Masters Project
This project focussed on the development and deployment of an optimisation toolchain for maximising the performance of an airborne wind energy system. Airborne wind energy system have the potential to access stronger and more consistent high-altitude wind resources while requiring significantly less material than conventional wind turbines. The toolchain aims to optimise the performance of the aerofoil by maximising the lift generated to maximise the power the system can generate.
Background
Airborne wind energy uses a tethered aircraft to create electricity while avoiding the large material costs of conventional wind turbines. Drag powered kites which are the focus of this study having a tethered kite with onboard generators, the kite aerofoil allows it to fly crosswind at high speeds increasing the relative wind speed at the turbines. There are several companies working on designs, most are in concept stages with a few that have built some prototypes such as Makani which are now defunct and Kitekraft.
The understanding on the topic for many years was that the power generated by the system is proportional to the coefficient of lift cubed divided by the equivalent coefficient of drag squared, so by optimising the glide ratio which is the ratio of the two coefficients, you would maximise power. Recent research done by Bauer in TU Munich who was working closely with the company kitekraft showed that when the drag forces of other parts of the system, in particular the tether drag is included the previous research is not always valid. In the case where the tether drag is dominant the power generated is proportional to the coefficient of lift squared and should be optimised for. There have been very limited studies on the optimisation of airfoils for airborne wind energy with even fewer optimising multi element high lift aerofoils aiming to maximise the CL which is the focus of my project.
This project built on work done a previous masters project and code developed by TU Munich and Kitekraft. The toolchain uses octave to run the optimisation with the CFD analysis using simplefoam in OpenFOAM. My work focused on incorporating geometric design parameterisation, so allowing the toolchain to create its own aerofoil geometry that it can modify to optimise its performance. Then to update the toolchain to allow it to be run on high performance computing using that to optimise the performance of a multi element aerofoil for use in an airborne wind energy system.
Toolchain Updates
The main update to the toolchain was the creation of a new objective function that implemented geometry parameterisation. It uses Bezier parsec parameterisation to create the airfoil geometry. This works by creating a thickness and camber curve from 2 Bezier curves each. The control points of the Bezier curves are calculated from a set of geometry related input parameters such as thickness, the inside angle of the trailing edge and the radius of the leading edge. The curves are processed to turn them into the coordinates of the airfoil and then saved as an STL file to be used in OpenFoam. The objective function runs some checks on the geometry to ensure the elements are not intersecting or the parameterisation created impossible geometry. Then it runs the CFD analysis in Openfoam and returns the coefficient of lift calculated
Through the irish centre for high end computing, access was obtained to the national supercomputer which is currently hosted on the meluxina supercomputer in Luxembourg. Some updates to the toolchain had to be made to allow it to be run on the supercomputer such as changing the utilities used to create the STL files. To take advantage of the 256 threads on each compute node, the toolchain was updated to allow it to run 32 instances of the objective function simultaneously. It does this by using a wrapper function to create multiple instances of the objective function for each generation of the optisation. Each instance of the objective function has 8 threads that it can use to run the CFD solver in parallel.
