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Particula

What is Particula?

Particula is an open-source, Python-based aerosol simulator that bridges experimental data with computational models. It captures gas-particle interactions, transformations, and dynamics to power predictive aerosol science—so you can uncover deeper insights and accelerate progress.

Why Use Particula?

Aerosols influence atmospheric science, air quality, and human health in powerful ways. Gaining insight into how they behave is essential for effective pollution control, accurate cloud formation modeling, and safer indoor environments. Particula provides a robust, flexible framework to simulate, analyze, and visualize aerosol processes with precision—empowering you to make breakthroughs and drive impactful science.

How Does Particula Help You?

Whether you’re a researcher, educator, or industry expert, Particula is designed to empower your aerosol work by:

  • Harnessing ChatGPT integration for real-time guidance, troubleshooting, and Q&A, here.
  • Providing a Python-based API for reproducible and modular simulations.
  • Interrogating your experimental data to validate and expand your impact.
  • Fostering open-source collaboration to share ideas and build on each other’s work.

Join the Community

We welcome contributions from scientists, developers, and students—and anyone curious about aerosol science! Whether you’re looking to ask questions, get help, or contribute fresh ideas, you’ve come to the right place.

Get more by posting on GitHub Discussions and tag any of the contributors using @github-handle.

We’re excited to collaborate with you! ✨

Cite Particula in Your Research

Particula [Computer software]. DOI: 10.5281/zenodo.6634653

Get Started with Particula

Setup Particula API Reference Examples Theory

PyPI Installation

If your Python environment is already set up, install Particula directly from PyPI: 

pip install particula

Conda Installation

Alternatively, you can install Particula using conda: 

conda install -c conda-forge particula

If you are new to Python or plan on going through the Examples, head to Setup Particula for more comprehensive installation instructions.

Quick Start Example

This “Quick Start Example” demonstrates a concise workflow for building an aerosol system in Particula and performing a single condensation step.

import numpy as np
import particula as par

# 1. Build the GasSpecies for an organic vapor:
organic = (
    par.gas.GasSpeciesBuilder()
    .set_name("organic")
    .set_molar_mass(180e-3, "kg/mol")
    .set_vapor_pressure_strategy(
        par.gas.ConstantVaporPressureStrategy(1e2)  # Pa
    )
    .set_partitioning(True)
    .set_concentration(np.array([1e2]), "kg/m^3")
    .build()
)

# 2. Use AtmosphereBuilder to configure temperature, pressure, and species:
atmosphere = (
    par.gas.AtmosphereBuilder()
    .set_temperature(298.15, "K")
    .set_pressure(101325, "Pa")
    .set_more_partitioning_species(organic)
    .build()
)

# 3. Build the particle distribution:
#    Using PresetParticleRadiusBuilder, we set mode radius, GSD, etc.
particle = (
    par.particles.PresetParticleRadiusBuilder()
    .set_mode(np.array([100e-9]), "m")
    .set_geometric_standard_deviation(np.array([1.2]))
    .set_number_concentration(np.array([1e8]), "1/m^3")
    .set_density(1e3, "kg/m^3")
    .build()
)

# 4. Create the Aerosol combining the atmosphere and particle distribution:
aerosol = (
    par.AerosolBuilder()
    .set_atmosphere(atmosphere)
    .set_particles(particle)
    .build()
)

# 5. Define the isothermal condensation strategy:
condensation_strategy = par.dynamics.CondensationIsothermal(
    molar_mass=180e-3,  # kg/mol
    diffusion_coefficient=2e-5,  # m^2/s
    accommodation_coefficient=1.0,
)

# 6. Build the MassCondensation process:
process = par.dynamics.MassCondensation(condensation_strategy)

# 7. Execute the condensation process over 10 seconds:
result = process.execute(aerosol, time_step=10.0)

#   The result is an Aerosol instance with updated particle properties.
print(result)

Wall loss strategies

In addition to condensation and coagulation, Particula models particle loss to chamber walls. The wall loss API lives in particula.dynamics.wall_loss and is exposed through the particula.dynamics namespace.

The strategy-based wall loss API is built around two classes:

  • particula.dynamics.WallLossStrategy – abstract base class for wall loss models.
  • particula.dynamics.SphericalWallLossStrategy – spherical chamber wall loss strategy that uses existing wall loss coefficient utilities.

Strategies operate on particula.particles.representation.ParticleRepresentation instances and support "discrete", "continuous_pdf", and "particle_resolved" distribution types.

import numpy as np
import particula as par

# Build a simple particle distribution (as in the quick start above)
particle = (
    par.particles.PresetParticleRadiusBuilder()
    .set_mode(np.array([100e-9]), "m")
    .set_geometric_standard_deviation(np.array([1.2]))
    .set_number_concentration(np.array([1e8]), "1/m^3")
    .set_density(1e3, "kg/m^3")
    .build()
)

wall_loss = par.dynamics.SphericalWallLossStrategy(
    wall_eddy_diffusivity=0.001,  # m^2/s
    chamber_radius=0.5,  # m
    distribution_type="discrete",
)

# Instantaneous wall loss rate
rate = wall_loss.rate(
    particle=particle,
    temperature=298.15,
    pressure=101325.0,
)

# Apply wall loss for 10 seconds
for _ in range(10):
    particle = wall_loss.step(
        particle=particle,
        temperature=298.15,
        pressure=101325.0,
        time_step=1.0,
    )

The legacy function-based API (get_spherical_wall_loss_rate, get_rectangle_wall_loss_rate) remains available for lower-level calculations; the strategy classes provide a more consistent interface alongside condensation and coagulation strategies.