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    K-Dense-AI

    fluidsim

    K-Dense-AI/fluidsim
    Research
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    SKILL.md

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    About

    Framework for computational fluid dynamics simulations using Python...

    SKILL.md

    FluidSim

    Overview

    FluidSim is an object-oriented Python framework for high-performance computational fluid dynamics (CFD) simulations. It provides solvers for periodic-domain equations using pseudospectral methods with FFT, delivering performance comparable to Fortran/C++ while maintaining Python's ease of use.

    Key strengths:

    • Multiple solvers: 2D/3D Navier-Stokes, shallow water, stratified flows
    • High performance: Pythran/Transonic compilation, MPI parallelization
    • Complete workflow: Parameter configuration, simulation execution, output analysis
    • Interactive analysis: Python-based post-processing and visualization

    Core Capabilities

    1. Installation and Setup

    Install fluidsim using uv with appropriate feature flags:

    # Basic installation
    uv pip install fluidsim
    
    # With FFT support (required for most solvers)
    uv pip install "fluidsim[fft]"
    
    # With MPI for parallel computing
    uv pip install "fluidsim[fft,mpi]"
    

    Set environment variables for output directories (optional):

    export FLUIDSIM_PATH=/path/to/simulation/outputs
    export FLUIDDYN_PATH_SCRATCH=/path/to/working/directory
    

    No API keys or authentication required.

    See references/installation.md for complete installation instructions and environment configuration.

    2. Running Simulations

    Standard workflow consists of five steps:

    Step 1: Import solver

    from fluidsim.solvers.ns2d.solver import Simul
    

    Step 2: Create and configure parameters

    params = Simul.create_default_params()
    params.oper.nx = params.oper.ny = 256
    params.oper.Lx = params.oper.Ly = 2 * 3.14159
    params.nu_2 = 1e-3
    params.time_stepping.t_end = 10.0
    params.init_fields.type = "noise"
    

    Step 3: Instantiate simulation

    sim = Simul(params)
    

    Step 4: Execute

    sim.time_stepping.start()
    

    Step 5: Analyze results

    sim.output.phys_fields.plot("vorticity")
    sim.output.spatial_means.plot()
    

    See references/simulation_workflow.md for complete examples, restarting simulations, and cluster deployment.

    3. Available Solvers

    Choose solver based on physical problem:

    2D Navier-Stokes (ns2d): 2D turbulence, vortex dynamics

    from fluidsim.solvers.ns2d.solver import Simul
    

    3D Navier-Stokes (ns3d): 3D turbulence, realistic flows

    from fluidsim.solvers.ns3d.solver import Simul
    

    Stratified flows (ns2d.strat, ns3d.strat): Oceanic/atmospheric flows

    from fluidsim.solvers.ns2d.strat.solver import Simul
    params.N = 1.0  # Brunt-Väisälä frequency
    

    Shallow water (sw1l): Geophysical flows, rotating systems

    from fluidsim.solvers.sw1l.solver import Simul
    params.f = 1.0  # Coriolis parameter
    

    See references/solvers.md for complete solver list and selection guidance.

    4. Parameter Configuration

    Parameters are organized hierarchically and accessed via dot notation:

    Domain and resolution:

    params.oper.nx = 256  # grid points
    params.oper.Lx = 2 * pi  # domain size
    

    Physical parameters:

    params.nu_2 = 1e-3  # viscosity
    params.nu_4 = 0     # hyperviscosity (optional)
    

    Time stepping:

    params.time_stepping.t_end = 10.0
    params.time_stepping.USE_CFL = True  # adaptive time step
    params.time_stepping.CFL = 0.5
    

    Initial conditions:

    params.init_fields.type = "noise"  # or "dipole", "vortex", "from_file", "in_script"
    

    Output settings:

    params.output.periods_save.phys_fields = 1.0  # save every 1.0 time units
    params.output.periods_save.spectra = 0.5
    params.output.periods_save.spatial_means = 0.1
    

    The Parameters object raises AttributeError for typos, preventing silent configuration errors.

    See references/parameters.md for comprehensive parameter documentation.

    5. Output and Analysis

    FluidSim produces multiple output types automatically saved during simulation:

    Physical fields: Velocity, vorticity in HDF5 format

    sim.output.phys_fields.plot("vorticity")
    sim.output.phys_fields.plot("vx")
    

    Spatial means: Time series of volume-averaged quantities

    sim.output.spatial_means.plot()
    

    Spectra: Energy and enstrophy spectra

    sim.output.spectra.plot1d()
    sim.output.spectra.plot2d()
    

    Load previous simulations:

    from fluidsim import load_sim_for_plot
    sim = load_sim_for_plot("simulation_dir")
    sim.output.phys_fields.plot()
    

    Advanced visualization: Open .h5 files in ParaView or VisIt for 3D visualization.

    See references/output_analysis.md for detailed analysis workflows, parametric study analysis, and data export.

