Measure Heat Dissipation and Predict Cooling with Python

Collect 3–4 small samples of different materials: a thick aluminum washer or bolt, a steel nut or bolt, a ceramic tile piece, and a thick plastic piece (HDPE or nylon). Each should be roughly the same size (2–3 cm across) so you're comparing material properties, not geometry.

You'll need a way to measure temperature: a digital cooking thermometer with a probe works well, or an infrared thermometer (point-and-shoot). For more precise readings, a K-type thermocouple with a USB reader (~$15 online) gives you digital data you can log directly. You'll also need a way to heat the samples safely: a pot of boiling water works perfectly.

Heat each sample by immersing it in boiling water for 3–5 minutes (so it reaches approximately 100 degC). Remove it with tongs, place it on a wooden cutting board (which acts as a poor conductor, minimizing heat loss through the bottom), and immediately start recording temperature readings every 15 seconds for 10–15 minutes.

Record the data in a notebook or spreadsheet: time (seconds) and temperature (degC). Also note the room temperature (ambient), which is the temperature the sample is cooling toward. Repeat each material at least twice for reproducibility. If readings are inconsistent, check whether airflow (drafts, AC vents) is affecting the experiment.

Load your data into Python and plot temperature vs. time for all materials on the same axes using matplotlib. You'll see classic exponential decay: rapid cooling at first (when the temperature difference is large) that gradually slows as the sample approaches room temperature.

Plot ln(T - T_ambient) vs. time — if Newton's law holds perfectly, this should be a straight line with slope equal to the negative cooling constant -k. The steeper the slope, the faster the material cools. Which material has the largest k? The smallest? How does this relate to the material's density and heat capacity?

Use scipy.optimize.curve_fit to fit the function T(t) = T_ambient + (T_initial - T_ambient) * exp(-k * t) to each material's cooling data. The function takes time as input and returns temperature, with k as the parameter to fit. Start with an initial guess of k=0.01.

Extract the best-fit k value for each material and compare. Create a bar chart of k values. The aluminum sample should cool fastest (high thermal conductivity spreads heat to the surface quickly), while plastic cools slowest (low conductivity traps heat inside). Discuss why this matters for aerospace: electronics housings are made of aluminum partly because it dissipates heat quickly.

Now create a regression model that predicts the cooling constant k from material properties. Look up the thermal conductivity, density, and specific heat capacity for each material. Even with just 3–4 data points, you can fit a simple relationship using scikit-learn's LinearRegression and see which property best predicts the cooling rate.

Predict: if you tested a copper sample (high conductivity, high density), what k value would your model predict? What about a wood sample (low conductivity, low density)? If possible, test one of these predictions experimentally to validate your model.

Create a summary with four key visualizations: (1) raw cooling curves for all materials, (2) linearized ln(T-T_amb) plots showing the straight-line fit, (3) bar chart of fitted cooling constants k, and (4) the regression model predictions vs. actual k values. Include photos of your experimental setup.

Discuss the connection to aerospace thermal management: explain how the same exponential cooling law governs how quickly an overheating avionics unit returns to normal temperature after a high-power computation phase, or how a spacecraft radiator panel dissipates heat into space. The physics is identical — what changes is the complexity of the geometry, the cooling mechanism (conduction, convection, radiation), and the consequences of getting it wrong.