Lära Temperature and Pressure Data Analysis | Thermodynamics and Data Analysis

Temperature and pressure are two of the most critical measurements in mechanical engineering. They are used to monitor the performance, safety, and efficiency of engines, HVAC systems, manufacturing processes, and many other applications. Consistent and accurate analysis of these parameters helps you detect faults, optimize operations, and ensure compliance with engineering standards. Python, with its powerful libraries and clear syntax, is an excellent tool for collecting, analyzing, and visualizing temperature and pressure data, making it easier to draw meaningful conclusions and communicate results.


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import statistics

# Hardcoded lists of temperature (in Celsius) and pressure (in kPa) readings
temperature_readings = [72.1, 73.4, 71.8, 74.0, 72.9, 73.2, 72.5]
pressure_readings = [101.2, 100.8, 101.5, 100.9, 101.0, 101.3, 100.7]

# Calculate mean, min, and max for temperature
temp_mean = statistics.mean(temperature_readings)
temp_min = min(temperature_readings)
temp_max = max(temperature_readings)

# Calculate mean, min, and max for pressure
press_mean = statistics.mean(pressure_readings)
press_min = min(pressure_readings)
press_max = max(pressure_readings)

print("Temperature (C) - Mean:", temp_mean, "Min:", temp_min, "Max:", temp_max)
print("Pressure (kPa) - Mean:", press_mean, "Min:", press_min, "Max:", press_max)

In the code above, you use Python's statistics.mean() function to calculate the average (mean) value for both temperature and pressure readings. The min() and max() functions find the lowest and highest values in each list. These basic statistics are essential for monitoring engineering systems because they help you quickly assess whether a system is operating within safe and efficient ranges. For example, if the maximum temperature exceeds design limits, it may indicate a risk of overheating. Similarly, consistent pressure readings that fall outside expected values could signal leaks or equipment malfunctions. Regularly calculating and monitoring these statistics allows you to make informed decisions and take corrective action before problems escalate.


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import matplotlib.pyplot as plt

# Simulate time points (e.g., minutes)
time_points = list(range(1, len(temperature_readings) + 1))

# Create a plot for temperature and pressure
plt.figure(figsize=(10, 5))
plt.plot(time_points, temperature_readings, marker='o', label='Temperature (C)')
plt.plot(time_points, pressure_readings, marker='s', label='Pressure (kPa)')
plt.xlabel('Time (minutes)')
plt.ylabel('Measurement')
plt.title('Temperature and Pressure Over Time')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

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Avsnitt 3. Kapitel 1

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Can you explain how the plot helps in analyzing the data?

What other types of visualizations can I use for temperature and pressure data?

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import statistics

# Hardcoded lists of temperature (in Celsius) and pressure (in kPa) readings
temperature_readings = [72.1, 73.4, 71.8, 74.0, 72.9, 73.2, 72.5]
pressure_readings = [101.2, 100.8, 101.5, 100.9, 101.0, 101.3, 100.7]

# Calculate mean, min, and max for temperature
temp_mean = statistics.mean(temperature_readings)
temp_min = min(temperature_readings)
temp_max = max(temperature_readings)

# Calculate mean, min, and max for pressure
press_mean = statistics.mean(pressure_readings)
press_min = min(pressure_readings)
press_max = max(pressure_readings)

print("Temperature (C) - Mean:", temp_mean, "Min:", temp_min, "Max:", temp_max)
print("Pressure (kPa) - Mean:", press_mean, "Min:", press_min, "Max:", press_max)


              12345678910111213141516
            
import matplotlib.pyplot as plt

# Simulate time points (e.g., minutes)
time_points = list(range(1, len(temperature_readings) + 1))

# Create a plot for temperature and pressure
plt.figure(figsize=(10, 5))
plt.plot(time_points, temperature_readings, marker='o', label='Temperature (C)')
plt.plot(time_points, pressure_readings, marker='s', label='Pressure (kPa)')
plt.xlabel('Time (minutes)')
plt.ylabel('Measurement')
plt.title('Temperature and Pressure Over Time')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 1

Temperature and Pressure Data Analysis

1. Why is it important to analyze temperature and pressure data in engineering systems?

2. Which Python functions can be used to compute mean and max values?

3. How does data visualization help in identifying trends in engineering data?

Temperature and Pressure Data Analysis

1. Why is it important to analyze temperature and pressure data in engineering systems?

2. Which Python functions can be used to compute mean and max values?

3. How does data visualization help in identifying trends in engineering data?