Rocket Skip-Glide Reentry Optimization

ISEF Category: Engineering Technology: Statics and Dynamics

Subcategory: Aerospace and Aeronautical Engineering  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A rocket does not just fall back down. It can trade speed for lift, like skipping a stone across water. That makes reentry a control problem, not just a physics problem. If you can model it well, you can predict which path lands farther, safer, or with less stress.

What Is It?

Skip-glide reentry is a flight path where a vehicle uses lift to bounce or arc through the upper atmosphere instead of dropping straight down. Think of a stone that skips across a pond. Each skip bleeds off energy in a controlled way. For rockets, that idea can help you compare descent paths and see how guidance choices change range, heating, and landing footprint.

This project blends flight dynamics, control, and data fitting. JSBSim can simulate rocket motion, while model predictive control, or MPC, can choose commands that steer the vehicle toward a target path. CasADi and do-mpc help solve the optimization problem. You then compare the simulated flight with real telemetry from a small instrumented rocket, so your model matches reality better instead of living in a perfect math world.

Why This Is a Good Topic

This makes a strong science fair topic because you can test real design choices, not just describe them. You can compare different control laws, different model assumptions, and different flight profiles using the same telemetry base. The project connects to a real aerospace problem, how to guide high-speed vehicles safely through the atmosphere, and it teaches you simulation, estimation, and optimization skills that matter in engineering.

Research Questions

• How does the chosen control law change the predicted downrange distance for a skip-glide reentry profile?
• What is the effect of different drag and lift coefficients on the match between simulated and measured flight data?
• Does adding telemetry-based parameter fitting improve trajectory prediction more than using a default rocket model?
• To what extent does sensor noise in GPS, barometer, and IMU data change the estimated flight path?
• Which trajectory shape produces the lowest peak deceleration while still landing within a target zone?
• How does the optimization horizon in MPC affect path smoothness and tracking error?

Basic Materials

• Cardboard or foam rocket airframe for instrumentation tests.
• GPS logger or GPS module with data export.
• Barometric pressure sensor.
• IMU module with accelerometer and gyroscope.
• Microcontroller such as Arduino or Raspberry Pi Pico.
• Laptop with Python installed.
• Digital kitchen scale with 0.1 g accuracy.
• Launch site access that follows local safety rules and model rocket regulations.
• Recovery gear such as streamer or parachute.
• Telemetry cables, batteries, and storage media.

Advanced Materials

• High-power rocket airframe and recovery system approved for supervised launches.
• Flight computer or data logger with synchronized GPS, barometer, and IMU channels.
• Wind-measurement access through site weather station data or a portable anemometer.
• High-rate telemetry receiver if available.
• Materials for mass-property measurements, including center-of-mass balancing setup.
• Access to a wind tunnel or aircraft performance dataset if your mentor has one.
• MATLAB or Python environment for system identification and validation.
• University-grade simulation workstation for repeated parameter sweeps.

Software & Tools

• Python: Runs data cleaning, plotting, system identification, and optimization scripts.
• JSBSim: Simulates flight dynamics for comparison against telemetry.
• do-mpc: Builds model predictive control experiments for trajectory shaping.
• CasADi: Solves constrained optimization problems used in MPC.
• Jupyter Notebook: Lets you document analysis, figures, and model checks in one place.

Experiment Steps

1. Define the exact flight question, such as range, stability, heating proxy, or landing footprint, so your model has one clear goal.
2. Choose the smallest set of state variables and forces that still describe the rocket well enough to compare with telemetry.
3. Fit the model parameters to real flight data so your simulation matches the measured trajectory before you test control ideas.
4. Build a baseline descent path and a skip-glide path, then decide which performance metrics will compare them fairly.
5. Add the MPC layer and set realistic constraints that reflect the rocket’s motion limits and sensing limits.
6. Validate the final model on a flight or hold-out dataset and check whether the predicted and measured paths agree across the full descent.

Common Pitfalls

• Using raw telemetry without time synchronization, which makes the simulation look wrong even when the model is good.
• Fitting too many parameters at once, which can make several different rocket models look equally good.
• Ignoring sensor bias in the barometer or IMU, which can distort altitude and attitude estimates.
• Comparing skip-glide and straight-descent paths with different starting conditions, which makes the result unfair.
• Treating a cleaned-up simulation as proof, then skipping validation against a separate flight or hold-out segment.

What Makes This Competitive

A stronger project will not just run a simulation. It will prove that the model fits real telemetry well, then test whether the skip-glide strategy beats a simpler descent under the same conditions. Good entries compare multiple controllers, report uncertainty, and use validation on separate flight data. The best versions also explain which parameters matter most and which ones barely change the outcome.

Project Variations

• Test the same skip-glide idea on a smaller model rocket and compare whether scale changes the fitted drag profile.
• Replace MPC with a simpler guidance rule, then measure how much prediction error you gain or lose.
• Use barometer-only, GPS-only, and full sensor fusion data to see which telemetry source best supports trajectory fitting.

Learn More

• NASA Technical Reports Server: Search for free reports on reentry dynamics, flight control, and trajectory optimization.
• MIT OpenCourseWare: Look for aerospace dynamics, controls, and optimization lecture materials.
• JSBSim documentation: Read the open-source flight dynamics model guide and example aircraft setups.
• PubMed: Search for review articles on sensor fusion and inertial navigation when you need data-processing background.
• NOAA National Weather Service data: Check local atmospheric and wind data for flight-condition context.
• Journal of Guidance, Control, and Dynamics: Search the journal for papers on trajectory optimization and reentry guidance.