Roadmap

My ideas for future projects that I’m considering are listed below:

1. Rocket Trajectory Simulation with Wind and Drag: Simulate the full flight trajectory of a model rocket under the influence of thrust, gravity, air resistance, and wind, using numerical integration methods like Runge-Kutta. Apply machine learning or evolutionary algorithms to optimise launch angle or thrust profile for maximum altitude or range.

2. Aircraft Wing Design Optimisation: Model the aerodynamic behaviour of a parametric wing using simplified lift and drag equations, adjusting shape features like aspect ratio and camber. Employ CMA-ES or a neural network to optimise the lift-to-drag ratio across varying flight conditions.

3. Propeller or Turbine Blade Shape Optimisation: Simulate the performance of a propeller or turbine blade using blade element theory, capturing the effects of angle of attack and rotational velocity. Optimise blade geometry to maximise thrust or energy extraction efficiency using an evolutionary algorithm.

4. Satellite Orbit Propagation & Ground Track Simulation: Build a simulation of a satellite in Earth orbit using 2-body or perturbed orbital mechanics, and visualise its path as a ground track on a world map. Machine learning can assist in predicting fuel-optimal correction burns or analysing orbital decay patterns.

5. Rocket Nozzle Design and Expansion Ratio Optimisation: Use isentropic flow theory to calculate the performance of different rocket nozzle shapes at various expansion ratios. Apply optimisation algorithms to maximise specific impulse or thrust efficiency based on pressure, altitude, and temperature.

6. Spacecraft Thermal Modelling During Orbit: Simulate the thermal environment of a spacecraft in orbit, including heating from solar radiation and cooling through infrared emission, based on surface materials and orbital parameters. Predict thermal cycles and risks of overheating or freezing during sunlit and shadow phases.

7. Predicting Launch Vehicle Failure with ML: Collect publicly available launch data (e.g., from SpaceX, NASA, Rocket Lab) and use machine learning classifiers to predict the likelihood of failure based on weather, vehicle type, and payload characteristics. Evaluate performance using confusion matrices and feature importance analysis.

8. Mars Entry, Descent, and Landing (EDL) Simulation: Simulate the critical phases of Mars lander descent, including hypersonic entry, parachute deployment, and powered landing, under Martian gravity and atmospheric conditions. Optimise descent profiles or retro-burn timing to ensure a safe and precise landing.

Given that I am a full-time student, projects will likely be completed early in the semester or during breaks.