Skip to main content Início Criadores cai-aa cae-agent-hub fenics-fem
fenics-fem Use this Skill to solve PDEs with the finite element method using FEniCS/dolfinx: weak form formulation, mesh generation with gmsh, Poisson/elasticity problems, boundary conditions, and paraview export.
Ir para a instalação Skills Marketplace Descubra e explore skills de IA criadas pela comunidade.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Mostrar detalhes do prompt Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
npx skills add https://github.com/Cai-aa/CAE-Agent-Hub --skill fenics-femO comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.
Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.
Baixar Zip Baixando... Mais deste repositório Plan, create, adapt, and quality-audit coin-style five-block O-grid hexahedral meshes for cylindrical CAE models. Use for software-independent O-grid planning or implementation in a selected preprocessor, including requests mentioning 钱币原理、圆柱网格、中心正方形、顶点切到圆周、结构化六面体网格, mapped square-to-circle partitions, or HEX8 meshes. Includes a generic planner and an Abaqus/CAE execution adapter; do not claim automated support in another CAE application without a matching adapter or proved live native-tool path.
calculix-sizing-optimization Workflow skill for two-stage sizing/parameter optimization on a CalculiX shell or beam deck via the optimize_structure tool — Latin Hypercube sweep plus coordinate descent to minimize mass subject to stress/displacement or natural-frequency constraints by tuning scalar section/material/load cards. Use when an agent must lighten a CalculiX shell or beam model while keeping stress and deflection within limits, or must thin a deck until its first modes clear a resonance floor (freq_1_hz constraint on a *FREQUENCY deck).
Use when: operating the standalone ansys-cfx-mcp CFX MCP leaf directly from generic clients or MCP hosts. Covers CFX-Pre, Solver, CFD-Post, cfx_workflow, cfx_model_context, run_code, validate_code, lifecycle actions, model context, named objects, API help, and result-file discovery.
Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
Explorador de arquivos
3 arquivos name fenics-fem description Use this Skill to solve PDEs with the finite element method using FEniCS/dolfinx: weak form formulation, mesh generation with gmsh, Poisson/elasticity problems, boundary conditions, and paraview export.
tags ["physics","finite-element","FEniCS","PDE","numerical-methods"] version 1.0.0 authors [{"name":"awesome-rosetta-skills contributors","github":"@xjtulyc"}] license MIT platforms ["claude-code","codex","gemini-cli","cursor"] dependencies {"python":["fenics-dolfinx>=0.6","gmsh>=4.11","pyvista>=0.39","numpy>=1.23","petsc4py>=3.18"]} last_updated 2026-03-17 status stable
FEniCS Finite Element Method for PDEs
TL;DR — Solve partial differential equations with the Finite Element Method (FEM)
using FEniCS/dolfinx. Derive the weak form, generate meshes with gmsh, apply
Dirichlet/Neumann boundary conditions, solve Poisson or elasticity problems,
and export results to XDMF/VTK for ParaView.
When to Use
Use this Skill when you need to:
Solve elliptic, parabolic, or hyperbolic PDEs on complex geometries
Implement custom weak forms for multi-physics problems
Apply mixed Dirichlet/Neumann/Robin boundary conditions
Perform convergence studies on successively refined meshes
Export solutions for publication-quality visualization in ParaView
Do not use this Skill when:
You need a quick 1D finite-difference solution → use SciPy solve_bvp
You want a spectral method for periodic domains → use pseudo-spectral libraries
You need GPU-accelerated large-scale CFD → consider OpenFOAM or Fluidity
Background & Key Concepts
Variational (Weak) Form
The FEM converts a strong-form PDE into an integral equation by multiplying by a
test function v and integrating by parts. For Poisson's equation:
Strong form: −∇²u = f in Ω, u = uD on ΓD, ∇u·n = g on ΓN
Weak form: Find u ∈ H¹(Ω) such that for all v ∈ H¹₀(Ω):
∫_Ω ∇u·∇v dx = ∫_Ω f v dx + ∫_ΓN g v ds
Function Spaces
Space dolfinx name Use case Continuous Galerkin deg 1 ("Lagrange", 1)Scalar fields, temperature Continuous Galerkin deg 2 ("Lagrange", 2)Higher accuracy, elasticity displacement Discontinuous Galerkin ("DG", 0)Cell-wise constants, flux Nédélec (edge elements) ("Nedelec1st", 1)Electromagnetics, H(curl)
Convergence and Error
For Lagrange P1 elements on a quasi-uniform mesh of size h:
L² error: O(h²) (one order above approximation degree)
H¹ error: O(h)
Environment Setup
"import dolfinx; print('dolfinx version:', dolfinx.__version__)"
"import gmsh; print('gmsh version:', gmsh.__version__)"
rm
pwd
Core Workflow
Step 1 — Poisson Equation on Unit Square """
Solve −∇²u = f on the unit square [0,1]×[0,1]
with homogeneous Dirichlet BC u=0 on ∂Ω.
