Impara System Response and Data Visualization | Dynamics and System Simulation

Understanding how a mechanical system responds to inputs or disturbances is a key part of engineering analysis. System response analysis focuses on how variables like displacement, velocity, and acceleration change over time when a system is subjected to forces or initial conditions. Time-domain plots—graphs that show how these quantities evolve with time—are essential tools for engineers. They allow you to interpret a system’s dynamic behavior, compare theoretical and simulated results, and communicate findings visually. Python, with its powerful plotting libraries such as matplotlib, makes it straightforward to generate clear, informative plots that help you analyze and present system responses effectively.


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import numpy as np
import matplotlib.pyplot as plt

# Simulate time values
t = np.linspace(0, 10, 1000)

# Example: displacement, velocity, and acceleration for a mass-spring-damper system
omega_n = 2 * np.pi  # natural frequency (rad/s)
zeta = 0.1           # damping ratio

# Displacement: x(t) = A * exp(-zeta*omega_n*t) * cos(omega_d*t)
A = 1.0
omega_d = omega_n * np.sqrt(1 - zeta**2)
x = A * np.exp(-zeta * omega_n * t) * np.cos(omega_d * t)
v = -A * np.exp(-zeta * omega_n * t) * (zeta * omega_n * np.cos(omega_d * t) + omega_d * np.sin(omega_d * t))
a = -A * np.exp(-zeta * omega_n * t) * (
    (zeta * omega_n)**2 * np.cos(omega_d * t)
    + 2 * zeta * omega_n * omega_d * np.sin(omega_d * t)
    + omega_d**2 * np.cos(omega_d * t)
)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(t, x, label="Displacement (m)")
plt.plot(t, v, label="Velocity (m/s)")
plt.plot(t, a, label="Acceleration (m/s²)")
plt.title("System Response Over Time")
plt.xlabel("Time (s)")
plt.ylabel("Response")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

The code above demonstrates how to visualize the dynamic response of a simulated mass-spring-damper system. By using matplotlib, you can plot displacement, velocity, and acceleration as functions of time on the same graph. This approach allows you to compare how each variable behaves and decays due to damping. When creating engineering plots, always label your axes clearly, include units, and provide a legend to distinguish between different signals. Using plt.grid(True) improves readability by adding grid lines, and plt.tight_layout() ensures that labels and titles do not overlap. These best practices make your plots more effective for analysis and communication in engineering contexts.


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import numpy as np
import matplotlib.pyplot as plt

# Time vector
t = np.linspace(0, 8, 500)

# Damped oscillator parameters
A = 1.0
omega_n = 4.0      # natural frequency (rad/s)
zeta = 0.2         # damping ratio
omega_d = omega_n * np.sqrt(1 - zeta**2)

# Simulate damped oscillator response
x = A * np.exp(-zeta * omega_n * t) * np.cos(omega_d * t)

# Plot the response
plt.figure(figsize=(8, 4))
plt.plot(t, x, color="navy")
plt.title("Damped Oscillator Response")
plt.xlabel("Time (s)")
plt.ylabel("Displacement (m)")
plt.grid(True)
plt.tight_layout()
plt.show()

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Sezione 2. Capitolo 6

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              123456789101112131415161718192021222324252627282930313233
            
import numpy as np
import matplotlib.pyplot as plt

# Simulate time values
t = np.linspace(0, 10, 1000)

# Example: displacement, velocity, and acceleration for a mass-spring-damper system
omega_n = 2 * np.pi  # natural frequency (rad/s)
zeta = 0.1           # damping ratio

# Displacement: x(t) = A * exp(-zeta*omega_n*t) * cos(omega_d*t)
A = 1.0
omega_d = omega_n * np.sqrt(1 - zeta**2)
x = A * np.exp(-zeta * omega_n * t) * np.cos(omega_d * t)
v = -A * np.exp(-zeta * omega_n * t) * (zeta * omega_n * np.cos(omega_d * t) + omega_d * np.sin(omega_d * t))
a = -A * np.exp(-zeta * omega_n * t) * (
    (zeta * omega_n)**2 * np.cos(omega_d * t)
    + 2 * zeta * omega_n * omega_d * np.sin(omega_d * t)
    + omega_d**2 * np.cos(omega_d * t)
)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(t, x, label="Displacement (m)")
plt.plot(t, v, label="Velocity (m/s)")
plt.plot(t, a, label="Acceleration (m/s²)")
plt.title("System Response Over Time")
plt.xlabel("Time (s)")
plt.ylabel("Response")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()


              123456789101112131415161718192021222324
            
import numpy as np
import matplotlib.pyplot as plt

# Time vector
t = np.linspace(0, 8, 500)

# Damped oscillator parameters
A = 1.0
omega_n = 4.0      # natural frequency (rad/s)
zeta = 0.2         # damping ratio
omega_d = omega_n * np.sqrt(1 - zeta**2)

# Simulate damped oscillator response
x = A * np.exp(-zeta * omega_n * t) * np.cos(omega_d * t)

# Plot the response
plt.figure(figsize=(8, 4))
plt.plot(t, x, color="navy")
plt.title("Damped Oscillator Response")
plt.xlabel("Time (s)")
plt.ylabel("Displacement (m)")
plt.grid(True)
plt.tight_layout()
plt.show()

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 6

System Response and Data Visualization

1. Why is it important to visualize system response in engineering?

2. Which matplotlib functions are commonly used for time-domain plots?

3. How can Python plots aid in interpreting simulation results?

System Response and Data Visualization

1. Why is it important to visualize system response in engineering?

2. Which matplotlib functions are commonly used for time-domain plots?

3. How can Python plots aid in interpreting simulation results?