Here are some reports that I delivered whilst at university. I hope they provide interesting reading.

The interdisciplinary project, in which the optinum hydro scheme was developed for a valley in the highlands, is a good example of some the work I completed as part of group of students. If you would like to read the whole report then please  contact me. Otherwise here is a short extract explaining the programming strategy and the Glen_Kinglas_Hydro_Appendices. Use of Hydrology in Matlab Script

Hydrology information for the map was compiled in the form of two matrices. The first was a matrix of hydrological run-off scenarios in their spatial co-ordinates. This related to the second which contained the flow data for these scenarios. This is covered in more detail in the hydrological section.


Figure 3.2.1 describes the format of the hydrology matrix used in the optimisation and design process. The flow duration data is stored along the Z axis. The position of this data in the X and Y axis coordinates corresponds to the physical geography.

A 3D matrix of flow duration data with respect to each spatial co-ordinate was required. To do this by hand, the catchment area at a point on the river would be found by examining the contours on a map. This area would be combined with rainfall and other hydrological factors would determine how much run-off reached the river. To do this computationally is much harder as the upslope area is more difficult to find since it cannot be determined visually. Furthermore, the total run-off for a given point is a sum of the run-offs for the upslope points that flow into it. This requires knowing all of the possible routes the flow can take to reach the point in question and summing the flow duration data for each of these points.



A 2D graph of turbine efficiencies

I recently had a moment of nostalgia and had another look at what we did. I decided it would also be neat if the script we wrote would work on an opensource plattform. So I have fixed the odd problem when using Octave to run the program und uploaded the result on Github. Here are some graphs I made.


A 3D graph of turbine efficiencies

This is a nice example where understanding some data is made a lot easier with images. My turbine efficiency graph is representation of the “hill curves” from various types of water turbines. For a given head and flow rate a turbine will run at a particular efficiency. When the two perfomance-defining criteria are plotted on x and y axes and the efficiency as a contour plot, a similarity to a map of a hilly lanscape can be seen. Hence the term hill curve. Of course it is possible to go one step better and just make a 3d graph.

If I ever buy land with a river then one of the first things I would do is use the software to design a hydroelectric setup.

At Edinburgh University a wind tunnel has been built using a Hoover. A blade is attached in the centre of the tunnel. Attached to the blade are an array of maneometers (the tubes with coloured dye). Two strain gauges positioned perpendicularly are used to measure the drag and lift forces.

An interesting project was designing a run of the river turbine scheme. The considerations were the efficiency of the turbine at various flow speeds and how often these flow rates occur. So a script was made which bins the flow speeds and compares these with the turbine efficiency curve for each possible rating of turbine.

The question this group project addressed was what is the most amount of energy you can fit in a shipping container? To add a bit of realism the group focused on designs that could be quickly implemented after natural disasters, as an alternative to diesel generators.

I wrote fairly elaborate script to determine from an aerodynamic point of view what was possible are what sort of forces would be transmitted to the frame supporting the wind turbine. Have a look at the pretty image I produced.

 The pressure on the blade was calculated and plotted on the body of the wind turbine.

The pressure on the blade was calculated and plotted on the body of the wind turbine.

The supporting structure is what takes up the most space. We discovered that filling up a shipping container with wind turbines bought from B&Q would provide the most power, but this is blue-sky thinking as there would still be the need to have some sort of struture to attach the turbines and space for a lot of cable.

A short study of the control of wind turbines can be found here.

So what difference would it make if we adopted the motto: reduce, reuse and recycle?

A simple bit of coding to solve some vector/trignometric problems.
Crane Design

Here is lab investigating the deflection of a proving ring.
Proving Ring

We found the second moment of area for a L beam and calculated the deflection. The theory was verified.
L beam

As part of my degree programme in 4th year I spent half a year in Germany studying the effects of pipe bends on the measurement accuracy of vortex meters.

Using an electron microscope we investigated the failure of a golf club.

A more involved design assignment used the free software Mastan. The main hurdles were familarisation with the program and post processing of the results.

We looked at carbon nanotubes as a means of curing cancer.

In a fith year course on CFD two flow scenarios where investigated. One was looking at what happens when a fluid enters a pipe junction.

My second CFD assignment examined the design of a mixing tank.

Master Thesis

This thesis investigated the design of a flow diffuser and conditioner for the new circular wave and tidal tank due to be built at Edinburgh University. The aim was to use a diffuser to produce a fully developed velocity profile over the shortest possible distance. A diffuser is a specially designed expansion in a pipe or channel which changes the speed the flow. This property of the device can be used to redistribute velocity of the flow as required.

The flow in the diffuser was anticipated to be highly turbulent and the use of a flow straightener, of various designs, was considered. A number of constraints on the dimensions of the tank prevented the use of a conventional diffuser so a novel design was developed instead. Using computational fluid mechanics the design was developed inexpensively. Validation and verification were performed using analytical calculations and potential flow theory. It was shown that these methods provided good agreement within the diffuser. Beyond the diffuser, potential flow theory predicted a constant velocity profile. It was reasoned that this is due to a fundamental flaw in potential flow theory in modelling the far field flow characteristics.
The unique design presented in the report stems from the unconventional tank required and the sparse documentation available on diffuser design. This thesis adds to the literature in the field of wave and tidal tank diffuser design.