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Cake day: August 22nd, 2023
  • iAvicenna@lemmy.worldtoTechnology@lemmy.world•Potentially habitable planet TRAPPIST-1b may have a carbon dioxide-rich atmosphere, The innermost Earth-like planet in the famous TRAPPIST-1 system might be capable of supporting a thick atmosphereEnglish
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    9 days ago

    Achieving a velocity of 0.5 times the speed of light (ccc) for space travel involves solving advanced challenges in physics and engineering. The Python script below creates a simplified optimization framework to analyze the propulsion needed. It uses physics principles like relativistic mass-energy equivalence and propulsion mechanisms such as fusion or antimatter engines.

    This code assumes you have the theoretical fuel and energy to achieve the speed, but it abstracts away complex challenges like time dilation, cosmic radiation, and material limitations.

    Python Code

    import numpy as np
import matplotlib.pyplot as plt

# Constants
c = 3e8  # Speed of light (m/s)
target_speed = 0.5 * c  # Target speed (0.5c)
ship_mass = 1e5  # Mass of the spacecraft without fuel (kg)
fuel_efficiency = 1e-3  # Fuel conversion efficiency (e.g., antimatter ~0.1, fusion ~0.001)
exhaust_velocity = 1e7  # Exhaust velocity of the propulsion system (m/s)
specific_impulse = exhaust_velocity / 9.81  # Specific impulse (seconds)

# Functions
def relativistic_mass(speed, rest_mass):
    """Calculate relativistic mass."""
    gamma = 1 / np.sqrt(1 - (speed / c) ** 2)
    return gamma * rest_mass

def fuel_needed(final_speed, ship_mass, exhaust_velocity):
    """Estimate fuel required using the Tsiolkovsky rocket equation."""
    delta_v = final_speed
    mass_ratio = np.exp(delta_v / exhaust_velocity)
    fuel_mass = ship_mass * (mass_ratio - 1)
    return fuel_mass

# Optimization
def optimize_propulsion(target_speed, ship_mass, exhaust_velocity, fuel_efficiency):
    fuel_mass = fuel_needed(target_speed, ship_mass, exhaust_velocity)
    energy_required = fuel_mass * fuel_efficiency * c**2  # Total energy for propulsion
    return fuel_mass, energy_required

# Perform calculations
fuel_mass, energy_required = optimize_propulsion(target_speed, ship_mass, exhaust_velocity, fuel_efficiency)

# Display results
print(f"Target speed: {target_speed / c:.2f}c")
print(f"Required fuel mass: {fuel_mass:.2e} kg")
print(f"Energy required: {energy_required:.2e} joules")

# Visualization
speeds = np.linspace(0, 0.5 * c, 100)
fuel_masses = [fuel_needed(speed, ship_mass, exhaust_velocity) for speed in speeds]

plt.figure(figsize=(10, 6))
plt.plot(speeds / c, fuel_masses, label='Fuel Mass Required')
plt.xlabel('Speed as a Fraction of Light Speed (v/c)')
plt.ylabel('Fuel Mass (kg)')
plt.title('Fuel Requirement vs. Speed')
plt.legend()
plt.grid()
plt.show()

    How it Works:

    Relativistic Mass: Accounts for relativistic effects as the spacecraft approaches significant fractions of the speed of light.
Tsiolkovsky Rocket Equation: Estimates fuel requirements based on the propulsion system's exhaust velocity and initial mass.
Energy Requirement: Evaluates energy needs assuming the specified fuel efficiency.

    Limitations:

    Idealized Assumptions: Assumes perfect efficiency and ignores engineering constraints.
Simplified Physics: Doesn't account for external factors like gravity, drag, or realistic propulsion systems.
Energy Storage: No consideration for how energy would be stored or delivered.

    Would you like more refinement or details on specific subsystems?

    FIRE ALL THE PHYSICISTS