Basics of Lattice Boltzmann Method for CFD modeling

Introduction to Methods of Flow Simulations

  • Computational flow dynamic (CFD) simulations using the finite volume method have been used for many industrial applications as discussed in the post.
  • However, nowadays lattice Boltzmann method (LBM) has been widely used in micro fluidics, biomedical, and multiphase flow applications.

What are the Lattice Boltzmann Method Simulations?

  • The Lattice Boltzmann Method (LBM) is a computational fluid dynamics (CFD) technique used for simulating fluid flow and solving fluid dynamics problems.
  • It is an alternative approach to traditional CFD methods like Finite volume methods (FVM) to handle complex fluid behaviors and geometries in many mesoscopic applications.
  • Lattice Boltzmann Method (LBM)  is also another dynamic method of fluid simulations at the macroscopic behavior using a simple mesoscopic model.
  • It inherited the main principles of Lattice.
  • Gas Automaton (LGA) and made improvements. From lattice gas automaton, it is possible to derive the macroscopic Navier-Stokes equations.
  •  Lattice Boltzmann Method and its difference from the traditional macroscopic methods for numerical calculations are given below:
    • It is based on and starts from Non-equilibrium statistical mechanics and Discrete model
    •  It connected the dynamic lattice model, whose time, space, and velocity phase space are fully
      discrete, with the Boltzmann equation.
    •  The implementation of this method can describe the law of fluid motion without
      Solving Navier-Stokes equations


Comparison of scales for simulation LBM and macrosopic scale

Comparison of LBM with Finite Volume Methods  and Molecular Modeling

  • Three commonly used methods finite volume methods lattice Boltzmann methods and molecular dynamics are given  in the following tables for advantages and disadvantages
  • Each simulation technique has a different level of time, length, and velocity scales.
  • Finite Volume Method
    • CFD solvers like ANSYS FLUENT and OpenFOAM are based on the Finite volume method (FVM) as presented in the post.
    • In this method, conservation equations of mass, momentum, and energy are solved numerically at macroscopic scales. This is based on the continuum hypothesis (Eulerian approach). Molecular level properties like pressure and viscosity are modeled as molecular fluid properties like viscosity, and conductivity.
    • This method is suitable for large-scale geometries (macro-scale)  with high velocity of bulk fluid flows in engineering applications
    • The ratio of molecular to geometry size (Knudesn number) is less than 0.001

Different scales for simulation LBM and macrosopic scale

  • Lattice Boltzmann Methods

    • LBM models the fluid consisting of fictive particles, and such particles perform consecutive propagation and collision processes over a discrete lattice
    • This method is suitable for micro-scale (mesoscale) geometries with small velocities of bulk fluid flows at a mesoscopic level in biomedical applications
  • Molecular dynamics (MD) simulations
    • Molecular dynamics (MD) simulations is used to predict every atom in a protein or other molecular system.
    • This method is  based on a general model of the physics governing interatomic interactions
    • This method is suitable for micro and nano-scale geometries with less velocity of molecular flows


Comparison of simulation at molecular level, LBM and finite volume method
Comparison of simulation at molecular level, LBM and finite volume method



Overview of LBM

  • Here’s a brief overview of the Lattice Boltzmann Method:
  • Discretization of Space and Time:
    • LBM operates on a discrete lattice grid, typically in a regular arrangement. Each lattice node represents a discrete point in space.
    • Time is also discretized into discrete time steps.
    • For the  Lattice Boltzmann Method (LBM), the lattice grid plays a central role as it discretizes the space where fluid flow (refer to the following figure (a, b))
    • This method used for simulations  at mesoscopic levels


Lattice grid for LBM


  • Particle Distribution Functions:
    • LBM models the fluid as a collection of fictitious particles, and it uses a set of distribution functions to represent the probability of particles moving in specific directions at each lattice node.
    • In a D2Q9 lattice (common in 2D simulations), there are nine distribution functions at each node, corresponding to nine discrete velocity directions.

