Leveraging Machine Learning to Decode Rocket Launch Simulations

Introduction

Rocket launch simulations, a critical component of aerospace engineering, offer profound insights into the myriad forces at play during a rocket’s ascent. This article delves into a sophisticated approach where machine learning, particularly Convolutional Neural Networks (CNNs), is harnessed to analyse and categorize these simulations based on their unique characteristics.

Rocket Launch Simulations: The Foundation

Simulating a rocket launch is akin to peering through a computational lens into the complex dynamics governing a rocket’s journey. This involves meticulously calculating the rocket’s altitude, velocity, and acceleration, taking into account the thrust, drag, gravitational forces, and the changing mass as fuel burns away.

Code Snippet: Simulating Rocket Launch

def simulate_rocket_launch():
    times, altitudes, velocities, masses, accelerations = ([] for i in range(5))
    altitude, velocity, mass_total, time = 0, 0, mass_empty + mass_fuel_initial, 0
    
    while mass_total > mass_empty and altitude >= 0:
            rho = rho_0 * np.exp(-altitude / H)  # Atmospheric density
            drag = 0.5 * rho * velocity**2 * Cd * A  # Drag force…