RocketDynamicsLab

Course Notes — RocketDynamicsLab

A graduate lab in six-degree-of-freedom rigid-body flight dynamics

Purpose

This repository is a teaching laboratory built around one required reading:

M. Khalil, H. Abdalla, O. Kamal, “Trajectory Prediction for a Typical Fin Stabilized Artillery Rocket”, ASAT-13-FM-04, 13th International Conference on Aerospace Sciences & Aviation Technology, Military Technical College, Cairo, 2009. (FM04.pdf, repo root.)

The paper is short (14 pages) but dense: in one block diagram (its Fig. 1) it packs a full 6-DOF rigid-body simulation — translational and rotational dynamics, Euler kinematics, navigation, an atmosphere model, an aerodynamic-coefficient lookup, and a Monte-Carlo dispersion study. Reading it once is not enough to understand it. This lab exists so you can:

  1. Read each equation of the paper next to a plain-language explanation (equations.md).
  2. Run the exact same equations yourself, in Python, one Euler/RK4 step at a time (numerical-methods.md).
  3. Perturb the aerodynamic coefficients, the mass properties, or the wind, and see the trajectory change, using the Streamlit GUI (src/gui/app.py).
  4. Reproduce the paper’s sensitivity/dispersion study (its Table 2 and Figs. 10–21) yourself and discuss why each parameter matters (uncertainty-analysis.md).

What this is not

This is not an operational trajectory/fire-control engineering tool, and it is not a faithful bit-for-bit reproduction of the paper’s numbers. Several of the paper’s tables (notably Table 1, the Datcom aerodynamic-coefficient table) are corrupted in the source PDF’s text layer — columns of numbers ran together during OCR/text-extraction and cannot be reliably un-scrambled. We digitized representative coefficients that preserve the reported trend (the transonic hump around M≈1–1.4, the supersonic falloff) and are rescaled to numerically well-behaved, textbook-typical magnitudes. Every place this happens is flagged in the code and in aerodynamic-model.md. Treat all numbers here as a fictional teaching dataset, not design data.

How the repository is organized

docs/                    <- you are here: theory, math, assignments
src/simulator/           <- the physics: frames, EOM, atmosphere, aero, integrators
src/visualization/       <- reusable Plotly figure builders
src/gui/                 <- the Streamlit multipage lab notebook
examples/                <- short standalone scripts (run without Streamlit)
tests/                   <- pytest unit/regression tests for the simulator
assets/                  <- static figures / diagrams used by the docs and GUI

Suggested reading order

  1. mathematical-model.md — the assumptions and the big picture.
  2. coordinate-systems.md — frames and Euler angles.
  3. equations.md — every term of Eqs. (1)–(4) explained.
  4. numerical-methods.md — how we turn ODEs into numbers.
  5. atmosphere-model.md and aerodynamic-model.md — the two “black boxes” that feed forces/moments into the equations of motion.
  6. uncertainty-analysis.md — the dispersion study.
  7. assignments.md — do the exercises.
  8. instructor-guide.md — if you are teaching this material.

Running the lab

pip install -r requirements.txt
streamlit run src/gui/app.py

Or, without the GUI, run any of the examples/*.py scripts directly.

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