Lattice Boltzmann Method functions in 2D and 3D

  • Collision and Streaming Steps:
    • LBM consists of two main steps: the collision step and the streaming step.
    • During the collision step, the distribution functions are modified based on collision rules. These rules account for particle interactions and interactions between neighboring nodes.
    • In the streaming step, particles are moved to neighboring lattice nodes based on their velocity. This step simulates the advection of particles in the fluid.
  • Macroscopic Properties:
    • From the distribution functions, macroscopic fluid properties such as density and velocity are calculated at each lattice node.
    • These properties can be used to visualize and analyze fluid flow and other fluid characteristics.
  • Boundary Conditions:
    • LBM handles boundary conditions differently from traditional methods. Various techniques, such as bounce-back rules, are used to account for solid boundaries and other boundary conditions within the lattice framework.

Lattice grid for Lattice Boltzmann Method

Lattice Grid for Lattice Boltzmann Method

  • In the Lattice Boltzmann Method (LBM), the lattice grid plays a central role as it discretizes the space where fluid flow and other phenomena are simulated.
  • The choice of lattice grid is an important consideration in LBM simulations, and different lattice structures can be used depending on the specific problem being studied. Here are some common lattice grids used in LBM:
  1. D2Q9 Lattice (Two Dimensions, Nine Velocities):
    • Dimensions: 2D grid.
    • Velocities: Nine discrete velocity vectors. The lattice structure often looks like a square grid with a central node and eight surrounding nodes.
    • Commonly used for 2D simulations of fluid flow.
  2. D3Q19 Lattice (Three Dimensions, Nineteen Velocities):
    • Dimensions: 3D grid.
    • Velocities: Nineteen discrete velocity vectors. The lattice structure is more complex than D2Q9 and is often cubic or cubic-centered.
    • Suitable for 3D simulations of fluid flow.
  3. D3Q27 Lattice (Three Dimensions, Twenty-Seven Velocities):
    • Dimensions: 3D grid.
    • Velocities: Twenty-seven discrete velocity vectors. This lattice structure provides higher resolution and accuracy for 3D simulations.
    • Used when finer resolution is needed in 3D simulations, particularly for complex geometries.
  4. Hexagonal Lattice:
    • Dimensions: 2D grid.
    • Velocities: Six discrete velocity vectors arranged in a hexagonal pattern.
    • Commonly used for simulating flow in hexagonal-packed porous media and other problems with hexagonal symmetry.
  5. Triangular Lattice:
    • Dimensions: 2D grid.
    • Velocities: Three discrete velocity vectors arranged in a triangular pattern.
    • Used in simulations involving triangular-packed porous media and other applications with triangular symmetry.
  6. Body-Centered Cubic (BCC) Lattice:
    • Dimensions: 3D grid.
    • Velocities: Thirteen discrete velocity vectors arranged in a body-centered cubic lattice.
    • Suitable for simulating multiphase flows and other problems with cubic symmetry.
  7. Face-Centered Cubic (FCC) Lattice:
    • Dimensions: 3D grid.
    • Velocities: Fifteen discrete velocity vectors arranged in a face-centered cubic lattice.
    • Useful for multiphase flow simulations and problems with cubic symmetry.
  8. Lattices for Multiphase and Multi-component Flows:
    • Specialized lattices may be designed for multiphase or multicomponent flow simulations, allowing for the modeling of interfacial phenomena, phase separation, and mass transfer.
  • The choice of lattice grid depends on several factors, including the dimensionality of the problem, the desired level of resolution, and the specific physical phenomena being simulated.
  • In practice, researchers select a lattice structure that best matches the problem’s characteristics and computational requirements.
  • The lattice structure and the discrete velocity vectors associated with it are essential components of LBM, as they determine the numerical accuracy and stability of the simulation.
  • Researchers often choose lattice structures that strike a balance between computational efficiency and accuracy for their specific application.


