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Your first simulation

This walks through running one simulation from a cold start to a finished results.json, using the real binary at fidelity 3 (fast enough to run on a login node in under a minute). Every command and output below was actually run to write this page — you should see the same shape of output, though your exact CPU-time numbers will differ.

1. Build

uv sync
make build

make build compiles build/BioReactor from src/BioReactor.c via Basilisk's qcc. If this is your first build, see Setup — you need Basilisk's qcc on your PATH first.

2. Write a params.json

Fidelity 3 (8×8 cells) is too coarse to trust physically, but that's the point of this tutorial — it's about the pipeline, not the physics. We'll also cut n_mix_cycles down to 5 (from a normal 80) so oxygen injection starts almost immediately, and t_end down to 40, so the whole run finishes in seconds instead of minutes.

mkdir -p runs/tutorial_demo
cat > runs/tutorial_demo/params.json <<'EOF'
{
  "run_id": "tutorial_demo",
  "fidelity": 3,
  "omega_b": 3.93,
  "n_harmonics": 1,
  "theta_max": [7.0, 0.0, 0.0],
  "phi_angular": [0.0, 0.0, 0.0],
  "omega_h": 0.0,
  "amplitude_h": [0.0, 0.0, 0.0],
  "phi_horizontal": [0.0, 0.0, 0.0],
  "geometry": {"a": 0.25, "b": 0.071, "n": 8.0},
  "fill_level": 0.5,
  "n_mix_cycles": 5,
  "t_end": 40.0
}
EOF

3. Run it

cd runs/tutorial_demo
../../build/BioReactor params.json

Expected tail of output:

checkpoint: writing checkpoint.dump at t=40.13

# Quadtree, 7217 steps, 12.7809 CPU, 14.1 real, 3.28e+04 points.step/s, 85 var

14.1 real is wall-clock seconds. You should now have six files sitting next to params.json:

checkpoint.dump  logstats.dat  normf.dat  shear_stress.dat  tr_oxy.dat  vol_frac_interf.dat

See the output files reference for what each one contains.

4. Postprocess

cd ../..
uv run python scripts/postprocess.py runs/tutorial_demo/

This writes runs/tutorial_demo/results.json. Ours came out to:

{
  "kLa_10": 3448.06, "kLa_25": 1762.12, "kLa_50": 552.84,
  "kLa_inst_10": 3928.23, "kLa_inst_25": 1396.43, "kLa_inst_50": 1076.45,
  "dtmix_0.50": 0.210, "dtmix_0.75": 0.263, "dtmix_0.95": 5.469,
  "vor_mean": 1.763, "vel_rms_qss": 0.772, "kla_fit_rmse_25": 0.0077,
  "tau_95_qss": 0.00283, "tau_98_qss": 0.00327, "tau_100_qss": 0.00394,
  "tau_95_max": 0.00405, "tau_98_max": 0.00462, "tau_100_max": 0.00575,
  "tau_mean_max": 0.00194
}

5. See it

Fidelity 3 is too coarse to look at — 8×8 cells barely resolves the interface. Bumping to fidelity 5 (32×32) and using BioReactor-video instead makes the sloshing actually visible, at the cost of ~1 minute instead of ~15 seconds:

make build-video
mkdir -p runs/tutorial_video_demo
cat > runs/tutorial_video_demo/params.json <<'EOF'
{
  "run_id": "tutorial_video_demo",
  "fidelity": 5,
  "omega_b": 3.93,
  "n_harmonics": 1,
  "theta_max": [7.0, 0.0, 0.0],
  "phi_angular": [0.0, 0.0, 0.0],
  "omega_h": 0.0,
  "amplitude_h": [0.0, 0.0, 0.0],
  "phi_horizontal": [0.0, 0.0, 0.0],
  "geometry": {"a": 0.25, "b": 0.071, "n": 8.0},
  "fill_level": 0.5,
  "n_mix_cycles": 8,
  "t_end": 20.0
}
EOF
build/BioReactor-video runs/tutorial_video_demo/params.json
uv run python scripts/render_videos.py runs/tutorial_video_demo

BioReactor-video itself only dumps raw binary frames to runs/tutorial_video_demo/frames/ — render_videos.py is the separate step that actually renders and encodes them (needs ffmpeg on PATH; module load ffmpeg on OSCAR), producing volume_fraction.mp4 (body frame, rocking with the bag) and volume_fraction_lab.mp4 (lab frame, fixed camera):

Lab-frame volume-fraction animation from a real fidelity-5 run: the bag itself rocking back and forth, with the liquid (red) sloshing inside it, against a fixed camera.

This is the same VOF field that vol_frac_interf.dat records numerically — the video is just that field rendered frame by frame, nothing the solver computes differently.

There's a second, separate video pipeline

config/slurm_video_template.sh + scripts/submit_video_run.py render videos a different way — directly from Basilisk's own view/bview output via ppm2mp4, producing vorticity3.mp4/oxygen3.mp4/tracer*.mp4 instead of volume_fraction*.mp4. That path hasn't been exercised while writing this page; the steps above are the ones actually verified here.

What you just exercised

BioReactor read params.json, ran the two-phase VOF solver with Henry's-law oxygen transport, wrote its state to .dat files as it went, dumped a checkpoint.dump at the end (this matters once you get to checkpoint restart), and postprocess.py reduced those raw files down to the KPIs in results.json. Every other workflow in this project — sweeps, batch sampling, the BO loop — is this same run → postprocess step, automated and repeated.

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