Overview

The objective of this work was to build a reliable simulation approach that could predict temperature extremes and transient behavior over multiple orbits. The simulations served both as a design tool and a validation mechanism to confirm thermal margins across the mission profile.

Initial Modeling Strategy (Python Prototype)

I began by constructing a low-fidelity thermal simulation in Python to capture the dominant physical processes before introducing geometric or numerical complexity. The spacecraft was initially treated as a simplified thermal mass with time-varying environmental heating. This approach enabled rapid iteration and clear physical interpretability, helping me establish expected temperature trends and identify worst-case hot and cold operating scenarios.

This early model became my baseline for validating higher-fidelity simulations later in the process. By keeping the first model simple, I could debug assumptions quickly and separate physical effects from tool- or geometry-driven artifacts.

Orbital Environment and Heat Inputs

The orbital environment was modeled to account for periodic exposure to direct solar radiation, reflected solar energy from Earth (albedo), and Earth-emitted infrared radiation. Eclipse periods were computed from orbital geometry and used to remove solar and albedo heating during shadowed phases. This allowed the simulation to capture sharp thermal transitions and transient behavior associated with eclipse entry and exit.

Explicitly modeling each heat source and its orbital dependence helped ensure temperature trends matched physical expectations, rather than relying on black-box results.

Transition to COMSOL (Geometry + Transient Thermal Modeling)

After validating the baseline behavior with the Python model, I moved the workflow into COMSOL to support a higher-fidelity thermal representation. This transition allowed me to incorporate spacecraft geometry, multi-material regions, conductive paths, and time-dependent boundary conditions within a single transient simulation environment. The Python model continued to function as a reference to sanity-check COMSOL results during iteration.

Challenges with Geometry and CAD Fidelity

A major challenge was determining the correct level of geometric fidelity. Early attempts to use detailed CAD geometry directly in COMSOL introduced unnecessary complexity, increased computation time, and made it difficult to trace heat flow paths or debug unexpected outputs. In several cases, added geometric detail obscured the underlying physics instead of improving accuracy.

This prompted a shift from CAD-driven fidelity toward physics-preserving abstraction.

Model Abstraction and Thermal Simplification

I restructured the CAD into a reduced-order thermal model composed of representative external surfaces and internal thermal nodes. I preserved the physical properties that matter most to thermal behavior—total surface areas, material optical characteristics, effective view factors, and dominant conductive paths—while removing nonessential geometric detail such as screws, internal PCB components, and overly complex features that did not meaningfully affect heat transfer at the spacecraft scale.

Conductive coupling between major components was represented using simplified thermal connections to maintain physically meaningful heat transfer behavior. This abstraction kept the COMSOL model computationally tractable while ensuring the dominant thermal mechanisms remained intact and interpretable.

Higher-Fidelity Transient Simulations

With the simplified geometry in place, I ran transient orbital–thermal simulations in COMSOL to capture time-resolved heating and multi-node temperature evolution over several orbital periods, including eclipse transitions. I continuously cross-checked results against the Python baseline to verify consistency in temperature trends and overall energy behavior.

Any disagreements were treated as indicators of assumption breakdowns or modeling errors (boundary conditions, material properties, conductive couplings, or eclipse timing) and were used to refine the model rather than being dismissed as numerical noise.

Validation and Design Integration

Throughout development, I prioritized validation through cross-comparison, sanity checks, and conservation principles instead of relying on a single tool output. The final thermal predictions were used to establish operational temperature bounds, confirm thermal margins, and inform mechanical design decisions such as surface treatments, material selection, and component placement.

Key Lessons Learned

This work reinforced the value of starting with simple physics-based models and increasing fidelity only when it adds meaningful insight. High-fidelity CAD does not inherently produce better thermal predictions; reliable results depend on preserving the governing physics and maintaining models that are debuggable and interpretable. The low-fidelity Python model proved essential for validation, debugging, and building confidence in the final COMSOL results.