Lattice Functions

  • These are fundamental equations and concepts used in the Lattice Boltzmann Method.
  • The specific details and formulations can vary depending on the specific LBM variant used and the problem being simulated (e.g., incompressible flow, thermal LBM, multiphase LBM, etc.).
  • The choice of lattice structure, collision model, and boundary conditions can also influence the equations used in LBM simulations.
  • In LBM, the Boltzmann equation is discretized to model fluid flow on a lattice grid.
  • You can refer books for more details of mathematical functions for this methods. Here are some key formulas and concepts used in the Lattice Boltzmann Method:

Distribution Functions:

  • LBM represents the probability distribution of particles at discrete velocity vectors in each lattice direction.
  • These distribution functions are typically denoted function represents a specific velocity direction.
  • In a D2Q9 lattice, there are nine distribution functions

Equilibrium Distribution Functions:

  • Equilibrium distribution functions () are calculated based on local macroscopic properties, such as density () and velocity ().
  • They are used to model the particles’ relaxation toward equilibrium in the collision step of LBM.

Streaming Step:

  •  In the streaming step, particles are moved to neighboring lattice nodes based on their velocity vectors:

Macroscopic Properties:

  • Macroscopic properties, such as density () and velocity (), are calculated from the distribution functions

Boundary Conditions:

  • Boundary conditions are applied at the domain boundaries to ensure that particles interact correctly with solid walls or other boundary types.
  • Common boundary conditions include the bounce-back rule for no-slip walls

Lattice Boltzmann Equation (Boltzmann BGK Equation):

  • The Lattice Boltzmann equation is a simplified form of the Boltzmann equation:

Steps in Lattice Boltzmann Method (LBM) 

The Lattice Boltzmann Method (LBM) is a computational fluid dynamics (CFD) technique that simulates fluid flow by modeling the behavior of particles on a lattice grid. Here are the main steps involved in performing CFD simulations using LBM:


  • Define the computational domain: Specify the physical dimensions of the simulation domain, including its size and geometry.
  • Set up the lattice grid: Choose a lattice structure (e.g., D2Q9 for 2D or D3Q27 for 3D simulations) and establish the grid resolution (spacing between lattice nodes).
  • Initialize fluid properties: Assign initial values for fluid density and velocity at each lattice node.
  • You can also set the initial conditions for other scalar quantities like temperature or concentration if they are part of the simulation.
  • Specify boundary conditions: Define the boundary conditions at the domain boundaries, which can include no-slip walls, inflow, outflow, and other conditions relevant to the problem.

Time Stepping:

  • Select a time step size: Determine an appropriate time step for advancing the simulation in discrete time increments.
  • The choice of time step affects stability and accuracy.
  • Set the total simulation time: Define the duration for which the simulation will run or specify stopping criteria.

Collision Step:

  • Calculate equilibrium distribution functions: Based on the macroscopic properties (density, velocity, temperature, etc.) at each lattice node, compute the equilibrium distribution functions using a suitable collision model.
  • Perform collision: Update the distribution functions by applying collision processes. The collision step incorporates relaxation towards the local equilibrium distribution functions.

Streaming Step:

  • Move particles: Displace particles by streaming them to neighboring lattice nodes based on their velocity. This step represents the advection of particles in the fluid.

Boundary Conditions:

  • Apply boundary conditions: Implement the defined boundary conditions at the domain boundaries.
  • Commonly used boundary conditions include bounce-back (no-slip), inflow/outflow conditions, and periodic boundaries.
  • Handle special boundaries: Adapt the boundary treatment for specific scenarios or complex geometries.
  • For instance, modeling porous media may require special boundary conditions.

Macroscopic Property Calculation:

  • Calculate macroscopic properties
  • Compute macroscopic properties such as fluid density, velocity, and temperature at each lattice node based on the updated distribution functions.

Time Advancement:

  • Advance the simulation time: Move to the next time step by repeating steps 3 to 6.
  • Continue iterating until the simulation reaches the desired time or satisfies predefined convergence criteria.

