# Fluidsim > Planifica, configura, inspecciona, reinicia y analiza simulaciones CFD con FluidSim con controles explícitos de validez numérica y seguridad HPC: selección de solver, parámetros, FFT/MPI, diagnóstico de salidas y compatibilidad de reinicio. Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/fluidsim Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/fluidsim.md Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills Autor: K-Dense-AI Licencia: MIT Actualizado: el mes pasado Coste de contexto: 69 tok instalada, 2.9k tok al activarse, 44k tok con todos los archivos del bundle Bundle: 16 archivos, 172 KB Permisos que pide: read write bash glob python ## Instalación Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina. ```bash # Claude Code npx -y skills add K-Dense-AI/scientific-agent-skills --skill fluidsim --agent claude-code # Cursor npx -y skills add K-Dense-AI/scientific-agent-skills --skill fluidsim --agent cursor # Codex npx -y skills add K-Dense-AI/scientific-agent-skills --skill fluidsim --agent codex # Gemini CLI npx -y skills add K-Dense-AI/scientific-agent-skills --skill fluidsim --agent gemini # Windsurf npx -y skills add K-Dense-AI/scientific-agent-skills --skill fluidsim --agent windsurf # Cline npx -y skills add K-Dense-AI/scientific-agent-skills --skill fluidsim --agent cline ``` ## Qué hace - Guía la planificación, configuración y validación de simulaciones CFD con FluidSim antes de ejecutarlas - Genera un plan JSON estricto con límites de CPU, RAM, disco, tiempo, timestep, CFL, resolución y dealiasing - Ejecuta un validador y estimador de recursos, y genera un script dry-run que no corre sin confirmación explícita - Aporta scripts para inventariar salidas, revisar presupuestos de conservación y comprobar compatibilidad de reinicio - Exige evidencia de convergencia numérica y validez física antes de aceptar cualquier resultado ## Cuándo usarla - Vas a configurar, seleccionar solver o revisar parámetros de una simulación FluidSim - Necesitas preparar un trabajo MPI o pseudo-espectral con límites de recursos explícitos - Quieres validar la resolución, el timestep o los presupuestos de conservación de una corrida - Necesitas comprobar si un estado guardado es compatible para reiniciar una simulación ## Qué la activa - "Ayúdame a configurar una simulación ns2d con FluidSim" - "Valida los parámetros de este config.json de FluidSim" - "Revisa si esta corrida está lista para un reinicio" - "Genera un plan de recursos para esta simulación CFD" ## Antes de instalar - Necesita Python 3.11+; los solvers pseudo-espectrales requieren FluidFFT, y MPI/FFT nativos requieren MPI compatible, cabeceras FFTW/PFFT/P3DFFT y compiladores. - Necesita en el PATH: python3 - Variables de entorno: FLUIDSIM_PATH - reads environment config ## Archivos - SKILL.md — 11 KB - references/advanced_features.md — 11 KB - references/installation.md — 10 KB - references/output_analysis.md — 10 KB - references/parameters.md — 10 KB - references/simulation_workflow.md — 11 KB - references/solvers.md — 8 KB - scripts/__init__.py — 74 B - scripts/_common.py — 15 KB - scripts/_schema.py — 28 KB - scripts/budget_summary.py — 13 KB - scripts/grid_resource_estimator.py — 9 KB - scripts/output_inventory.py — 11 KB - scripts/restart_compatibility.py — 15 KB - scripts/simulation_dry_run.py — 8 KB - scripts/solver_config_validator.py — 2 KB ## SKILL.md Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT. Esta sección es el documento original y está en inglés. # FluidSim Use FluidSim 0.9.0 as a framework for Python-defined numerical solvers, especially periodic Cartesian pseudospectral CFD. Upstream FluidSim is CeCILL-2.1; the MIT frontmatter license applies only to this skill. This skill does **not** treat a completed run, a stable time step, a smooth plot, or a closed program exit as evidence of numerical convergence or physical validity. ## Required workflow 1. State equations, units or nondimensionalization, geometry, boundaries, initial conditions, forcing, observables, and acceptance criteria. 2. Select a verified solver and inspect its generated default parameters. 3. Create a strict JSON plan with explicit CPU, RAM, disk, wall-time, output-file, timestep, CFL, resolution, and dealiasing bounds. 4. Run the bundled validator and resource estimator. 5. Generate and review a dry-run script. It does nothing unless executed with an explicit config-ID acknowledgement. 6. Run one tiny serial pilot. Inspect budgets, divergence/constraints, spectral tails, CFL/time-step history, and output growth. 7. Refine grid and time step independently. Check conservation/budget residuals and observable sensitivity. 8. Only then prepare a site-specific MPI job. Never submit or launch MPI automatically. 