Skip to main content

A hybrid simulator for closed channel-based microfluidic devices

Project description

MMFT Hybrid Simulator

License: GPL v3 Ubuntu-build MacOS-build

A Hybrid Simulator for Microfluidic Devices developed by the Chair for Design Automation at the Technical University of Munich as part of the Munich MicroFluidic Toolkit (MMFT). This simulator exploits the Modified Nodal Analysis (which is a simulation method on a high level of abstraction) to accelerate CFD simulations (using the LBM as implemented in the OpenLB library) of microfluidic devices.

For more information about our work on Microfluidics, please visit https://www.cda.cit.tum.de/research/microfluidics/.

If you have any questions, feel free to contact us via microfluidics.cda@xcit.tum.de or by creating an issue on GitHub.

System Requirements

The implementation should be compatible with any current C++ compiler supporting C++17 and a minimum CMake version 3.21. The python package requires Python version 3.7 or newer. The package is currently tested for Linux distributions.

Usage

C++

To use this library, include the following code in your cmake file:

include(FetchContent)
FetchContent_Declare(
    hybridsim
    GIT_REPOSITORY https://github.com/cda-tum/mmft-hybrid-simulator.git
    GIT_TAG master
)
FetchContent_MakeAvailable(hybridsim)

target_link_libraries(${TARGET} PRIVATE hybridsim)

and include the library API header in your project file:

#include <baseSimulator.h>
#include <baseSimulator.hh>

Python

Install the python package

pip install mmft.hybridsim

and import the hybrid simulator in your code

from mmft import hybridsim

Example

To use the hybrid simulator, the network must be defined in a Network.JSON file. A network is defined as a set of Nodes, Channels and CFD Modules.

A Node contains the x and y position on a Cartesian coordinate system, where the origin is the bottom-left corner of the microfluidic device:

{
    "iD": 1,
    "x": 2e-3,
    "y": 1e-3
}

A Channel connects two nodes (nA and nB) and has a width and a height:

{
    "iD": 1,
    "nA": 1,
    "nB": 2,
    "width": 1e-4,
    "height": 1e-4
}

A CFD Module is defined with type "LBM" and contains paramaters for the LBM solver instance and information on the geometry of the CFD instance:

{
    "iD": 0,
    "Type":"LBM",
    "name": "Test1-cross-0",
    "stlFile": "/path/to/cross.stl",
    "charPhysLength": 1e-4,
    "charPhysVelocity": 1e-2,
    "alpha": 0.1,
    "resolution": 20,
    "epsilon": 0.5,
    "posX": 3.75e-3,
    "posY": 0.75e-3,
    "sizeX": 5e-4,
    "sizeY": 5e-4,
    "Openings":
    [
        {
            "nodeId": 4,
            "normalX": 1.0,
            "normalY": 0.0,
            "width": 1e-4
        },
        {
            "nodeId": 8,
            "normalX": 0.0,
            "normalY": -1.0,
            "width": 1e-4
        },
        {
            "nodeId": 9,
            "normalX": 0.0,
            "normalY": 1.0,
            "width": 1e-4
        },
        {
            "nodeId": 10,
            "normalX": -1.0,
            "normalY": 0.0,
            "width": 1e-4
        }
    ]
}

Most importantly, the geometry of the CFD Module is described by a .STL file. The in-/outflow boundaries of the CFD Module are described by the Openings. Each opening is coupled to a single Node (located in the middle of the opening), has a normal direction and a width.

Examples of networks can be found in the examples folder.

C++

The simulation case is defined in main.cpp. An example of a simulation case in c++ is given here:

#include <iostream>

#include <baseSimulator.h>
#include <baseSimulator.hh>

using T = double;

int main(int argc, char const* argv []) {

    // New simulation object
    std::cout << "[Main] Create simulation object..." << std::endl;
    sim::Simulation<T> simulation = sim::Simulation<T>();

    // Load and set the network from a JSON file
    std::cout << "[Main] Load the JSON network..." << std::endl;
    std::string file = "/path/to/Network.JSON";
    arch::Network<T>* network = new arch::Network<T>(file);
    simulation.setNetwork(network);

    // Add Pressure and/or Flow Rate Pumps
    std::cout << "[Main] Add pressure and Flow rate pumps..." << std::endl;
    network->setPressurePump(0, T(1e3));