Visualization and Analysis:

  • Visualize results: Use the computed macroscopic properties to visualize and analyze fluid flow patterns, temperature distribution, pressure, or other relevant data.
  • Post-processing: Perform additional post-processing and analysis, such as calculating forces, heat transfer rates, or other quantities of interest.

Termination and Output:

  • Stop the simulation: Terminate the simulation when it reaches the desired time or satisfies convergence criteria.
  • Save results: Save simulation results and data for further analysis or documentation.

Parallelization (Optional):

  • Implement parallel computing techniques to distribute the computational workload across multiple processors or nodes, enhancing simulation efficiency for large-scale problems.

Adaptation and Optimization (Optional):

  • Continuously adjust simulation parameters (e.g., relaxation time, grid resolution) to optimize accuracy and efficiency based on problem-specific requirements.

These steps provide a general outline of the Lattice Boltzmann Method for CFD simulations. The details and complexities of the implementation may vary depending on the specific problem being solved and the software or code used for the simulation.


Applications of Lattice Boltzmann Method (LBM)

The Lattice Boltzmann Method (LBM) is a computational fluid dynamics (CFD) technique used for simulating fluid flow and solving fluid dynamics problems.

The Lattice Boltzmann Method (LBM) has found applications in a wide range of fields due to its versatility and ability to handle complex fluid behaviors and geometries. Some of the notable applications of LBM include:

  1. Fluid Flow Simulations
    • LBM can be used for fluid flows of l fluid dynamics, including sediment transport in rivers, pollutant dispersion in air and water, and ocean circulation
    • Incompressible and Compressible Flows:
  2. Heat Transfer simulations
  3. Micro fluid flow
    • LBM is used to model fluid flow in micro-channels and microdevices, which is important in fields like lab-on-a-chip technology, micro-fluidic mixing, and biofluidics.
  4. Porosity and Permeability:
    • LBM is applied to study fluid flow through porous media, making it useful in hydrogeology, petroleum engineering, and geophysics for modeling groundwater flow, oil reservoirs, and subsurface processes.
  5. Blood Flow and Cardiovascular Modeling:
    • LBM is used to simulate blood flow in arteries and veins, aiding in the understanding of hemodynamics, the study of blood vessel diseases, and the design of medical devices such as stents.
  6. Multiphase flows:.
    • Bubble flow modeling
    • Particle-Laden Flows
  7. Bio-fluid Dynamics:
    • LBM is used in biomechanics and bioengineering to simulate the flow of fluids in biological systems, such as airflow in the respiratory system, cerebrospinal fluid flow in the brain, and blood flow in the heart.
  8. Material Science:
    • LBM can simulate the flow of complex fluids, including colloidal suspensions and polymer melts, aiding in the design of materials with desired properties.
  9. Non-Newtonian Flows:
    • LBM can model non-Newtonian fluids like viscoelastic fluids, which are encountered in applications such as polymer processing and food industry processes.
  10. Environmental Impact Studies:
    • LBM is used to assess the environmental impact of various projects, including the dispersion of pollutants in urban areas and the modeling of water quality in lakes and rivers..
  11. Nano-fluidics:
    • LBM is applied to study fluid flow at the nanoscale, which is relevant in nanotechnology and the design of nanofluidic devices.

Fluid Dynamics Simulations

  • LBM is extensively used to simulate fluid flow in various settings, such as flow over aerodynamic shapes, flow through pipes and channels, and flow around complex geometries.
  • It can handle both laminar and turbulent flows.

