Add entropy2cons for CompressibleEulerQuasi1D

src/equations/compressible_euler_quasi_1d.jl

@@ -355,6 +355,13 @@ end
     return SVector(w[1], w[2], w[3], a)
 end
 
+@inline function entropy2cons(w, equations::CompressibleEulerEquationsQuasi1D)
+    w_rho, w_rho_v1, w_rho_e, a = w
+    u = entropy2cons(SVector(w_rho, w_rho_v1, w_rho_e),
+                     CompressibleEulerEquations1D(equations.gamma))
+    return SVector(a * u[1], a * u[2], a * u[3], a)
+end
+
 # Convert primitive to conservative variables
 @inline function prim2cons(u, equations::CompressibleEulerEquationsQuasi1D)
     rho, v1, p, a = u

src/solvers/dgmulti/flux_differencing.jl

@@ -431,7 +431,7 @@ end
 
 # For traditional SBP operators on triangles, the operators are fully dense. We avoid using
 # sum factorization here, which is slower for fully dense matrices.
-@inline function has_sparse_operators(::Union{Tri, Tet},
+@inline function has_sparse_operators(::Union{Line, Tri, Tet},
                                       approx_type::AT) where {AT <: SBP}
     False()
 end

test/test_dgmulti_1d.jl

@@ -273,6 +273,32 @@ end
         @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 1000
     end
 end
+
+@trixi_testset "elixir_euler_quasi_1d.jl (Polynomial) " begin
+    @test_trixi_include(joinpath(EXAMPLES_DIR, "elixir_euler_quasi_1d.jl"),
+                        cells_per_dimension=(8,),
+                        approximation_type=Polynomial(),
+                        l2=[
+                            3.3742251708854453e-6,
+                            2.9716405988822176e-6,
+                            3.1641250402788772e-6,
+                            1.0482169269991052e-6
+                        ],
+                        linf=[
+                            8.056816211965412e-6,
+                            6.031057946387364e-6,
+                            6.90878439346676e-6,
+                            1.5199471203874992e-6
+                        ])
+    # Ensure that we do not have excessive memory allocations
+    # (e.g., from type instabilities)
+    let
+        t = sol.t[end]
+        u_ode = sol.u[end]
+        du_ode = similar(u_ode)
+        @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 1000
+    end
+end
 end
 
 # Clean up afterwards: delete Trixi.jl output directory

test/test_unit.jl

@@ -2070,6 +2070,8 @@ end
 
             @test max_abs_speed_naive(u_ll, u_rr, 1, equations) ≈
                   max_abs_speed(u_ll, u_rr, 1, equations)
+
+            @test u_ll ≈ entropy2cons(cons2entropy(u_ll, equations), equations)
         end
     end