fluidsim

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Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

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npx skill4agent add k-dense-ai/claude-scientific-skills fluidsim

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fluidsim-frameworkcfd-simulationspython-scientific-computingmpi-parallelizationfft-pseudospectral-methods

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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:
bash
# Basic installation
uv uv pip install fluidsim

# With FFT support (required for most solvers)
uv uv pip install "fluidsim[fft]"

# With MPI for parallel computing
uv uv pip install "fluidsim[fft,mpi]"
Set environment variables for output directories (optional):
bash
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
python
from fluidsim.solvers.ns2d.solver import Simul
Step 2: Create and configure parameters
python
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
python
sim = Simul(params)
Step 4: Execute
python
sim.time_stepping.start()
Step 5: Analyze results
python
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
python
from fluidsim.solvers.ns2d.solver import Simul
3D Navier-Stokes (
ns3d
): 3D turbulence, realistic flows
python
from fluidsim.solvers.ns3d.solver import Simul
Stratified flows (
ns2d.strat
, 
ns3d.strat
): Oceanic/atmospheric flows
python
from fluidsim.solvers.ns2d.strat.solver import Simul
params.N = 1.0  # Brunt-Väisälä frequency
Shallow water (
sw1l
): Geophysical flows, rotating systems
python
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:
python
params.oper.nx = 256  # grid points
params.oper.Lx = 2 * pi  # domain size
Physical parameters:
python
params.nu_2 = 1e-3  # viscosity
params.nu_4 = 0     # hyperviscosity (optional)
Time stepping:
python
params.time_stepping.t_end = 10.0
params.time_stepping.USE_CFL = True  # adaptive time step
params.time_stepping.CFL = 0.5
Initial conditions:
python
params.init_fields.type = "noise"  # or "dipole", "vortex", "from_file", "in_script"
Output settings:
python
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
python
sim.output.phys_fields.plot("vorticity")
sim.output.phys_fields.plot("vx")
Spatial means: Time series of volume-averaged quantities
python
sim.output.spatial_means.plot()
Spectra: Energy and enstrophy spectra
python
sim.output.spectra.plot1d()
sim.output.spectra.plot2d()
Load previous simulations:
python
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
python
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
python
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
bash
mpirun -np 8 python simulation_script.py
Parametric studies: Run multiple simulations with different parameters
python
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

python
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

python
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

python
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:
bash
mpirun -np 64 python script.py

Taylor-Green Vortex Validation

python
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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