Multiphase Flows:

  • LBM is well-suited for modeling multiphase flows, including applications like bubble dynamics, droplet coalescence and breakup, and oil-water separation.
  • The Lattice Boltzmann Method (LBM) is a powerful computational fluid dynamics (CFD) technique for modeling multiphase flows. It is particularly well-suited for simulating complex multiphase phenomena, including two-phase and three-phase flows. Here’s how LBM can be applied to multiphase flow modeling:

Two-Phase Flows:

  • LBM can effectively model two-phase flows, where two immiscible fluids, such as oil and water, coexist within the same domain.
  • To model bubble floes, CFD solvers based on interface capturing methods  like volume of fluid methods (VOF) computationally expensive compared to LBM solver as presented in the post of basics if multiphase modeling.
  • Numerical solvers need to be validated with experimental data for credibility of numerical models.
  • Key aspects of two-phase flow modeling with LBM include the following approaches in modeling of two phase flows:
  • Interfacial Tracking:
    • LBM is capable of tracking fluid interfaces explicitly. This involves representing the interface between the two fluids on the lattice grid.
  • Surface Tension:
    • Surface tension effects at the fluid interface can be incorporated into LBM simulations through additional terms in the collision step.
  • Contact Line Dynamics
    •  LBM can simulate contact line dynamics, which is crucial in modeling phenomena like capillary flows, wetting, and droplet coalescence and breakup.
  • Boundary Conditions:
    • Proper boundary conditions, including the implementation of a ‘no-slip’ condition at solid-fluid interfaces, are essential for accurate two-phase flow simulations.

Three-Phase Flows:

  • LBM can also be extended to simulate three-phase flows, where three immiscible fluids interact. This is particularly useful in applications involving interfaces with complex topologies.
  • LBM’s capabilities include:
    • Multiple Interfaces:
      • LBM can handle the presence of multiple interfaces, such as triple junctions where three fluids meet.
    • Contact Line Behavior:
      • Similar to two-phase flows, LBM can model contact line behavior in three-phase flows, including the movement and interaction of the fluid-fluid-solid contact lines.

Multiphase Flow Regimes:

LBM can simulate various multiphase flow regimes, including:

    • Droplet Dynamics: Modeling the motion, deformation, and breakup of droplets in a continuous phase.
    • Bubble Dynamics: Simulating the behavior of gas bubbles in liquid, relevant in applications like boiling and bubble column reactors.
    • Immiscible Flow: Studying the flow of immiscible fluids with different properties, such as density and viscosity.

Solid-Fluid Interactions:

LBM allows for the modeling of solid-fluid interactions, which is important when simulating multiphase flows in porous media or around solid objects. This includes the use of bounce-back boundary conditions.

Complex Geometries:

  • LBM’s flexibility in handling complex geometries and boundary conditions makes it suitable for modeling multiphase flows in intricate domains, including porous media, microfluidic devices, and porous media.

Applications for multiphase flows

  • Multiphase flow modeling with LBM finds applications in various fields
  • Petroleum Engineering (oil-water flows),
  • Chemical engineering (reactors and mixers),
  • Environmental science (contaminant transport),
  • Biomedical Engineering (blood flow and tissue perfusion).


  • While LBM is powerful for multiphase flow simulations, it can be computationally intensive, particularly when simulating fine-scale interfaces or high-density ratios.
  • Proper tuning of parameters and mesh resolution is essential for accurate results.
  • Overall, LBM offers a promising approach for multiphase flow modeling, thanks to its ability to handle complex interfaces, versatile boundary conditions, and parallel computing capabilities.
  • Researchers and engineers often use LBM to gain insights into multiphase flow phenomena and optimize processes in various industrial and scientific applications

Heat Transfer Modeling

  • LBM is used to study conduction, convection, and radiation heat transfer in various systems, including heat exchangers, electronics cooling, and natural convection.
  • The Lattice Boltzmann Method (LBM) is a versatile computational fluid dynamics (CFD) technique that can be effectively used for simulating heat transfer processes. It can model a wide range of heat transfer phenomena, including conduction, convection, and radiation. Here’s how LBM can be applied to heat transfer simulations:


  • LBM can simulate heat conduction within a solid or fluid domain. It does this by tracking the temperature distribution across the lattice nodes. Key considerations for using LBM for heat conduction include:
  • Thermal Relaxation:
    • LBM uses distribution functions to represent particle velocities and includes additional distribution functions for temperature.
    • The collision step in LBM includes relaxation processes that allow temperature to equilibrate within the lattice.
  • Lattice Specifics:
    • The choice of lattice structure and the thermal relaxation time parameter can affect the accuracy and stability of conduction simulations.
    • A proper lattice and relaxation time should be chosen based on the specific problem and material properties.
  • Boundary Conditions:
    • Appropriate boundary conditions, such as temperature boundary conditions (e.g., constant temperature, heat flux, or mixed boundary conditions),
    • Boundary conditions must be applied to model heat conduction at domain boundaries.


