Aerospace Engineering Personal Statementfor Imperial

I want to study aerospace engineering because I am most interested in the point where mathematical models meet physical limits. That became clear when I first watched NASA's video of Ingenuity lifting off in Jezero Crater on 19 April 2021. What held my attention was not simply that a helicopter flew on Mars, but that it flew in an atmosphere so thin that a rotorcraft seems almost unsuited to it. I started reading about rotor speed, disc loading and low Reynolds numbers because I wanted to know which assumptions about lift still worked there. The more I read, the more I liked the fact that there was no single elegant answer. Ingenuity worked because engineers managed compromises between lift, mass, power and stability. That is what continues to attract me to the subject. I do not see aerospace engineering as finding a perfect design, but as deciding which trade-offs are acceptable and proving that those choices still work outside ideal conditions. At university I want to understand that process more rigorously, especially in fluid dynamics and flight dynamics, because those seem to be the areas where theory becomes most useful only after it has been tested against reality.

My studies have helped me prepare by making the mathematics and physics behind flight feel less separate from design decisions. In Further Mathematics, differential equations stopped feeling like a way of finishing textbook questions and started to look like a way of describing whether a small disturbance dies away or grows into instability. In mechanics, I became more interested in what happens slightly away from equilibrium, because that seems much closer to how real aircraft behave than idealised particles do. Physics added another layer. Stress-strain graphs and material behaviour made it obvious that aerodynamic efficiency cannot be treated on its own if the structure carrying the load becomes too heavy or too weak. To push that further, I read John D. Anderson Jr.'s Introduction to Flight. His discussion of circulation and induced drag challenged the simplified Bernoulli explanations I had met before and made me think more carefully about why improving one part of a design often creates costs somewhere else. That changed the way I thought about winglets and aspect ratio. I had assumed that better performance meant adding whichever feature reduced drag most, but I began to see why a design that looks better aerodynamically can still become harder to control, build or justify.

Outside lessons, I tried to test these ideas for myself instead of only writing about them. For my EPQ, I asked whether winglets meaningfully improve the glide performance of small unmanned aircraft. I used XFLR5 to compare a plain rectangular wing with a winglet-equipped version at low Reynolds numbers, then built two foamboard gliders with matched span and mass to see how much of the predicted difference survived in practice. My first trials were poor because hand launches changed pitch and speed too much, so I made a simple release rig from a clamp and dowel, launched both gliders indoors from the same height, and recorded horizontal distance over repeated runs. I also wrote a short Python script to calculate averages and compare the spread of the results with the simulated lift-to-drag trend. The model suggested a clearer benefit than the prototypes showed. That mismatch became the most useful part of the project because it forced me to think about what the software had not captured: rough cut surfaces, tiny asymmetries, alignment errors and how sensitive small vehicles are to them. I also enjoyed the UKMT Senior Mathematical Challenge because its problems rewarded careful assumptions rather than brute force, which felt close to the reasoning I needed in the project. These experiences were useful because they made me more careful about evidence and showed me that I enjoy refining a question, testing it, and accepting results messier than the original model promised.