Current service and installation activities for offshore wind turbines are carried out by jack-up vessels, which eliminate wave disturbances to a large extent. The use of these vessels imposes several disadvantages, including high operational costs and the limitation of operating in restricted water depths. An alternative is a crane mounted on a floating vessel (monohull), which is quicker in operation and multi-deployable. With these vessels, however, a motion compensator is needed to eliminate most of the hydrodynamic disturbances. The purpose of this graduation assignment is to develop a concept design for a passive motion compensator (PMC) to isolate the vessel motion from the payload.

This goal is achieved by the implementation of a multi-objective optimization algorithm based on genetic programming (GP) that constructs a two-dimensional PMC based on the principle of quasi-zero stiffness. The GP employs a set of genetic operators to explore the design space in terms of topology and design variables, which effectively mimics Darwin’s Theory of Evolution. The better a mechanism performs, the higher the fitness, and thus the higher the chance this design appear in future generations.

Each design is a composition of cylinders and nodes that are assembled into a mechanism. During the fitness evaluation, all mechanisms are examined by a nonlinear finite element method to extract the force-displacement relations. The arc-length control method is used due to the large displacements and rotations of the members in the mechanism, whereby both the nodal displacement vector and external load vector are varied to follow an equilibrium path in an incremental-iterative way. To provide sufficient support to the payload, the mechanism is prestressed, which is applied by using the arc-length procedure as well.

Static and dynamic results show that the GP is suitable for constructing a high-level planar QZS mechanism for the intended application in various sea states. From multiple runs, it can be concluded that the optimizer generally produces designs with the same topology. Dynamic analyses in the time domain show that motions are effectively mitigated in the horizontal and vertical directions for the chosen designs. In addition, analysis in the frequency domain shows that the mechanisms effectively attenuate motions in the frequencies of interest.
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In recent developments in the field of multi-material additive manufacturing, differences in material properties are exploited to create printed shape memory structures, which are referred to as 4D-printed structures. New printing techniques allow for the introduction of prestresses in the specimen during manufacturing. This research focuses on bi-polymer 4D-printed structures, where the transformation process is based on a heat-induced stiffness transition in one of the materials and an initial prestress in the other material. Upon the decrease in stiffness, the prestress is released, causing a bending behaviour. A methodology to find the design of 4D-printed structures is developed, where a finite element model is combined with a density-based topology optimization method to describe the material layout. This modeling approach is verified by a convergence analysis and validated by comparing its numerical results to analytical and published data. The use of topology optimization to design 4D-printed structures is explored by applying the methodology to a variety of design problems. Bi-layer designs are printed with acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU), of which the latter material is prestressed during the Fused Filament Fabrication (FFF) printing process. Tests are performed with these printed samples, with the goal to find the prestress value and to further validate the modeling approach by comparing the numerical results to the experimental results. Once the prestress value is found, topology optimized samples are printed and tested to evaluate their performance.
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