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fluidsim

K-Dense-AI
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정보

FluidSim은 Navier-Stokes 방정식 및 천수 방정식과 같은 유체 역학을 시뮬레이션하기 위한 Python CFD 프레임워크입니다. FFT를 활용한 고성능 의사스펙트럴 방법을 사용하며, 병렬 컴퓨팅을 위해 MPI를 지원합니다. Python으로 난류, 와류 역학 또는 지구물리학적 흐름에 대한 시뮬레이션을 실행해야 할 때 사용하세요.

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Claude Code

추천
기본
npx skills add K-Dense-AI/claude-scientific-skills -a claude-code
플러그인 명령대체
/plugin add https://github.com/K-Dense-AI/claude-scientific-skills
Git 클론대체
git clone https://github.com/K-Dense-AI/claude-scientific-skills.git ~/.claude/skills/fluidsim

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

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

GitHub 저장소

K-Dense-AI/claude-scientific-skills
경로: skills/fluidsim
0
agent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skills
FAQ

Frequently asked questions

What is the fluidsim skill?

fluidsim is a Claude Skill by K-Dense-AI. Skills package instructions and resources that Claude loads on demand, so Claude can perform fluidsim-related tasks without extra prompting.

How do I install fluidsim?

Use the install commands on this page: add fluidsim to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.

What category does fluidsim belong to?

fluidsim is in the Design category, tagged design.

Is fluidsim free to use?

Yes. fluidsim is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.

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