Aim: Over the years I became quite quick at solving the cube. I was keen to see if I could create a mechanical system that would do it in a similar time. Because of financial limitations and equipment I thought it impossible to achieve my usual times of around 1 minute and so settled on a target of 10 minutes. So my aim became; “To create a mechanical system that could solve the cube 100% reliably in less than 10 minutes” What I did: I started from the view that I wanted to get it to find a solution using the process that I usually use. The downside of this approach was that this approach meant that most internet research was irrelevant to my project. Also some methods I found were very sophisticated and expensive eg. the university professor who created a system to solve it in 6 seconds. I wrote software capable of solving the cube, printed out its results then testing the instruction steps by manually manipulating the cube. This was improved until 100% reliable. I then developed the user interface to input the colours on each face. The building of the hardware to manipulate the cube proved my most difficult challenge. To get the cube flipped and rotated accurately using the 5 servos. I modeled this using lego and popsicle sticks until the movements met the accuracy and reliability outcomes I needed. Surprisingly these materials held up to the challenge. Integrating the software and hardware functional models took a lot longer than anticipated to get the software instructions executed and coordinated. A great deal of fine tuning was required. Outcome: The system solves the cube 100% of the time. I was exceptionally pleased with this result in view of the lego and popsicle stick model. On reflection I have achieved a successful working model that university students have aspired to and this gives me great satisfaction. Conclusion: While the outcome is pleasing I envisaged achieving a much faster system with easier data input using camera and colour recognition software. Unfortunately time and my budget restrictions prevented this from being developed. However this is a step I am interested in implementing in the future. The speed could be improved by designing more efficient cube solving algorithms, implementing a camera with colour recognition, and possibly rethinking and redesigning my mechanical design. I would also like to Figure 1 illustrates how the air would flow through a fan, and get pushed underground in several short HDPE pipes. This tempered air would then be fed into a small, insulated air chamber built against the home that contains an air-sourced heat pump. The walls of this chamber would have small vents to balance air pressure, and an exit near the top for cooled exhaust air. When the temperature outdoors is in the coldest stage of winter (daily average of -3.0ºC), the tempered air being brought into the chamber would simulate an outdoor ambient temperature of about 10.0ºC, allowing a heat pump to operate with a COP of ~3.79 (based on data from Goodman Air Conditioning and Heating).² This means that for every unit of energy put into the heat pump, 3.79 units of energy are extracted. 4. Conclusions: In building an enclosed air chamber for around an air-sourced heat pump, it was found that it is possible to simulate a 10.0ºC climate in the coldest parts of winter through air tempering. This will allow the heat pump to run substantially more efficiently throughout the year. This system could be used effectively to heat a home in the winter, as well as cool a home in the summer.