Student Theses

Since 2008, the InVentus team has been developing and operating a headwind vehicle. This is a vehicle that uses wind turbines exclusively as its power source for propulsion. Together with other student teams from around the world, InVentus participates in “Racing Aeolus,” an international competition held annually in August on the coast of the Netherlands near the city of Den Helder.

In addition to the main mechanical turbine, the InVentus team plans to install a second, smaller turbine at the front of the vehicle. Previous research has shown that the optimal hub height of the front turbine depends on the wind direction. The goal of this thesis is to redesign the yaw system and the mast so that the height of the front turbine can be adjusted. At the same time, the design should allow for quick disassembly of the turbine so that the vehicle can be switched between configurations with one or both turbines. To this end, requirements will first be gathered, and potential solutions will be discussed and evaluated in order to develop a concept. The concept will then be implemented in CAD and sized.

• Literature review on technology roadmapping and future long-range aircraft development
• Identification of relevant future aircraft technologies and associated capability needs
• Assessment of future education, skills, and workforce requirements for aircraft development
• Gap analysis between current aerospace education/workforce profiles and future requirements
• Development of a roadmap linking technology maturation with required competences and training needs
• Formulation of recommendations for education and workforce development in support of future aircraft programmes
• Presentation and documentation of interim and final results

• Literature review on rescue and flight termination systems
• Definition of critical mission points
• Assessment of the feasibility of a rescue system
• Analysis of the required safety zone
• Development and evaluation of a safety concept
• Documentation and presentation of interim and final results

Auxetic structures are characterized by a negative transverse contraction coefficient and exhibit exceptional mechanical properties such as increased energy absorption and dissipation, improved damage resistance, and adaptable stiffness behavior. While auxetic lattice structures have been studied predominantly in isotropic materials, their combination with anisotropic fiber composites has not yet been sufficiently researched.
The aim of this work is to design, manufacture, and experimentally investigate an auxetic lattice structure made of a fiber composite material. These lattice structures are to be placed in the context of engine suspension for rear-mounted engines in novel aircraft designs. The focus is on analyzing the mechanical behavior under quasi-static loading and the vibration characteristics of the structure in order to evaluate the potential of auxetic FRP lattices for lightweight design.

The aim of this work is to investigate the applicability of the SimTex FE solver, a solver optimized for contact-intensive, multi-fiber-based simulation models, to the numerical simulation of the coreless filament winding manufacturing process, as well as a downstream expansion-based modeling strategy to consider fiber bundle deformation. Within the scope of the thesis, a Python-based optimization workflow with pre- and post-processors for modeling and evaluation will be expanded and used as the basis for parameter studies and parameter calibration using small reference structures. The results will then be analyzed with regard to the limitations of the modeling approach, the computation time, and deviations from geometric measurement data of corresponding measurement of test specimens.

Work Packages:
- Literature review and induction (Coreless filament winding (CFW), textile submesoscopic multifilament modeling strategies)
- Investigation of the suitability of the SimTex solver for the problem
- Advancement of a pre- and postprocessor for modeling and calibration
- Investigation of parameters affecting the modeling results and calibration
- Validation of the geometrical results against existing experimental data
- Documentation and presentation of the results

Floating offshore wind turbines will allow the exploitation of the wind energy resource even in deep waters. Currently, there are still significant open challenges to reduce the cost associated with floating wind turbines and allow the commercialization of the technology. Among them, improved methodologies are required to correctly estimate the loading on the mooring system used for station keeping of the machines. In fact, the mooring lines are currently designed following standard practices from the Oil and Gas industry, leading to significant overestimation and costs. To reduce uncertainty in the design process and reduce costs, improved knowledge on the loading and fatigue are required; however, numerical models are usually computationally expensive, as a significant number of operating conditions and system designs need to be tested to identify an optimal solution. In this framework, machine learning methods can be exploited to develop a model for predicting mooring line fatigue, without the need for computationally expensive simulations. The model can then be leveraged to optimize the design by comparing large numbers of technical solutions at a fraction of the cost.

Objectives
1. Adapt current machine learning python libraries for the assessment of fatigue loading
2. Test different algorithms identified in the literature (linear regression, graph based neural networks, etc) to assess the models that provide the best performance

Recommended workflow
1 Review of current design standards and workflows for design of mooring lines in floating wind
The first task will involve reviewing existing literature concerning the design of mooring lines for floating wind turbines. The student will be guided in this taks, learning about how to perform a critical review of the literature following scientific and technical standards exploiting available software and databases for literature review. This part of the work will leverage also previous work from other students, limiting the required effort and time expense.
2 Adaptation of available machine learning models for fatigue prediction for mooring lines
Currently multiple machine learning models have been proposed in the literature to tackle a vast variety of problems. Such models, developed by software engineers, have been mostly structured into open-access python libraries, which can be freely used and adapted by any user. In this step, the student will learn about one of this libraries (possibly pytorch) and apply it for the prediction of fatigue loading.

3 Assessment of different machine learning algorithm
The python library selected within the scope of the thesis will be used to benchmark different machine learning algorithms. The student will use floating wind turbine data already structured and post-processed to train different algorithms and assess their performance. User-defined parameters will be investigated to perform a sensitivity of results to different setups.

In this work the student will adapt previous code developed at the university of Zurich and available open access to floating wind farms. The activity will be performed with the support of the supervisor and direct collaboration with ETH, which will provide theoretical and coding support.