Propulsion Projects
Explore guided student projects in Propulsion. Build hands-on skills with real aerospace tools and data.
5 projects
Combine with other filters →Build a Model Rocket Flight Computer
Log altitude, acceleration, and temperature on a real launch
Build an Arduino-based flight computer that records sensor data during a model rocket launch. Learn embedded programming, data logging, and basic rocketry while creating something you can actually fly.
Start Project →Turbine Blade Multiphysics Analysis
Perform coupled thermal-structural-vibration analysis of a turbine blade under real operating conditions
Conduct a complete multiphysics analysis of a gas turbine blade in Siemens Simcenter: steady-state thermal analysis under combustion gas and cooling air conditions, one-way coupled structural analysis for thermal stress, and constrained modal analysis for vibration frequencies under centrifugal load — matching the workflow used in engine certification.
Start Project → Some AI/MLPredict Model Rocket Altitude from Motor Data with Python
Use real thrust curves and simple physics to predict how high your rocket will fly
Build a dataset from published model rocket motor thrust curves and basic physics, then train a scikit-learn regression model to predict peak altitude from motor impulse, rocket mass, and drag estimate. Combines rocketry fundamentals with your first predictive ML model.
Start Project → Some AI/MLPredict Jet Engine Thrust from Sensor Data with Random Forest
Use NASA engine data to learn what sensor readings reveal about thrust
Use the NASA C-MAPSS turbofan engine simulation dataset to build a regression model that predicts thrust output from temperature, pressure, and spool speed sensor readings. Explore feature importance and gain insight into gas turbine thermodynamics through data.
Start Project → AI/MLML Model for Combustion Instability Detection
Train a neural network to hear when a combustor is about to go unstable
Train a time-series classifier — LSTM or 1D-CNN — on pressure oscillation data to detect thermoacoustic instability in combustion chambers. Addresses a critical safety problem in rocket engines and gas turbines with deep learning on sequential sensor data.
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