Gain insights into using Python for mechanical and aerospace engineering. Learn about basics, graphing techniques, airfoil plotting, and dynamic pressure and orbital modeling in 2D and 3D.

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In this course, you will learn about the Python programming language in the context of mechanical and aerospace engineering. 

You will start with the basics of Python, like variables, collections, and different types of loops. Once you have the basics out of the way, the real fun begins. You will learn how to: graph thrust available and thrust required, graph dynamic pressure during a rocket launch, and how to plot airfoil coordinates. 

Towards the end, you will get a strong overview of orbital parameters and learn how to model both 2D and 3D static orbits. Once you’ve completed this course, you will have a better understanding of Python and how it works in the context of mechanical/aerospace engineering.

Python for Mechanical and Aerospace Engineering

# Full code
Here is what the completed program looks like:

import numpy as np
import matplotlib.pyplot as plt


# Equations
def density(height: float) -> float:
    """
    Returns the air density in slug/ft^3 based on altitude
    Equations from https://www.grc.nasa.gov/www/k-12/rocket/atmos.html
    :param height: Altitude in feet
    :return: Density in slugs/ft^3
    """
    if height < 36152.0:
        temp = 59 - 0.00356 * height
        rho = 2116 * ((temp + 459.7)/518.6)**5.256
    elif 36152 <= height < 82345:
        rho = 473.1*np.exp(1.73 - 0.000048*height)
    else:
        temp = -205.05 + 0.00164 * height
        rho = 51.97*((temp + 459.7)/389.98)**-11.388

    return rho


def velocity(time: float, acceleration: float) -> float:
    """
    Convert time to velocity using Vf = Vi + at
    (where Vf = final velocity,
    Vi = initial velocity, [0 in this case]
     a = acceleration, t = time
    :param time: int time in seconds
    :param acceleration: acceleration in ft/s^2
    :return: velocity in ft/s
    """
    return acceleration*time


def altitude(time: float, acceleration: float) -> float:
    """
    Convert time to altitude using the constant acceleration equation
    x = vi*t + 0.5*a*t^2, where vi = 0 in this case
    :param time: Time in seconds
    :param acceleration: acceleration in ft/s^2
    :return: Altitude in feet
    """
    return 0.5*acceleration*time**2

plt.style.use('bmh')
y_values = []
x_values = np.arange(0.0, 550.0, 0.5)
for elapsed_time in x_values:
    accel = 51.764705882
    alt = altitude(elapsed_time, accel)
    # Dynamic pressure q = 0.5*rho*V^2 = 0.5*density*velocity^2
    q = 0.5 * density(alt) * velocity(elapsed_time, accel) ** 2
    y_values.append(q)

plt.plot(x_values, y_values, 'b-',
          label=r"a = 51.76 $\frac{ft}{s^2}$")
max_val = max(y_values)
ind = y_values.index(max_val)

# Plot an arrow and text with the max value
plt.annotate('{:.2E} psf @ {} s'.format(max_val, x_values[ind]),
              xy=(x_values[ind] + 2, max_val),
              xytext=(x_values[ind] + 15, max_val + 1E5),
              arrowprops=dict(facecolor='blue', shrink=0.05),
              )
# plot the point of Max Q
plt.plot(x_values[ind], max_val, 'rx')

# Now let's make acceleration = 32.2 ft/s^2 (1g)
y2_values = []
for elapsed_time in x_values:
    accel = 32.2
    alt = altitude(elapsed_time, accel)
    q = 0.5 * density(alt) * velocity(elapsed_time, accel) ** 2
    y2_values.append(q)

plt.plot(x_values, y2_values, 'k-',
          label=r"a = 32.2 $\frac{ft}{s^2}$")
max_val = max(y2_values)
ind = y2_values.index(max_val)

# Plot an arrow and text with the max value
plt.annotate('{:.2}E+09 psf @ {} s'.format(max_val/1E9, x_values[ind]),
              xy=(x_values[ind] + 3, max_val),
              xytext=(x_values[ind] + 15, max_val + 1E5),
              arrowprops=dict(facecolor='black', shrink=0.05),
              )

# plot the point of Max Q
plt.plot(x_values[ind], max_val, 'rx')

# Now let's make acceleration = 20.0 ft/s^2
y3_values = []
for elapsed_time in x_values:
    accel = 20.0
    alt = altitude(elapsed_time, accel)
    q = 0.5 * density(alt) * velocity(elapsed_time, accel) ** 2
    y3_values.append(q)

plt.plot(x_values, y3_values, 'g-',
         label=r"a = 20.0 $\frac{ft}{s^2}$")
max_val = max(y3_values)
ind = y3_values.index(max_val)

# Plot an arrow and text with the max value
plt.annotate('{:.2}E+09 psf @ {} s'.format(max_val/1E9,
                                           x_values[ind]), 
        xy=(x_values[ind] + 3, max_val),
        xytext=(x_values[ind] + 15, max_val + 1E5),
        arrowprops=dict(facecolor='green', shrink=0.05))

# plot the point of Max Q
plt.plot(x_values[ind], max_val, 'rx')

plt.xlim(0, 150)
plt.xlabel('Time (s)')
plt.ylabel('Pressure (psf)')
plt.title('Dynamic pressure as a function of time')
plt.legend()
#plt.show()
plt.savefig("output/chap3_4.png")

Even 400,000 psf is likely too high of a value for maximum dynamic pressure, but the graph does show a drastic relationship between average acceleration and pressure. The takeaway here is that changing the launch average acceleration can have dramatic effects on the maximum pressure experienced by the launch vehicle.

# Quiz

Use the following code to answer the quiz question.

Develop the code for the dynamic pressure experienced by a rocket during launch.

Getting Comfortable with Python

FizzBuzz

Graphing Thrust Available and Thrust Required

Graphing Dynamic Pressure During a Rocket Launch

Getting and Plotting Airfoil Coordinates

Modeling a 2-Body Orbit in 2D and 3D

Unit Conversions

Introduction to Web Scraping

Modeling Camera Shutter Effect

Writing Reports with Pweave

The End

Putting It All Together

Full code