    6. Advanced Features

    Custom forcing: Maintain turbulence or drive specific dynamics

    params.forcing.enable = True
    params.forcing.type = "tcrandom"  # time-correlated random forcing
    params.forcing.forcing_rate = 1.0
    

    Custom initial conditions: Define fields in script

    params.init_fields.type = "in_script"
    sim = Simul(params)
    X, Y = sim.oper.get_XY_loc()
    vx = sim.state.state_phys.get_var("vx")
    vx[:] = sin(X) * cos(Y)
    sim.time_stepping.start()
    

    MPI parallelization: Run on multiple processors

    mpirun -np 8 python simulation_script.py
    

    Parametric studies: Run multiple simulations with different parameters

    for nu in [1e-3, 5e-4, 1e-4]:
        params = Simul.create_default_params()
        params.nu_2 = nu
        params.output.sub_directory = f"nu{nu}"
        sim = Simul(params)
        sim.time_stepping.start()
    

    See references/advanced_features.md for forcing types, custom solvers, cluster submission, and performance optimization.

    Common Use Cases

    2D Turbulence Study

    from fluidsim.solvers.ns2d.solver import Simul
    from math import pi
    
    params = Simul.create_default_params()
    params.oper.nx = params.oper.ny = 512
    params.oper.Lx = params.oper.Ly = 2 * pi
    params.nu_2 = 1e-4
    params.time_stepping.t_end = 50.0
    params.time_stepping.USE_CFL = True
    params.init_fields.type = "noise"
    params.output.periods_save.phys_fields = 5.0
    params.output.periods_save.spectra = 1.0
    
    sim = Simul(params)
    sim.time_stepping.start()
    
    # Analyze energy cascade
    sim.output.spectra.plot1d(tmin=30.0, tmax=50.0)
    

    Stratified Flow Simulation

    from fluidsim.solvers.ns2d.strat.solver import Simul
    
    params = Simul.create_default_params()
    params.oper.nx = params.oper.ny = 256
    params.N = 2.0  # stratification strength
    params.nu_2 = 5e-4
    params.time_stepping.t_end = 20.0
    
    # Initialize with dense layer
    params.init_fields.type = "in_script"
    sim = Simul(params)
    X, Y = sim.oper.get_XY_loc()
    b = sim.state.state_phys.get_var("b")
    b[:] = exp(-((X - 3.14)**2 + (Y - 3.14)**2) / 0.5)
    sim.state.statephys_from_statespect()
    
    sim.time_stepping.start()
    sim.output.phys_fields.plot("b")
    

    High-Resolution 3D Simulation with MPI

    from fluidsim.solvers.ns3d.solver import Simul
    
    params = Simul.create_default_params()
    params.oper.nx = params.oper.ny = params.oper.nz = 512
    params.nu_2 = 1e-5
    params.time_stepping.t_end = 10.0
    params.init_fields.type = "noise"
    
    sim = Simul(params)
    sim.time_stepping.start()
    

    Run with:

    mpirun -np 64 python script.py
    

    Taylor-Green Vortex Validation

    from fluidsim.solvers.ns2d.solver import Simul
    import numpy as np
    from math import pi
    
    params = Simul.create_default_params()
    params.oper.nx = params.oper.ny = 128
    params.oper.Lx = params.oper.Ly = 2 * pi
    params.nu_2 = 1e-3
    params.time_stepping.t_end = 10.0
    params.init_fields.type = "in_script"
    
    sim = Simul(params)
    X, Y = sim.oper.get_XY_loc()
    vx = sim.state.state_phys.get_var("vx")
    vy = sim.state.state_phys.get_var("vy")
    vx[:] = np.sin(X) * np.cos(Y)
    vy[:] = -np.cos(X) * np.sin(Y)
    sim.state.statephys_from_statespect()
    
    sim.time_stepping.start()
    
    # Validate energy decay
    df = sim.output.spatial_means.load()
    # Compare with analytical solution
    

    Quick Reference

    Import solver: from fluidsim.solvers.ns2d.solver import Simul

    Create parameters: params = Simul.create_default_params()

    Set resolution: params.oper.nx = params.oper.ny = 256

    Set viscosity: params.nu_2 = 1e-3

    Set end time: params.time_stepping.t_end = 10.0

    Run simulation: sim = Simul(params); sim.time_stepping.start()

    Plot results: sim.output.phys_fields.plot("vorticity")

    Load simulation: sim = load_sim_for_plot("path/to/sim")

    Resources

    Documentation: https://fluidsim.readthedocs.io/

    Reference files:

    • references/installation.md: Complete installation instructions
    • references/solvers.md: Available solvers and selection guide
    • references/simulation_workflow.md: Detailed workflow examples
    • references/parameters.md: Comprehensive parameter documentation
    • references/output_analysis.md: Output types and analysis methods
    • references/advanced_features.md: Forcing, MPI, parametric studies, custom solvers
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