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fluidsim

Name: Fluidsim
Author: lingxling

// 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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updated:2026년 5월 11일 11:14

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claude-api.md

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Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a Claude feature (caching, thinking, compaction, tool use, batch, files, citations, memory) or model (Opus/Sonnet/Haiku) in a file; questions about prompt caching / cache hit rate in an Anthropic SDK project. SKIP: file imports `openai`/other-provider SDK, filename like `*-openai.py`/`*-generic.py`, provider-neutral code, general programming/ML.

2026-05-11108

autoskill.md

from "lingxling/awesome-skills-cn"

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

2026-05-11108

exa-search.md

from "lingxling/awesome-skills-cn"

Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.

2026-05-11108

geniml.md

from "lingxling/awesome-skills-cn"

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

2026-05-11108

gtars.md

from "lingxling/awesome-skills-cn"

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

2026-05-11108

hugging-science.md

from "lingxling/awesome-skills-cn"

Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome /

2026-05-11108

package.json

"author": "lingxling"

"repository": "lingxling/awesome-skills-cn"

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소프트웨어 개발자컴퓨터 및 수학직15-1252L4

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	{"skill-author":"K-Dense Inc."}

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

fluidsim

이 저장소의 다른 Skills

이 저장소의 다른 Skills

FluidSim

Overview

Core Capabilities

1. Installation and Setup

2. Running Simulations

3. Available Solvers

4. Parameter Configuration

5. Output and Analysis

6. Advanced Features

Common Use Cases

2D Turbulence Study

Stratified Flow Simulation

High-Resolution 3D Simulation with MPI

Taylor-Green Vortex Validation

Quick Reference

Resources

FluidSim

Overview

Core Capabilities

1. Installation and Setup

2. Running Simulations

3. Available Solvers

4. Parameter Configuration

5. Output and Analysis

6. Advanced Features

Common Use Cases

2D Turbulence Study

Stratified Flow Simulation

High-Resolution 3D Simulation with MPI

Taylor-Green Vortex Validation

Quick Reference

Resources