9. Preserve config, script, `uv.lock`, package/platform/backend versions, logs, output inventory, checksums, and restart lineage. Stop if physical assumptions, units, boundary conditions, forcing semantics, resolution criteria, resource limits, or acceptance criteria are missing. ## Version and installation As verified on 2026-07-23: - Latest stable PyPI release: `fluidsim==0.9.0` (2025-12-04). - Package metadata requires Python `>=3.11` and lists Python 3.11–3.14. - Pseudospectral parameter creation needs FluidFFT; bare `fluidsim` imported in the smoke test, but `ns2d.create_default_params()` failed until the `fft` extra was installed. - Current companion versions tested here: `fluidfft==0.4.5` and `pyFFTW==0.15.1`. Prefer a project lock: ```bash uv init --python 3.11 uv add "fluidsim[fft]==0.9.0" "fluidfft==0.4.5" "pyFFTW==0.15.1" uv lock uv sync --frozen ``` For an isolated disposable environment: ```bash uv venv --python 3.11 uv pip install "fluidsim[fft]==0.9.0" "fluidfft==0.4.5" "pyFFTW==0.15.1" ``` The project lock is the reproducibility record; direct pins alone do not freeze all transitive artifacts. Do not reuse a lock across incompatible platforms or MPI ABIs. MPI is optional and native: ```bash uv add "mpi4py==4.1.2" "fluidfft-mpi-with-fftw==0.0.1" "fluidfft-fftwmpi==0.0.1" uv lock ``` Those packages still require a compatible MPI runtime and FFTW development libraries. The optional native plugins are: - `fluidfft-fftw==0.0.1`: sequential `fft2d.with_fftw1d`, `fft2d.with_fftw2d`, `fft3d.with_fftw3d`. - `fluidfft-mpi-with-fftw==0.0.1`: MPI `fft2d.mpi_with_fftw1d`, `fft3d.mpi_with_fftw1d`. - `fluidfft-fftwmpi==0.0.1`: MPI-enabled FFTW `fft2d.mpi_with_fftwmpi2d`, `fft3d.mpi_with_fftwmpi3d`. - `fluidfft-p3dfft==0.0.1`: `fft3d.mpi_with_p3dfft`; requires P3DFFT. - FluidFFT also declares PFFT and P3DFFT extras; audit and pin their native stacks for the target cluster. FluidFFT documents cuFFT historically, but FluidFFT 0.4.5 declares no CUDA extra or installed GPU plugin in its package metadata, and its CUDA installation page is unfinished. Do not claim GPU acceleration or install an unrelated CUDA wheel as a FluidSim backend. Treat GPU work as source-level experimental integration requiring separate validation. See [installation](references/installation.md) for system dependencies, MPI ABI, HDF5-MPI, backend discovery, and verification. ## API snapshot Use direct, versioned imports: ```python from fluidsim.solvers.ns2d.solver import Simul params = Simul.create_default_params() params.oper.nx = params.oper.ny = 32 params.oper.Lx = params.oper.Ly = 2 * 3.141592653589793 params.oper.coef_dealiasing = 2 / 3 params.time_stepping.USE_CFL = True params.time_stepping.cfl_coef = 0.5 params.time_stepping.deltat0 = 0.001 params.time_stepping.deltat_max = 0.01 params.time_stepping.t_end = 0.1 params.time_stepping.max_elapsed = "00:05:00" params.init_fields.type = "noise" params.init_fields.noise.velo_max = 0.01 params.output.HAS_TO_SAVE = False params.output.ONLINE_PLOT_OK = False ``` Important 0.9 corrections: - CFL field: `params.time_stepping.cfl_coef`, not `CFL`. - Time-correlated forcing: `params.forcing.tcrandom.time_correlation`, not a flat `tcrandom_time_correlation`. - NS2D default initial types include `constant`, `noise`, `jet`, `dipole`, `from_file`, `from_simul`, and `in_script`; do not invent a universal list for every solver. - Output state files default to `state_phys_t*.nc`; spectra use `spectra1D.h5`/`spectra2D.h5`; scalar means are solver-dependent `spatial_means.txt` or JSON-lines. - `params.output.sub_directory` is relative under `FLUIDSIM_PATH`. `ParamContainer` rejects undeclared attributes. Always generate defaults from the selected `Simul` class and inspect them before changing values. See [parameters](references/parameters.md). ## Solvers Primary Cartesian CFD keys and imports: ```python from fluidsim.solvers.ns2d.solver import Simul # ns2d from fluidsim.solvers.ns2d.bouss.solver import Simul # ns2d.bouss from fluidsim.solvers.ns2d.strat.solver import Simul # ns2d.strat from fluidsim.solvers.ns3d.solver import Simul # ns3d from fluidsim.solvers.ns3d.bouss.solver import Simul # ns3d.bouss from fluidsim.solvers.ns3d.strat.solver import Simul # ns3d.strat ``` The 0.9 registry also includes `plate2d`, `sw1l` variants, `waves2d`, 1D models, 0D models, spherical solvers, and framework adapters. Availability in the registry does not make a solver appropriate for a scientific question. Verify equations, variables, geometry, boundaries, and diagnostics in the solver source. See [solvers](references/solvers.md). ## Forcing and time advancement Forcing is solver-specific. A current normalized random example is: ```python params.forcing.enable = True params.forcing.type = "tcrandom" params.forcing.forcing_rate = 1.0 params.forcing.nkmin_forcing = 4 params.forcing.nkmax_forcing = 5 params.forcing.tcrandom.time_correlation = "based_on_forcing_rate" ``` Record the forced variable, normalization definition, wave-number band, random seed/state, injection target, and measured injection. FluidSim 0.9 saves state parameters for restart; 