Metadata-Version: 2.1
Name: SplitFVM
Version: 0.2
Summary: 1D Finite-Volume Split Newton Solver
Home-page: https://github.com/gpavanb1/SplitFVM
Author: gpavanb1
Author-email: gpavanb@gmail.com
License: MIT
Project-URL: Bug Reports, https://github.com/gpavanb1/SplitFVM/issues
Project-URL: Source, https://github.com/gpavanb1/SplitFVM/
Description: # SplitFVM
        
        [![Downloads](https://pepy.tech/badge/splitfvm)](https://pepy.tech/project/splitfvm)
        
        ![img](https://github.com/gpavanb1/SplitFVM/blob/main/assets/logo.jpg)
        
        1D [Finite-Volume](https://en.wikipedia.org/wiki/Finite_volume_method) with [adaptive mesh refinement](https://en.wikipedia.org/wiki/Adaptive_mesh_refinement) and steady-state solver using Newton and [Split-Newton](https://github.com/gpavanb1/SplitNewton) approach
        
        ## What does 'split' mean?
        
        The system is divided into two and for ease of communication, let's refer to first set of variables as "outer" and the second as "inner".
        
        * Holding the outer variables fixed, Newton iteration is performed till convergence using the sub-Jacobian
        
        * One Newton step is performed for the outer variables with inner held fixed (using its sub-Jacobian)
        
        * This process is repeated till convergence criterion is met for the full system (same as in Newton)
        
        ## How to install and execute?
        
        Just run 
        ```
        pip install splitfvm
        ```
        
        There is an [examples](https://github.com/gpavanb1/SplitFVM/examples) folder that contains a test model - [Advection-Diffusion](https://en.wikipedia.org/wiki/Convection%E2%80%93diffusion_equation)
        
        You can define your own equations by simply creating a derived class from `Model` and adding to the `_equations` using existing or custom equations!
        
        A basic driver program is as follows
        ```
        # Define the problem
        m = AdvectionDiffusion(c=0.2, nu=0.001)
        
        # Define the domain and variables
        # ng stands for ghost cell count
        d = Domain.from_size(20, 2, ["u", "v", "w"]) # nx, ng, variables
        
        # Set IC and BC
        ics = {"u": "gaussian", "v": "rarefaction"}
        bcs = {
            "u": {
                "left": "periodic",
                "right": "periodic"
            },
            "v": {
                "left": {"dirichlet": 3},
                "right": {"dirichlet": 4}
            },
            "w": {
                "left": {"dirichlet": 2},
                "right": "periodic"
            }
        }
        s = Simulation(d, m, ics, bcs)
        
        # Advance in time or to steady state
        s.evolve(dt=0.1)
        bounds = [[-1., -2., 0.], [5., 4., 3.]]
        iter = s.steady_state(split=True, split_loc=1, bounds=bounds)
        
        # Visualize
        draw(d, "label")
        ```
        
        ## Whom to contact?
        
        Please direct your queries to [gpavanb1](http://github.com/gpavanb1)
        for any questions.
        
        ## Acknowledgements
        
        Do visit its [Finite-Difference](https://github.com/gpavanb1/SplitFDM) cousin
        
        Special thanks to [Cantera](https://github.com/Cantera/cantera) and [WENO-Scalar](https://github.com/comp-physics/WENO-scalar) for serving as an inspiration for code architecture
        
Keywords: amr newton python finite-volume armijo optimization pseudotransient splitting
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Description-Content-Type: text/markdown
