Add support for fluidsim for computational fluid dynamics

docs/scientific-skills.md

@@ -117,6 +117,9 @@
 - **COBRApy** - Python package for constraint-based reconstruction and analysis (COBRA) of metabolic networks. Provides tools for building, manipulating, and analyzing genome-scale metabolic models (GEMs). Key features include: flux balance analysis (FBA) for predicting optimal metabolic fluxes, flux variability analysis (FVA), gene knockout simulations, pathway analysis, model validation, and integration with other COBRA Toolbox formats (SBML, JSON). Supports various optimization objectives (biomass production, ATP production, metabolite production), constraint handling (reaction bounds, gene-protein-reaction associations), and model comparison. Includes utilities for model construction, gap filling, and model refinement. Use cases: metabolic engineering, systems biology, biotechnology applications, understanding cellular metabolism, and predicting metabolic phenotypes
 - **Pymatgen** - Python Materials Genomics (pymatgen) library for materials science computation and analysis. Provides comprehensive tools for crystal structure manipulation, phase diagram construction, electronic structure analysis, and materials property calculations. Key features include: structure objects with symmetry analysis, space group determination, structure matching and comparison, phase diagram generation from formation energies, band structure and density of states analysis, defect calculations, surface and interface analysis, and integration with DFT codes (VASP, Quantum ESPRESSO, ABINIT). Supports Materials Project database integration, structure file I/O (CIF, POSCAR, VASP), and high-throughput materials screening workflows. Use cases: materials discovery, crystal structure analysis, phase stability prediction, electronic structure calculations, and computational materials science research
 
+### Engineering & Simulation
+- **FluidSim** - Object-oriented Python framework for high-performance computational fluid dynamics (CFD) simulations using pseudospectral methods with FFT. Provides solvers for periodic-domain equations including 2D/3D incompressible Navier-Stokes equations (with/without stratification), shallow water equations, and Föppl-von Kármán elastic plate equations. Key features include: Pythran/Transonic compilation for performance comparable to Fortran/C++, MPI parallelization for large-scale simulations, hierarchical parameter configuration with type safety, comprehensive output management (physical fields in HDF5, spatial means, energy/enstrophy spectra, spectral energy budgets), custom forcing mechanisms (time-correlated random forcing, proportional forcing, script-defined forcing), flexible initial conditions (noise, vortex, dipole, Taylor-Green, from file, in-script), online and offline visualization, and integration with ParaView/VisIt for 3D visualization. Supports workflow features including simulation restart/continuation, parametric studies with batch execution, cluster submission integration, and adaptive CFL-based time stepping. Use cases: 2D/3D turbulence studies with energy cascade analysis, stratified oceanic and atmospheric flows with buoyancy effects, geophysical flows with rotation (Coriolis effects), vortex dynamics and fundamental fluid mechanics research, high-resolution direct numerical simulation (DNS), parametric studies exploring parameter spaces, validation studies (Taylor-Green vortex), and any periodic-domain fluid dynamics research requiring HPC-grade performance with Python flexibility
+
 ### Data Analysis & Visualization
 - **Dask** - Parallel computing for larger-than-memory datasets with distributed DataFrames, Arrays, Bags, and Futures
 - **Data Commons** - Programmatic access to public statistical data from global sources including census bureaus, health organizations, and environmental agencies. Provides unified Python API for querying demographic data, economic indicators, health statistics, and environmental datasets through a knowledge graph interface. Features three main endpoints: Observation (statistical time-series queries for population, GDP, unemployment rates, disease prevalence), Node (knowledge graph exploration for entity relationships and hierarchies), and Resolve (entity identification from names, coordinates, or Wikidata IDs). Seamless Pandas integration for DataFrames, relation expressions for hierarchical queries, data source filtering for consistency, and support for custom Data Commons instances