Manufactured solution: u_exact = sin(πx)sin(πy)
Source term: f = 2π² sin(πx)sin(πy)
"""
from mpi4py import MPI
import numpy as np
from dolfinx import mesh, fem, io
from dolfinx.fem.petsc import LinearProblem
import ufl
def solve_poisson_unit_square (n_cells: int = 32 , degree: int = 1 ) -> dict :
"""
Solve the Poisson equation −∇²u = f on the unit square.
Args:
n_cells: Number of cells in each direction (total cells = 2*n_cells²).
degree: Polynomial degree of Lagrange finite elements.
Returns:
Dictionary with keys: uh (solution), L2_error, H1_error.
"""
domain = mesh.create_unit_square(
MPI.COMM_WORLD, n_cells, n_cells, mesh.CellType.triangle
)
V = fem.functionspace(domain, ("Lagrange" , degree))
x = ufl.SpatialCoordinate(domain)
u_exact_expr = ufl.sin(ufl.pi * x[0 ]) * ufl.sin(ufl.pi * x[1 ])
f_expr = 2.0 * ufl.pi**2 * u_exact_expr
def boundary_all (x ):
return (
np.isclose(x[0 ], 0.0 ) | np.isclose(x[0 ], 1.0 ) |
np.isclose(x[1 ], 0.0 ) | np.isclose(x[1 ], 1.0 )
)
boundary_dofs = fem.locate_dofs_geometrical(V, boundary_all)
u0 = fem.Function(V)
u0.x.array[:] = 0.0
bc = fem.dirichletbc(u0, boundary_dofs)
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
a = ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
L = f_expr * v * ufl.dx
problem = LinearProblem(a, L, bcs=[bc],
petsc_options={"ksp_type" : "cg" , "pc_type" : "hypre" })
uh = problem.solve()
diff = uh - u_exact_expr
L2_error = float (fem.assemble_scalar(fem.form(ufl.inner(diff, diff) * ufl.dx)) ** 0.5 )
H1_error = float (fem.assemble_scalar(
fem.form(ufl.inner(ufl.grad(diff), ufl.grad(diff)) * ufl.dx)
) ** 0.5 )
print (f"n_cells={n_cells} , degree={degree} : "
f"L2={L2_error:.2 e} , H1={H1_error:.2 e} " )
return {"uh" : uh, "L2_error" : L2_error, "H1_error" : H1_error}
def convergence_study () -> None :
"""Run convergence study with successive mesh refinement."""
print ("Convergence study for Poisson on unit square:" )
print (f"{'N' :>6 } {'L2 error' :>12 } {'H1 error' :>12 } {'L2 rate' :>8 } " )
prev_L2 = None
for n in [4 , 8 , 16 , 32 , 64 ]:
result = solve_poisson_unit_square(n_cells=n, degree=1 )
L2 = result["L2_error" ]
rate = (np.log(prev_L2 / L2) / np.log(2.0 )) if prev_L2 else float ("nan" )
print (f"{n:>6 } {L2:>12.4 e} {result['H1_error' ]:>12.4 e} {rate:>8.2 f} " )
prev_L2 = L2
Step 2 — Linear Elasticity """
Solve linear elasticity on a 2D beam under body force (gravity).
Strong form: −div(σ(u)) = f in Ω
σ(u) = λ tr(ε(u)) I + 2μ ε(u) (Hooke's law, Lamé form)
ε(u) = ½(∇u + ∇uᵀ) (small-strain tensor)
"""
from mpi4py import MPI
import numpy as np
from dolfinx import mesh, fem, io
from dolfinx.fem.petsc import LinearProblem
import ufl
def solve_linear_elasticity (
nx: int = 40 ,
ny: int = 10 ,
E: float = 210e9 ,
nu: float = 0.3 ,
rho: float = 7850.0 ,
g: float = 9.81 ,
output_xdmf: str = "elasticity.xdmf" ,
) -> None :
"""
Solve 2D linear elasticity (plane stress) on a rectangular beam.
