A doctoral research project that built a predictive simulation model for high-speed Rotax drone engines, calculating power output and fuel consumption before any physical engine was built.
Drone engine development is expensive. Every prototype costs time, material, and testing resources. And when the engine fails, you often do not know whether the problem is in the design, the manufacturing, or the assumptions you started with.
This doctoral research project asked a simple question: can we predict how a high-speed Rotax engine will perform before we machine a single part? The answer was a simulation model that calculated power output, fuel consumption, and thermal behavior using computational fluid dynamics and thermodynamic analysis.
The model was built for Rotax 912 and 914 series engines operating at altitudes and speeds typical of medium-altitude drones. Every variable was grounded in real engine geometry, real fuel properties, and real atmospheric conditions.
The result: a validated predictive tool that estimated engine performance within acceptable engineering tolerance, giving designers the ability to test dozens of configurations on a computer before committing to a single physical prototype.
Drone engine development relied on physical prototyping. Each iteration meant machining parts, assembling engines, and running tests. When performance fell short, teams could not tell if the problem was aerodynamic, thermal, or mechanical.
There was no reliable way to predict power output or fuel consumption at altitude before metal was cut. Decisions were made with guesswork, and guesswork is expensive at high RPM.
We built a computational model of the Rotax engine using actual cylinder geometry, valve timing, and fuel injection parameters. The simulation ran combustion cycles, airflow patterns, and heat transfer calculations across the operating envelope.
Every result was checked against published engine data and independent test reports. Where the model diverged from reality, we traced the cause and refined the assumptions.
The research produced a validated simulation framework that predicts engine power, fuel consumption, and thermal load across altitude and speed ranges relevant to drone operation.
The work was published in peer-reviewed journals and forms part of the engineering foundation behind Freedom Masons approach to engine testing and performance analysis.
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Every item below was written to survive peer review and to be used by engineers who need to predict engine performance before committing to hardware.
What this research produced, and the evidence base behind the engineer who delivered it.
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