0.8.6 fixed time-correlated forcing restart behavior. Available pseudospectral schemes include Euler/RK2 phase-shift variants, `RK2_trapezoid`, and `RK4`. A named order does not establish accuracy. Check CFL, fast-wave/diffusive limits, `deltat_max`, and time-step refinement. See [advanced features](references/advanced_features.md). ## Outputs, loading, and restart For read-only analysis: ```python from fluidsim import load_sim_for_plot sim = load_sim_for_plot("run-directory", hide_stdout=True) sim.output.spatial_means.plot() sim.output.spectra.plot1d() sim.output.phys_fields.plot(time=1.0) ``` `load_sim_for_plot` uses a coarse operator and disables saving/online plotting. For a state-bearing object: ```python from fluidsim import load_state_phys_file sim = load_state_phys_file("run-directory", t_approx="last") ``` For a controlled restart, prefer `load_for_restart` or first run `fluidsim-restart --only-check`. Do not use `--modify-params` with untrusted text: the upstream CLI executes Python code supplied to that option. This skill's generator never emits it. Verify solver, grid/domain, state variables, versions, forcing state, checksum, target time, output destination, and resource bounds. Resolution changes require the dedicated reviewed workflow, not a silent grid edit. See [simulation workflow](references/simulation_workflow.md) and [output analysis](references/output_analysis.md). ## Scientific acceptance gate Before interpreting results, require: - Explicit dimensional units or a complete nondimensionalization map. - Correct equations, periodic geometry/boundaries, initial state, forcing, and diagnostic definitions. - Resolution and dealiasing evidence: spectra/tails, resolved gradients, and solver-appropriate small-scale criteria. - Timestep evidence: CFL history, fastest-wave and dissipative limits, and smaller-step comparison. - Conservation and budget checks including forcing, dissipation, transfers, and residuals. - Grid/time refinement with uncertainty or sensitivity for reported observables. - Comparison to an analytical solution, manufactured solution, benchmark, or independently reproduced result where appropriate. - Complete provenance and restart lineage. Never label a run “DNS,” “converged,” “validated,” “steady,” or “physically correct” from parameter values or plots alone. ## Bundled local tools All tools emit strict JSON, reject URLs/traversal/symlinks, enforce hard bounds, use no network or subprocess, and never launch a simulation: ```bash python3 scripts/solver_config_validator.py --example python3 scripts/solver_config_validator.py --config config.json python3 scripts/grid_resource_estimator.py --config config.json python3 scripts/simulation_dry_run.py --config config.json --output run.py python3 scripts/output_inventory.py --path run-directory python3 scripts/budget_summary.py --path run-directory python3 scripts/restart_compatibility.py --source state.nc --target-config config.json ``` The HDF5 tools lazily require `h5py`, inspect bounded metadata/hyperslabs, and never follow external links or load full field arrays. ## References - [Installation and FFT/MPI backends](references/installation.md) - [Solver registry and selection](references/solvers.md) - [Simulation, pilot, and restart workflow](references/simulation_workflow.md) - [Verified parameter surface](references/parameters.md) - [Output, plotting, and budget analysis](references/output_analysis.md) - [Forcing, operators, MPI, and migrations](references/advanced_features.md) ## Dated upstream basis Verified 2026-07-23 against [PyPI 0.9.0](https://pypi.org/project/fluidsim/), [FluidSim 0.9 docs](https://fluidsim.readthedocs.io/en/latest/), [release notes](https://fluidsim.readthedocs.io/en/latest/changes.html), [official source mirror](https://github.com/fluiddyn/fluidsim), [FluidFFT 0.4.5 docs](https://fluidfft.readthedocs.io/en/latest/), and the primary FluidSim ([DOI 10.5334/jors.239](https://doi.org/10.5334/jors.239)) and FluidFFT ([DOI 10.5334/jors.238](https://doi.org/10.5334/jors.238)) papers. API claims use official docs/source; method/performance claims in the references are scoped to the cited primary papers and their benchmark setups. ## Dónde encaja - Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente. - Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio - [Todas las skills](https://skillsagentes.com/skills.md) - [Ranking de instalaciones](https://skillsagentes.com/ranking.md) ## Otras skills del mismo repositorio - [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas. - [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual. - [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver). - [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo. - [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras. --- Skills Agentes · [Índice de páginas en markdown](https://skillsagentes.com/sitemap.md) · [Inicio](https://skillsagentes.com/index.md)