    // Define and set the continuous phase fluid
    std::cout << "[Main] Set the continuous phase fluid..." << std::endl;
    sim::Fluid<T>* fluid = new sim::Fluid<T>(0, T(1000), T(1e-3));
    fluid->setName("Water");
    simulation.setContinuousPhase(fluid);

    // Define and set the resistance model
    std::cout << "[Main] Set the resistance model..." << std::endl;
    sim::ResistanceModel1D<T>* resistanceModel = new sim::ResistanceModel1D<T>(fluid->getViscosity());
    simulation.setResistanceModel(resistanceModel);

    // Perform simulation and store results
    std::cout << "[Main] Simulation..." << std::endl;
    simulation.simulate();

    // Print the results
    std::cout << "[Main] Results..." << std::endl;
    simulation.printResults();

    return 0;
}

Python

The simulation case can be defined once the mmft.hybridsim package is installed. An example for a simulation case in python is given here:

from mmft import hybridsim

# New simulation object
simulation = hybridsim.Simulation()

# Load and set the network from a JSON file
network = hybridsim.Network("/path/to/Network.JSON")
simulation.setNetwork(network)

# Add Pressure and/or Flow Rate Pumps
network.setPressurePump(0, 1e3)

# Define and set the continuous phase fluid
fluid = hybridsim.Fluid(0, 1000, 1e-3)
simulation.setContinuousPhase(fluid)

# Define and set the resistance model
resistanceModel = hybridsim.ResistanceModel(fluid.getViscosity())
simulation.setResistanceModel(resistanceModel)

# Perform simulation and store results
simulation.simulate()

# Print the results
simulation.print()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mmft.hybridsim-0.1.1rc1.tar.gz (920.5 kB view details)

Uploaded Source

Built Distributions

mmft.hybridsim-0.1.1rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (522.6 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

mmft.hybridsim-0.1.1rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (522.7 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

mmft.hybridsim-0.1.1rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (522.8 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

mmft.hybridsim-0.1.1rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (522.3 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

File details

Details for the file mmft.hybridsim-0.1.1rc1.tar.gz.

File metadata

  • Download URL: mmft.hybridsim-0.1.1rc1.tar.gz
  • Upload date:
  • Size: 920.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for mmft.hybridsim-0.1.1rc1.tar.gz
Algorithm Hash digest
SHA256 1d83c63aced872fbb39e1eb97ccea6a81ce4387cb4b20066422599916d0dd453
MD5 9ddf3749ebefb33ed8eba02539458ee0
BLAKE2b-256 1e116b171ff0c4cdc46271ad042fefc9b35292fa65eeb67de1dbeca61656b40a

See more details on using hashes here.

File details

Details for the file mmft.hybridsim-0.1.1rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for mmft.hybridsim-0.1.1rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 85eb29d2e370699f4fc936a4a479b779a751ea6f7e67f1ee4bbecf2211a7defa
MD5 bf70e112da0b44c8c3283ef841bd5159
BLAKE2b-256 3171ddc24ef383c61f0d71add273ba3ea3337a49d48366b32c6b184ca1df9a1e

See more details on using hashes here.

File details

Details for the file mmft.hybridsim-0.1.1rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for mmft.hybridsim-0.1.1rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b864de930ca53a38a346a47d472fd1b58f3cfba98542a67e5dd08da851bd315b
MD5 7226fe22d27b49de6dce12993414686a
BLAKE2b-256 a0a6a92206fa2d6c53f08ca8384de8e276077c1b2c21c00c5cd6a2ae56c60d38

See more details on using hashes here.

File details

Details for the file mmft.hybridsim-0.1.1rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for mmft.hybridsim-0.1.1rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8a8ccfdc884cc8e9d90fb052ef0b0916d4eff19b018842e5e289a3136cf48361
MD5 78c6d2da91c81a813f6e224533705de8
BLAKE2b-256 0f069fe93dc98a38780fb1f342eaeed8592a9b07ac54a29494de40ebc16c77f1

See more details on using hashes here.

File details

Details for the file mmft.hybridsim-0.1.1rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for mmft.hybridsim-0.1.1rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 50cb1ffe39f2473cc4db24e7f72bddfc367b0df9766124d627a84ed72a78cc4a
MD5 a562feb4342819ee5041b373db4fc8e2
BLAKE2b-256 38e0bedf656d4aea6d43aa51c63d50296b69c6d47df2a26b62460c6a8d772b88

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page