The left end is clamped (u=0), a body force f=(0,-ρg) is applied.
Exports solution to XDMF for ParaView.
Args:
nx: Cells in x direction.
ny: Cells in y direction.
E: Young's modulus in Pa.
nu: Poisson's ratio.
rho: Material density in kg/m³.
g: Gravity magnitude in m/s².
output_xdmf: Output file path.
"""
lam = E * nu / ((1.0 + nu) * (1.0 - 2.0 * nu))
mu = E / (2.0 * (1.0 + nu))
domain = mesh.create_rectangle(
MPI.COMM_WORLD,
[np.array([0.0 , 0.0 ]), np.array([1.0 , 0.25 ])],
[nx, ny],
cell_type=mesh.CellType.triangle,
)
V = fem.functionspace(domain, ("Lagrange" , 1 , (2 ,)))
def epsilon (u ):
return ufl.sym(ufl.nabla_grad(u))
def sigma (u ):
return lam * ufl.nabla_div(u) * ufl.Identity(2 ) + 2 * mu * epsilon(u)
f = fem.Constant(domain, np.array([0.0 , -rho * g]))
def left_boundary (x ):
return np.isclose(x[0 ], 0.0 )
boundary_dofs = fem.locate_dofs_geometrical(V, left_boundary)
u_D = fem.Function(V)
u_D.x.array[:] = 0.0
bc = fem.dirichletbc(u_D, boundary_dofs)
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
a = ufl.inner(sigma(u), epsilon(v)) * ufl.dx
L = ufl.inner(f, v) * ufl.dx
problem = LinearProblem(a, L, bcs=[bc],
petsc_options={"ksp_type" : "preonly" , "pc_type" : "lu" })
uh = problem.solve()
uh.name = "Displacement"
s = sigma(uh) - (1.0 / 3 ) * ufl.tr(sigma(uh)) * ufl.Identity(2 )
von_mises = ufl.sqrt(1.5 * ufl.inner(s, s))
W = fem.functionspace(domain, ("DG" , 0 ))
vm_expr = fem.Expression(von_mises, W.element.interpolation_points())
vm_func = fem.Function(W)
vm_func.interpolate(vm_expr)
vm_func.name = "VonMises"
with io.XDMFFile(MPI.COMM_WORLD, output_xdmf, "w" ) as xdmf:
xdmf.write_mesh(domain)
xdmf.write_function(uh)
xdmf.write_function(vm_func)
max_disp = np.max (np.abs (uh.x.array))
print (f"Max displacement: {max_disp:.4 e} m" )
print (f"Solution written to {output_xdmf} " )
if __name__ == "__main__" :
solve_linear_elasticity()
Step 3 — gmsh Mesh Generation and Import into dolfinx """
Use gmsh to generate a structured mesh of a disk with a hole,
then import into dolfinx for FEM analysis.
"""
import gmsh
import numpy as np
from mpi4py import MPI
from dolfinx.io.gmshio import model_to_mesh
from dolfinx import fem, io
import ufl
from dolfinx.fem.petsc import LinearProblem
def create_annular_mesh (
r_inner: float = 0.2 ,
r_outer: float = 1.0 ,
mesh_size: float = 0.05 ,
output_msh: str = "annulus.msh" ,
) -> None :
"""
Create a 2D annular mesh (disk with circular hole) using gmsh.
Args:
r_inner: Inner radius (hole).
r_outer: Outer radius.
mesh_size: Target mesh element size.
output_msh: Output .msh file path.
"""
gmsh.initialize()
gmsh.model.add("annulus" )
outer = gmsh.model.occ.addDisk(0 , 0 , 0 , r_outer, r_outer)
inner = gmsh.model.occ.addDisk(0 , 0 , 0 , r_inner, r_inner)
gmsh.model.occ.cut([(2 , outer)], [(2 , inner)])
gmsh.model.occ.synchronize()
gmsh.model.mesh.setSize(gmsh.model.getEntities(0 ), mesh_size)
surfaces = gmsh.model.getEntities(2 )
for _, tag in surfaces:
gmsh.model.addPhysicalGroup(2 , [tag], tag)
gmsh.model.setPhysicalName(2 , tag, f"domain_{tag} " )
curves = gmsh.model.getBoundary(surfaces, oriented=False )
for dim, tag in curves:
gmsh.model.addPhysicalGroup(1 , [abs (tag)], abs (tag))
gmsh.model.setPhysicalName(1 , abs (tag), f"boundary_{abs (tag)} " )
gmsh.model.mesh.generate(2 )
gmsh.model.mesh.optimize("Netgen" )
gmsh.write(output_msh)
gmsh.finalize()
print (f"Annular mesh written to {output_msh} " )
def solve_poisson_annulus (msh_file: str = "annulus.msh" ) -> None :
"""
Import gmsh mesh and solve Poisson equation on the annular domain.
