### Fluidsim
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name: "fluidsim"
description: "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, o..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfluidsimExecute the skills CLI command in your project's root directory to begin installation:
Fetches fluidsim from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate fluidsim. Access via /fluidsim in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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| name | fluidsim |
| description | 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. |
| license | CeCILL FREE SOFTWARE LICENSE AGREEMENT |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
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:
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.
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.
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.
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.
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.
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.
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)
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")
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
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
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")
Documentation: https://fluidsim.readthedocs.io/
Reference files:
references/installation.md: Complete installation instructionsreferences/solvers.md: Available solvers and selection guidereferences/simulation_workflow.md: Detailed workflow examplesreferences/parameters.md: Comprehensive parameter documentationreferences/output_analysis.md: Output types and analysis methodsreferences/advanced_features.md: Forcing, MPI, parametric studies, custom solversPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
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Registry listing for fluidsim matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in fluidsim — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend fluidsim for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
fluidsim fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
fluidsim fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in fluidsim — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for fluidsim matched our evaluation — installs cleanly and behaves as described in the markdown.
fluidsim reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for fluidsim matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend fluidsim for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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