.
7. Compile the findings, insights, and recommendations into a comprehensive thesis or journal article to
report on the results of the graduation project."
If you are interested, Harm Jan Kamphof, Researcher can tell you more about the
You can apply for this internship through the site of MARIN