BC: u=1 on inner boundary, u=0 on outer boundary.
"""
gmsh.initialize()
gmsh.open (msh_file)
domain, cell_tags, facet_tags = model_to_mesh(
gmsh.model, MPI.COMM_WORLD, 0 , gdim=2
)
gmsh.finalize()
V = fem.functionspace(domain, ("Lagrange" , 2 ))
def inner_boundary (x ):
return np.sqrt(x[0 ]**2 + x[1 ]**2 ) < 0.25
def outer_boundary (x ):
return np.sqrt(x[0 ]**2 + x[1 ]**2 ) > 0.9
dofs_inner = fem.locate_dofs_geometrical(V, inner_boundary)
dofs_outer = fem.locate_dofs_geometrical(V, outer_boundary)
u_inner = fem.Function(V); u_inner.x.array[:] = 1.0
u_outer = fem.Function(V); u_outer.x.array[:] = 0.0
bcs = [
fem.dirichletbc(u_inner, dofs_inner),
fem.dirichletbc(u_outer, dofs_outer),
]
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
a = ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
L = fem.Constant(domain, 0.0 ) * v * ufl.dx
problem = LinearProblem(a, L, bcs=bcs)
uh = problem.solve()
uh.name = "u"
with io.XDMFFile(MPI.COMM_WORLD, "annulus_solution.xdmf" , "w" ) as xdmf:
xdmf.write_mesh(domain)
xdmf.write_function(uh)
print ("Annulus Laplace solution saved to annulus_solution.xdmf" )
Advanced Usage
Steady Stokes Flow (Velocity-Pressure) """
Solve the steady Stokes equations (viscous flow at Re→0) on a channel.
−μ ∇²u + ∇p = f
∇·u = 0
Uses Taylor-Hood P2/P1 elements (LBB-stable mixed formulation).
"""
from mpi4py import MPI
import numpy as np
from dolfinx import mesh, fem
from dolfinx.fem.petsc import LinearProblem
import ufl
def solve_stokes_channel (
nx: int = 64 ,
ny: int = 16 ,
mu: float = 1.0 ,
U_max: float = 1.0 ,
) -> None :
"""
Solve steady Stokes flow in a 2D channel.
Parabolic inlet profile, no-slip walls, stress-free outlet.
"""
domain = mesh.create_rectangle(
MPI.COMM_WORLD,
[np.array([0.0 , 0.0 ]), np.array([4.0 , 1.0 ])],
[nx, ny],
cell_type=mesh.CellType.triangle,
)
P2 = fem.functionspace(domain, ("Lagrange" , 2 , (2 ,)))
P1 = fem.functionspace(domain, ("Lagrange" , 1 ))
V_el = ufl.VectorElement("Lagrange" , domain.ufl_cell(), 2 )
Q_el = ufl.FiniteElement("Lagrange" , domain.ufl_cell(), 1 )
W = fem.functionspace(domain, ufl.MixedElement([V_el, Q_el]))
(u, p) = ufl.TrialFunctions(W)
(v, q) = ufl.TestFunctions(W)
f = fem.Constant(domain, np.array([0.0 , 0.0 ]))
a = (mu * ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
- ufl.div(v) * p * ufl.dx
+ q * ufl.div(u) * ufl.dx)
L = ufl.inner(f, v) * ufl.dx
def walls (x ):
return np.isclose(x[1 ], 0.0 ) | np.isclose(x[1 ], 1.0 )
def inlet_velocity (x ):
vals = np.zeros((2 , x.shape[1 ]))
vals[0 ] = 4.0 * U_max * x[1 ] * (1.0 - x[1 ])
return vals
def inlet (x ):
return np.isclose(x[0 ], 0.0 )
W0, _ = W.sub(0 ).collapse()
dofs_walls = fem.locate_dofs_geometrical((W.sub(0 ), W0), walls)
dofs_inlet = fem.locate_dofs_geometrical((W.sub(0 ), W0), inlet)
u_no_slip = fem.Function(W0); u_no_slip.x.array[:] = 0.0
u_inflow = fem.Function(W0); u_inflow.interpolate(inlet_velocity)
bcs = [
fem.dirichletbc(u_no_slip, dofs_walls, W.sub(0 )),
fem.dirichletbc(u_inflow, dofs_inlet, W.sub(0 )),
]
problem = LinearProblem(a, L, bcs=bcs,
petsc_options={"ksp_type" : "minres" , "pc_type" : "hypre" })
wh = problem.solve()
print ("Stokes channel flow solved. Extract wh.sub(0) for velocity, wh.sub(1) for pressure." )
Time-Dependent Heat Equation """
Solve the unsteady heat equation with backward Euler time integration.
