Dynamics

Here we collect tutorials on the dynamic processes that can affect aerosol populations, including condensation, coagulation, and special customizations.

Condensation

These examples demonstrate bin-based and fully resolved approaches to modeling condensation, plus a CPU-only latent-heat diagnostic/reference walkthrough.

• Condensation 1: Bins
• Condensation 2: Masses Binned
• Condensation 3: Masses Resolved
• Condensation: Latent Heat Bookkeeping – Published CPU-only latent-heat bookkeeping walkthrough using MassCondensation with public factory/builder APIs. The reported energy density is diagnostic only and does not feed back into temperature. The executable CPU integration baseline remains particula/integration_tests/condensation_latent_heat_conservation_test.py.
• Staggered Condensation – Demonstrates staggered ODE stepping for improved stability and mass conservation.

Coagulation

These coagulation tutorials are maintained as Jupytext percent pairs (.py ⇄ .ipynb). Edit the .py file, then sync and execute to refresh outputs.

• Coagulation 1: PMF Pattern – Probability mass function (discrete) approach.
• Coagulation 2: PDF Pattern – Probability density function (continuous) approach.
• Coagulation 3: Particle Resolved – Particle-resolved approach with individual particle tracking.
• Coagulation 4: Methods Compared – Side-by-side strategy comparison (PMF vs PDF) using the PMF↔PDF helper.
• Coagulation 5: Time Evolution – Compare number concentration decay over time for PMF, PDF, and particle-resolved.

Functional

These illustrate functional approaches to coagulation, comparing PMF- and PDF-based methods against particle-resolved methods. All coagulation tutorials are Jupytext-paired: edit the .py, run python3 .opencode/tools/validate_notebook.py <ipynb> --sync, then python3 .opencode/tools/run_notebook.py <ipynb>.

• Coagulation 1: Probability Mass Function
• Coagulation Tutorial: Basic 2-PDF
• Coagulation Tutorial: Basic 3-Compared

Charge

Charged coagulation tutorials are Jupytext-paired (.py:percent ⇄ .ipynb). Edit the .py, then sync and execute to refresh outputs for the site.

• Coagulation with Charge (functional) – Edit the paired .py; executed outputs are present in the notebook.
• Coagulation with Charge (objects) – Edit the paired .py; executed outputs are present in the notebook.

Customization

• Adding Particles During Simulation – Demonstrates customizing the simulation by injecting new particles.