[robyTKD]

Hi developers,

I don't understand how SU2 evaluates objective functions gradient. I thought it simply evaluates shape sensitivity at each position of design variable and imposes this value as of Hicks-Henne bump functions, but in SU2_GPC I found these lines:

		/*--- Continuous adjoint gradient computation ---*/
		if (rank == MASTER_NODE)
			cout << "Evaluate functional gradient using the continuous adjoint strategy." << endl;
		
    /*--- Load the delta change in the design variable (finite difference step). 
     Note that this assumes DV_Value_New = finite_diff_step and DV_Value_Old = 0.0
     in the config file. ---*/
		delta_eps = config->GetDV_Value_New(iDV);
    my_Gradient = 0.0; Gradient = 0.0;
      
      /*--- Reset update points ---*/
      for (iPoint = 0; iPoint < boundary->GetnPoint(); iPoint++)
        UpdatePoint[iPoint] = true;
      
      for (iMarker = 0; iMarker < config->GetnMarker_All(); iMarker++) {
				if (config->GetMarker_All_Moving(iMarker) == YES) {
					for (iVertex = 0; iVertex < boundary->nVertex[iMarker]; iVertex++) {
						
						iPoint = boundary->vertex[iMarker][iVertex]->GetNode();
						if ((iPoint < boundary->GetnPointDomain()) && UpdatePoint[iPoint]) {
							
							Normal = boundary->vertex[iMarker][iVertex]->GetNormal();
							VarCoord = boundary->vertex[iMarker][iVertex]->GetVarCoord();
							Sensitivity = boundary->vertex[iMarker][iVertex]->GetAuxVar();
							
							dS = 0.0; 
							for (iDim = 0; iDim < boundary->GetnDim(); iDim++) {
								dS += Normal[iDim]*Normal[iDim];
								deps[iDim] = VarCoord[iDim] / delta_eps;
							}
							dS = sqrt(dS);
							
							dalpha_deps = 0.0;
							for (iDim = 0; iDim < boundary->GetnDim(); iDim++) {
								dalpha[iDim] = Normal[iDim] / dS;
								dalpha_deps -= dalpha[iDim]*deps[iDim];
							}
							
							my_Gradient += Sensitivity*dalpha_deps;
							UpdatePoint[iPoint] = false;
						}
					}
				}				
    }

Does this imply that the deformation , deps in the code, is projected along the surface normal and then multiplied by Sensitivity? Why does this operation is needed since shape sensitivity is a gradient expressing the maximum change in the objective function due to a normal deformation?

In the picture attached below you find shape sensitivity with DRAG as objective function in blue, of_gradient in red. I don't understand why gradient evaluation doesn't follow shape sensitivity.

Cheers,
Roberto