  • LBM can handle convective heat transfer, where heat is transferred by the movement of the fluid itself. The modeling of convection includes:
  • Advection of Temperature:
    • Just like LBM simulates fluid flow by streaming particles, it can also simulate the advection of temperature.
    • This involves propagating temperature information in accordance with the fluid velocity field.
  • Buoyancy
    • LBM can simulate natural convection, where fluid motion is driven by temperature gradients and buoyancy effects.
    • Appropriate boundary conditions and initial conditions are essential in these simulations.

Radiation Heat Transfer

  • LBM can be coupled with radiation models to simulate radiative heat transfer in participating media. Key considerations include:
  • Radiative Transfer Equation (RTE):
    • The RTE describes how radiation energy is transported through a participating medium.
    • LBM can be coupled with RTE solvers to compute radiative heat transfer within the domain.
  • Scattering and Absorption:
    • LBM can model scattering and absorption of radiation within the medium by considering the absorption and scattering properties of the participating media.

Multi-physics Simulations:

  • LBM can be integrated into multi-physics simulations where multiple heat transfer mechanisms are present simultaneously.
  • For instance, it can model heat transfer in conjunction with fluid flow and solid-solid conduction.
  • Complex Geometries:One of LBM’s strengths is its ability to handle complex geometries and boundary conditions, making it well-suited for simulating heat transfer in intricate domains.
  • Transient Heat Transfer:LBM is capable of simulating transient heat transfer processes, allowing for the investigation of temperature changes over time.


  • LBM can be applied to various heat transfer applications, including electronics cooling, HVAC system design, combustion simulations, and materials processing.
  • While LBM offers several advantages for heat transfer simulations, such as its parallelism and ability to handle complex geometries
  • CFD users should be aware of the specific lattice structure, boundary conditions, and relaxation parameters needed to accurately model heat transfer phenomena.
  • Validation and verification against experimental data or analytical solutions are important to ensure the accuracy and reliability of LBM-based heat transfer simulations.

Turbulence Flow Simulation

  • LBM can be employed for turbulence modeling, including simulating turbulent flows and studying turbulence characteristics such as eddy structures and energy spectra.
  • The Lattice Boltzmann Method (LBM) can be used for turbulence modeling, although it is not as widely adopted as traditional turbulence modeling methods like Large Eddy Simulation (LES) or Reynolds-Averaged Navier-Stokes (RANS) models.
  • Nevertheless, LBM has some advantages and unique characteristics that make it a promising approach for simulating turbulent flows in certain scenarios.
  • Here’s how LBM can be applied to turbulence modeling in following various approaches if turbulence modeling with

Direct Numerical Simulation (DNS)

  • DNS is a technique where all scales of turbulence are directly resolved without any turbulence modeling. LBM can be used for DNS of turbulent flows in specific cases where it is computationally efficient.
  • The finer grid resolution required for DNS can make LBM computationally demanding, limiting its applicability to relatively simple geometries and lower Reynolds number flows.