∂u/∂t − α∇²u = 0 (α = thermal diffusivity)
"""
from mpi4py import MPI
import numpy as np
from dolfinx import mesh, fem
from dolfinx.fem.petsc import LinearProblem
import ufl
def solve_heat_equation (
nx: int = 40 ,
alpha: float = 0.01 ,
T_final: float = 1.0 ,
dt: float = 0.01 ,
) -> None :
"""Solve 2D heat equation with Gaussian initial condition."""
domain = mesh.create_unit_square(MPI.COMM_WORLD, nx, nx)
V = fem.functionspace(domain, ("Lagrange" , 1 ))
u_n = fem.Function(V)
x = fem.Expression(
ufl.exp(-50.0 * ((ufl.SpatialCoordinate(domain)[0 ] - 0.5 )**2
+ (ufl.SpatialCoordinate(domain)[1 ] - 0.5 )**2 )),
V.element.interpolation_points()
)
u_n.interpolate(x)
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
dt_const = fem.Constant(domain, dt)
a = (u * v + dt_const * alpha * ufl.inner(ufl.grad(u), ufl.grad(v))) * ufl.dx
L = u_n * v * ufl.dx
problem = LinearProblem(a, L, bcs=[],
petsc_options={"ksp_type" : "cg" , "pc_type" : "hypre" })
t = 0.0
n_steps = int (T_final / dt)
for step in range (n_steps):
t += dt
uh = problem.solve()
u_n.x.array[:] = uh.x.array[:]
if step % 20 == 0 :
max_u = np.max (np.abs (uh.x.array))
print (f"t={t:.3 f} : max(u)={max_u:.4 f} " )
print ("Heat equation time integration complete." )
Troubleshooting Error Cause Fix PETSc error: KSP divergedIll-conditioned system or wrong BC Check BCs; try LU solver: "ksp_type": "preonly", "pc_type": "lu" dolfinx.fem.functionspace not foundOld API (dolfinx < 0.6) Use FunctionSpace(domain, ("CG", 1)) for older versions gmsh: no surfaces foundForgot synchronize() after OCC operations Call gmsh.model.occ.synchronize() before meshing Negative Jacobian warning Poor mesh quality Call gmsh.model.mesh.optimize("Netgen") MixedElement import errorChanged API in dolfinx 0.7+ Use basix.ufl.mixed_element or BlockedElement XDMF file not readable in ParaView H5 file missing Both .xdmf and .h5 files must be in the same directory Slow solve for large meshes Dense direct solver Switch to iterative solver with HYPRE preconditioner
External Resources
Examples
Example 1 — Full Poisson Convergence Study if __name__ == "__main__" :
print ("=== Poisson Convergence Study ===" )
convergence_study()
print ()
print ("=== Poisson on Unit Square (n=64) ===" )
result = solve_poisson_unit_square(n_cells=64 , degree=2 )
print (f"L2 error with P2 elements: {result['L2_error' ]:.2 e} " )
Example 2 — Cantilever Beam Under Gravity if __name__ == "__main__" :
print ("=== Linear Elasticity: Steel Cantilever Beam ===" )
solve_linear_elasticity(
nx=80 , ny=20 ,
E=210e9 ,
nu=0.3 ,
rho=7850.0 ,
g=9.81 ,
output_xdmf="steel_beam.xdmf" ,
)
print ("Open steel_beam.xdmf in ParaView to visualize displacement and von Mises stress." )
Example 3 — gmsh Annulus Mesh and Laplace Solve if __name__ == "__main__" :
print ("=== gmsh Annular Mesh + Laplace Equation ===" )
create_annular_mesh(r_inner=0.2 , r_outer=1.0 , mesh_size=0.05 )
solve_poisson_annulus("annulus.msh" )
print ("Open annulus_solution.xdmf in ParaView." )
Changelog Version Date Change 1.0.0 2026-03-17 Initial release — Poisson, elasticity, Stokes, heat equation, gmsh integration
