add panel polar test

Author: 1-Bart-1

test/test_panel.jl

@@ -110,4 +110,59 @@ end
         @test isapprox(alpha, expected_alpha)
         @test isapprox(rel_vel, relative_velocity)
     end
+
+    # Generate mock polar data
+    alphas = -10:1:25
+    n_points = length(alphas)
+    polar_data = zeros(n_points, 4)
+    
+    # Fill polar data with realistic values
+    for (i, alpha) in enumerate(alphas)
+        cd = 0.015 + 0.015 * abs(alpha/10)^1.5  # Drag increases with angle
+        cl = 0.1 * alpha + 0.01 * alpha^2 * exp(-alpha/20)  # Lift with stall behavior
+        cm = -0.02 * alpha  # Linear pitching moment
+        polar_data[i,:] = [alpha, cd, cl, cm]
+    end
+
+    # Create two sections with slightly different polar data
+    section1 = Section([0.0, 0.0, 0.0], [1.0, 0.0, 0.0], ("polar_data", polar_data))
+    section2 = Section([0.0, 10.0, 0.0], [1.0, 10.0, 0.0], ("polar_data", polar_data * 1.1))
+    
+    # Create panel
+    panel = create_panel(section1, section2)
+    
+    @testset "Panel Polar Data Initialization" begin
+        # Test if panel has polar data
+        @test hasattr(panel, :panel_polar_data)
+        @test !isnothing(panel.panel_polar_data)
+        @test size(panel.panel_polar_data) == size(polar_data)
+        
+        # Test if panel polar data is correctly averaged
+        expected_data = (polar_data + polar_data * 1.1) / 2
+        @test isapprox(panel.panel_polar_data, expected_data, rtol=1e-5)
+    end
+    
+    @testset "Coefficient Interpolation" begin
+        test_alphas = [-5.0, 0.0, 5.0, 10.0]
+        for alpha in test_alphas
+            # Calculate coefficients using panel methods
+            alpha_rad = deg2rad(alpha)
+            cl = panel.calculate_cl(alpha_rad)
+            cd, cm = panel.calculate_cd_cm(alpha_rad)
+            
+            # Calculate expected values through interpolation
+            expected_cl = linear_interpolate(polar_data[:,1], polar_data[:,3], [alpha])[1]
+            expected_cd = linear_interpolate(polar_data[:,1], polar_data[:,2], [alpha])[1]
+            expected_cm = linear_interpolate(polar_data[:,1], polar_data[:,4], [alpha])[1]
+            
+            # Average with slightly different data (1.1 factor)
+            expected_cl = (expected_cl + 1.1 * expected_cl) / 2
+            expected_cd = (expected_cd + 1.1 * expected_cd) / 2
+            expected_cm = (expected_cm + 1.1 * expected_cm) / 2
+            
+            @test isapprox(cl, expected_cl, rtol=1e-5)
+            @test isapprox(cd, expected_cd, rtol=1e-5)
+            @test isapprox(cm, expected_cm, rtol=1e-5)
+        end
+    end
 end