Small fixes to the linear acoustics example

python/dune/perftool/pdelab/driver/gridfunctionspace.py

@@ -254,7 +254,8 @@ def define_power_gfs(element, is_dirichlet, name, subgfs):
     gv = name_leafview()
     fem = name_fem(element.sub_elements()[0])
     gfstype = type_gfs(element, is_dirichlet)
-    return "{} {}({}, {});".format(gfstype, name, gv, fem)
+    return ["{} {}({}, {});".format(gfstype, name, gv, fem),
+            "{}.name(\"{}\");".format(name, name)]
 
 
 @preamble

test/hyperbolic/linearacoustics.mini

@@ -8,8 +8,9 @@ cells = 20 20
 elementType = hexahedral
 
 [instat]
-T = 1.5
-dt = 0.0025
+T = 0.5
+dt = 0.005
+theta = 0.0
 
 [wrapper.vtkcompare]
 name = {__name}

test/hyperbolic/linearacoustics.ufl

@@ -12,15 +12,18 @@ n = FacetNormal(cell)('+')
 u = TrialFunction(MV)
 v = TestFunction(MV)
 
-rho, q0, q1 = split(u)
-rhot, q0t, q1t = split(v)
-
 mass = inner(u, v)*dx
 
-flux = as_matrix([[q0, q1], [rho, 0.], [0., rho]])
+def flux(s):
+    rho, q0, q1 = split(s)
+    return as_matrix([[q0, q1], [rho, 0.], [0., rho]])
+
+# Define numerical fluxes to choose from
+llf_flux = dot(avg(flux(u)), n) - 0.5*jump(u)
+numerical_flux = llf_flux
 
-r = -1.*inner(flux, grad(v))*dx \
-  + inner(dot(avg(flux), n) + 0.5*jump(u), jump(v))*dS \
+r = -1. * inner(flux(u), grad(v))*dx \
+  - inner(numerical_flux, jump(v))*dS \
   + inner(u, v)*ds
 
 forms = [mass, r]

test/sumfact/hyperbolic/linearacoustics.mini

@@ -8,8 +8,9 @@ cells = 20 20
 elementType = hexahedral
 
 [instat]
-T = 1.5
-dt = 0.0025
+T = 0.5
+dt = 0.005
+theta = 0.0
 
 [wrapper.vtkcompare]
 name = {__name}

test/sumfact/hyperbolic/linearacoustics.ufl

@@ -12,15 +12,18 @@ n = FacetNormal(cell)('+')
 u = TrialFunction(MV)
 v = TestFunction(MV)
 
-rho, q0, q1 = split(u)
-rhot, q0t, q1t = split(v)
-
 mass = inner(u, v)*dx
 
-flux = as_matrix([[q0, q1], [rho, 0.], [0., rho]])
+def flux(s):
+    rho, q0, q1 = split(s)
+    return as_matrix([[q0, q1], [rho, 0.], [0., rho]])
+
+# Define numerical fluxes to choose from
+llf_flux = dot(avg(flux(u)), n) - 0.5*jump(u)
+numerical_flux = llf_flux
 
-r = -1.*inner(flux, grad(v))*dx \
-  + inner(dot(avg(flux), n) + 0.5*jump(u), jump(v))*dS \
+r = -1. * inner(flux(u), grad(v))*dx \
+  - inner(numerical_flux, jump(v))*dS \
   + inner(u, v)*ds
 
 forms = [mass, r]