Agent skill

fluidsim

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.

View SKILL.md on GitHub Repository
Stars 23,776
Forks 2,298

Install this agent skill to your Project

npx add-skill https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/fluidsim

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:

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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

davila7/claude-code-templates

verl-rl-training

Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.

23,776 2,298
Explore
davila7/claude-code-templates

openrlhf-training

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

23,776 2,298
Explore
davila7/claude-code-templates

gguf-quantization

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

23,776 2,298
Explore
davila7/claude-code-templates

Claude Code Guide

Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.

23,776 2,298
Explore
davila7/claude-code-templates

qdrant-vector-search

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

23,776 2,298
Explore
davila7/claude-code-templates

behavioral-modes

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

23,776 2,298
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results