Predict Avionics Bay Temperature with Regression Models

Model the avionics bay as a thermal resistance-capacitance (R-C) network. Define 5–8 nodes: individual electronic units (flight computer, radar processor, communication radios), the avionics bay air volume, the bay walls/structure, and the external environment. Each node has a thermal capacitance (mass times specific heat) and heat generation rate (power dissipation).

Connect nodes with thermal resistances representing conduction (through mounting brackets and cold plates), convection (from component surfaces to bay air, and from bay air through ventilation), and radiation (between components and between bay walls and the environment). Use standard correlations: forced convection Nusselt numbers for internal airflow, conduction R = L/(k*A) for structural paths. Solve the system using scipy.integrate.solve_ivp for transient response or direct matrix inversion for steady-state.

Parameterize the operating conditions that affect avionics bay temperature. Key variables include: ambient temperature (-40 to +50 degC), altitude (0 to 45,000 ft, which affects air density and cooling effectiveness), flight phase (ground idle, taxi, takeoff, climb, cruise, descent, landing — each has different ventilation airflow and power demands), solar load (function of time of day and aircraft orientation), and equipment power state (which units are on/off).

Define 3–4 thermal management states: cooling fan speed (off, low, high), ram air valve position (closed, partially open, fully open), and vapor-cycle cooling (on/off). Create a parameter table listing all variables and their ranges. Use Latin Hypercube Sampling to generate 5,000–10,000 operating condition combinations that cover the full envelope.

Run the thermal network model for each operating condition sample. For transient cases, simulate a representative flight profile (30 min ground, 20 min climb, 2 hr cruise, 30 min descent) and extract the peak temperature for each component. For steady-state cases, compute the equilibrium temperature directly.

Store each data point as a feature vector (ambient temp, altitude, flight phase, power states, cooling system state) paired with the target (peak temperature of the hottest component). Also record the second-hottest component temperature and the bay air temperature — you'll use these for multi-output regression later. The dataset generation step will take several hours; use Python's multiprocessing to parallelize across CPU cores.

Encode categorical features (flight phase, equipment state) using one-hot encoding or ordinal encoding. Split the data 70/15/15. Train three models: Ridge regression (linear baseline), Random Forest (500 estimators), and Gradient Boosting (scikit-learn's HistGradientBoostingRegressor).

Evaluate using RMSE, MAE, and maximum absolute error. The maximum error matters most — if the model ever predicts 60 degC when the true answer is 72 degC (above the limit), the consequence in operation would be a missed thermal exceedance. Report the 99th percentile absolute error as a practical reliability metric.

Extract feature importance from the gradient boosting model. Create a SHAP summary plot. You'll likely find that ambient temperature and equipment power dissipation dominate — this makes physical sense because they directly determine the heat balance. Altitude should matter because it affects air density (cooling effectiveness) and ram air temperature.

Investigate interaction effects: does the importance of altitude change depending on whether the cooling fan is on or off? Use SHAP dependence plots to visualize two-way interactions. These interactions reveal when simple thermal rules of thumb fail — for example, the cooling fan may be sufficient at cruise altitude but inadequate on a hot day at a high-altitude airport (thin air + high ambient = worst case).

Use the trained model to analyze specific operational scenarios. Hot day at Phoenix (PHX): ambient temp 50 degC, altitude 1,100 ft, ground idle with minimal airflow — does the avionics bay exceed limits? How many minutes of ground time before overheating? High-altitude cruise in winter: ambient -50 degC, 41,000 ft — is there a risk of components getting too cold?

Create a thermal margin dashboard: for each flight phase and ambient condition combination, show the predicted temperature as a colored heatmap (green = within limits, yellow = approaching limits, red = exceeding limits). This is exactly the kind of tool a flight operations team would use to set ground-time limits or equipment operating procedures for extreme conditions.

Extend the model to predict temperatures at all component nodes simultaneously using scikit-learn's MultiOutputRegressor wrapper or a single model with multiple outputs. This enables identifying which specific component is the thermal bottleneck under each condition — valuable information for designers considering targeted cooling improvements.

Write a technical report structured as an engineering analysis: problem statement, thermal model description and validation, ML model development and performance, operational analysis results, and recommendations. Include a discussion of how this approach could be deployed: an onboard thermal prediction system that warns the crew before temperature limits are reached, or a ground-based planning tool that validates equipment configurations before dispatch.