“It has no office. It has no personnel. It has no director. But it exists and it has flourished for 50 years: The Cooperative Research Ships (CRS).” This is the original quote from the editorial in MARIN’s Report magazine in June 2019.

Now, five years later, CRS is still around, and still thriving, with 24 committed member companies working on expanding CRS knowledge by sharing ideas and results. Of course, some things have changed: the COVID pandemic has affected the CRS modus operandi too. Where we used to meet four times per year in person, nowadays we meet only twice per year. However, these face-to-face meetings are appreciated even more, and extra online meetings throughout the year allow for the presentation and discussion of up-to-date results. What has stayed is the shared awareness that it pays off to cooperate on pre-competitive research.

Manoeuvring & seakeeping: New requirements such as Safe Return to Port (SRtP), Minimum Power Requirements (MPR), or Navy operational requirements motivated us to study ship safety at low speed in harsh weather conditions. This multidisciplinary problem involves propulsion, manoeuvring and seakeeping. Using RANS, RANS-BEM and RANS-(U)RANS calculations we created validation data for better predictions of the propeller, rudder and wave drift forces and we derived guidelines on the rudder position relative to the propeller. We also improved the mathematical models of CRS tools and, using these tools, we made guidelines on how to verify ship safety. In the follow-up project we will further develop time domain simulation tools used to assess regulatory compliance and perform dedicated model tests for the validation of wave drift forces’ predictions. In another project, we implemented an advanced forward speed method in the CRS frequency domain seakeeping tool, which gives a better prediction of the wave drift forces.

Data science: The aim of the first “data driven project” was to integrate machine learning and data science into maritime research, achieving notable advancements in predictive analytics, seakeeping, and performance modelling. This project delivered key results, established a repository of resources, and laid the groundwork for continued innovation.

The sequel project focused on federated learning, AI applications in computer vision, and surrogate modelling, with specific examples such as fuel consumption prediction, wave estimation from images, and ship performance optimisation. In the next project we aim to expand our knowledge and experience regarding machine learning optimisation using high fidelity simulation tools such as CFD.

On a final note, after 14 years of inspiring chairmanship, Bas Buchner handed over the CRS helm to Guilhem Gaillarde (Manager Ships). Undoubtedly, his 28 years of experience at MARIN will definitely help CRS in sailing a good course!

Underwater radiated noise: Nowadays, there is great concern regarding the effect of shipping noise on marine life. This has led to the inclusion of underwater radiated noise (URN) in the definition of good environmental status for the EU marine environment, in both IMO guidelines and in Class rules. This is why we invested in further development of computational prediction methods for propeller URN, including sound propagation. We investigated cavitation detection algorithms for onboard monitoring of propeller cavitation noise. More practical knowledge was obtained through case studies in which propeller geometries are designed for both high efficiency and low URN.

Propeller design optimisation: We applied multi-objective optimisation methods to automated propeller design. This design process is characterised by many input parameters (>30) and many design constraints that apply simultaneously in operational conditions. Various optimisation methods were tested, with one Competitive Particle Swarm Method nearly always coming out on top, which is now the default method.

Composite propellers: We investigated the application of composite materials in propulsors, with a focus on composite propeller design using CRS analysis software. This software was enhanced for unsteady conditions; model-scale validation data were collected through cavitation tunnel testing using digital image correlation techniques. We also addressed scaling effects in model testing, the behaviour of composites under extreme loads and cavitation, and other applications such as rudders and stators. In a follow-up project we will use this knowledge to design, test, manufacture, and class-certify a composite propeller of a small naval vessel.