Large Eddy Simulations

  • LBM can be used as the underlying method for LES, a popular approach for simulating turbulent flows. In LES, large-scale turbulent structures are directly resolved, while smaller-scale turbulence is modeled.
  • LBM’s ability to handle complex geometries and boundary conditions makes it suitable for LES in scenarios with intricate flow domains.
  • Filtered Lattice Boltzmann Method (FLBM):
    • FLBM is an extension of LBM designed for LES. In FLBM, a filter operation is applied to the lattice distribution functions to separate large-scale and small-scale flow components.
    • FLBM can capture large-scale turbulent structures while minimizing numerical dissipation for smaller scales, improving the accuracy of LES simulations.
  • Turbulence Modeling with Moment-Based LBM:
    • Some researchers have developed moment-based LBM models that directly model turbulence moments, such as the Reynolds stress tensor.
    • These models are inspired by traditional RANS models.
    • Moment-based LBM can be used to simulate turbulence in scenarios where capturing turbulence statistics is more important than resolving individual turbulent structures.

Advantages of LBM for Turbulence:

  • LBM’s inherent parallelism makes it well-suited for simulating turbulence on high-performance computing clusters.
  • LBM naturally accommodates complex geometries and boundary conditions, allowing for accurate simulations in realistic flow scenarios.
  • The particle-based nature of LBM may offer unique insights into turbulence phenomena and interactions.

Challenges for High-Speed Flow Modeling

    • LBM can be computationally expensive for high Reynolds number flows and fine grid resolutions, limiting its application in certain turbulent flow scenarios.
    • Developing accurate and efficient turbulence models within the LBM framework is an ongoing research challenge.


Microfluidics Simulations

  • The Lattice Boltzmann Method (LBM) is a powerful computational technique for simulating fluid flow at the microscale, making it particularly well-suited for microfluidics applications.
  • Microfluidics deals with the manipulation and control of small volumes of fluids in microscale devices, and LBM offers several advantages in this context.
  • Here’s how LBM is applied in microfluidics:

Handling Small Length Scales:

  • Microfluidics involves fluid flow in channels and devices with dimensions on the order of micrometers.
  • LBM’s ability to handle fine grids and complex geometries makes it suitable for simulating flow at these small length scales.

Lattice Resolution:

  • LBM allows for the precise control of grid resolution, which is crucial for accurately capturing flow phenomena in microfluidic devices.
  • Fine lattice resolutions can be used to resolve fluid-structure interactions and boundary effects.

Complex Microchannel flow

  • Microfluidic devices often have intricate geometries, such as microchannels, T-junctions, and chambers.
  • LBM can easily handle these complex geometries and simulate fluid flow in microfluidic networks.

Multiphase Flows:

  • LBM can model multiphase flows, which are common in microfluidics.
  • It can simulate the behavior of immiscible fluids, droplet formation, and coalescence in microfluidic devices.
LBM for melting and solidification
LBM for melting and solidification

Capillary Flow and Wetting:

  • LBM can accurately capture capillary flow and wetting phenomena that are essential in microfluidics, such as flow in porous media, surface tension-driven flows, and wetting dynamics on microstructured surfaces.

Transport and Mixing:

LBM can be used to study mass transport and mixing processes in microfluidic channels, which are important in applications like lab-on-a-chip devices and microreactors.

Micro-Heat Transfer:

  • LBM can be extended to simulate heat transfer in microfluidic devices, including conduction, convection, and radiation heat transfer. This is crucial for applications involving thermal control or microscale chemical reactions.

Particle-Laden Flows:

  • LBM can model particle transport and dispersion in microfluidics, which is useful in applications like particle sorting and lab-on-a-chip systems.

Biomicrofluidics and Biofluidics

LBM is applied in microfluidics and biofluids to simulate the flow of biological fluids (e.g., blood) in microchannels and to study phenomena like cell motion and interactions.

Basic of LBM and its applications
Basic of LBM and its applications


Optimization and Design:

  • LBM can aid in the design and optimization of microfluidic devices by providing insights into fluid behavior and performance, helping engineers and researchers improve device efficiency and functionality.

Scaling Laws:

  • LBM can help establish scaling laws for fluid flow in microfluidic systems, allowing researchers to predict and optimize performance based on device dimensions and operating conditions.

Numerical Experiments:

  • LBM serves as a virtual laboratory for conducting numerical experiments, enabling researchers to explore and understand fluid behavior in microfluidic environments before physical prototypes are built.

LBM in Commercial  Multi physics Solvers

  • COMSOL Multiphysics is a powerful finite element analysis (FEA) and multi-physics simulation software that is widely used for simulating a wide range of physical phenomena.
  • While COMSOL primarily uses finite element methods (FEM) as the numerical technique for solving partial differential equations, it does have some capabilities for implementing the Lattice Boltzmann Method (LBM) as well.
  • This method can handle complex geometries and is often used for simulating flows in porous media, multiphase flows, and other scenarios.
  • To implement the Lattice Boltzmann Method in COMSOL Multiphysics, you would typically use the “User Defined PDE” feature and write the governing equations for your specific LBM model.

LBM simulation in COMSOL

Here are the basic steps to set up an LBM simulation in COMSOL:

  1. Define the Lattice:
    • Choose the lattice structure (e.g., D2Q9, D3Q19) based on the specific LBM model you are using.
  2. Define Lattice Boltzmann Equations:
    • In COMSOL, you would need to set up the LBM equations within a “User Defined PDE” module.
    • This typically involves defining the collision operator and streaming step for the distribution functions.
  3. Boundary Conditions:
    • Specify appropriate boundary conditions for your problem.
    • This might include no-slip boundary conditions for solid walls, inflow/outflow conditions,
    • There may be specific conditions relevant to your application.
  4. Initial Conditions: Set the initial conditions for your LBM simulation.
  5. Time Stepping:
    • Choose an appropriate time-stepping scheme, like the single relaxation time (SRT) or multiple relaxation time (MRT) scheme, and set the time step.
  6. Post-Processing:
    1. Define the quantities you want to monitor or visualize in the post-processing step. You can create plots, animations, and other output data as needed.
  7. Mesh: Generate an appropriate mesh for your simulation domain.
  8. Simulation: Run the simulation using the settings you’ve defined.
  9. Post-processing of data

LBM Other Softwares

  • It’s important to note that implementing LBM in COMSOL may require some level of expertise in both the LBM method and COMSOL Multiphysics.
  • Be sure to consult COMSOL’s documentation and, if possible, work with experts in the field to set up and validate your simulations.
  • You can check the official COMSOL website or contact their customer support to see if LBM has been integrated into the software or if there are any third-party add-ons or user contributions that enable LBM simulations within the platform.
  • If COMSOL Multiphysics still does not support LBM, you may need to explore dedicated CFD software that offers LBM capabilities, such as Palabos, OpenLB, or other LBM-specific packages.
  • You can then import the results from these simulations into COMSOL if necessary for further multi-physics analysis.
  • Always make sure to verify the most up-to-date information and features available in your software of choice, as developments and updates may have occurred since my last knowledge update.


  • Overall, LBM is a versatile and powerful tool for simulating fluid flows, particularly in scenarios where traditional methods face limitations or computational challenges.
  • It has contributed to advancements in the understanding of fluid dynamics in various scientific and engineering applications.
  • LBM is a valuable tool for simulating and understanding fluid flow and transport phenomena in microfluidic systems.
  • Its ability to handle small-length scales, complex geometries, and multiphase flows makes it well-suited for studying and optimizing microfluidic devices and processes across various scientific and engineering disciplines.


  1. Guo, Zhaoli, and Shu, Chien-Ting., Lattice Boltzmann Method and Its Applications in Engineering,  (2013)
  2. Rothman, Daniel H. and Zaleski, Stéphane, Lattice Boltzmann Modeling: An Introduction for Geoscientists and Engineers. (1997)
  3. Succi, Sauro, The Lattice Boltzmann Equation for Fluid Dynamics and Beyond,. (2001)
  4. Mohamad, A. A, Lattice Boltzmann Method and Its Applications, (2013)
  5. M.E. Kutay, LBM Basics lecture notes
  6. Comsol User Guide for Molecular Dynamics Solver, 2020
  7. Comsol for LBM Simulation, 2022

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