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Unconstrained shape design of a transonic inviscid airfoil at a cte. AoA
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Constrained shape design of a transonic inviscid wing at a cte. C_{L}
Shape Design With Multiple Objectives and Penalty Functions
Unsteady Shape Optimization NACA0012
Coupled CFDRHT Adjoint Sensitivities using FFDboxes
Written by  for Version  Revised by  Revision date  Revised version 

@rsanfer  7.0.2  @rsanfer  20200207  7.0.2 
Solver: 

Uses: 

Prerequisites: 

Complexity: 
Advanced 
Goals
This tutorial follows up the coupled SU2’s incompressible CFDRHT tutorial, extending the capabilities to the coupled adjoint computation. Upon the completion of this example, the user will be capable of
 Controlling the convergence with Cauchy criteria
 Computing coupled adjoint solutions of problems involving incompressible flow and radiation
 Generating an FFD box for a problem of external aerodynamics
 Projecting the surface sensitivities into the FFD parameters
In this tutorial, we define a problem similar to the Laminar_Cylinder, however using in this case the incompressible solver. A cylinder with D = 1.0 m
is immersed in a laminar, external flow with a Reynolds number Re = 40
.
Resources
For this tutorial, please download the contents of the folder multiphysics/adjoint_rht of the Tutorials repository.
Background
SU2 adopts a P1 model for the simulation of Radiative Heat Transfer, for which further details have been provided for the incompressible CFDRHT tutorial. In this case, we will incorporate the computation of the adjoint solution for the coupled problem, which will be the main focus of this tutorial. The discrete adjoint will be computed using the fixedpoint expression
where is the objective function, the fixed point iterator on the coupled CFDRHT problem, the extended state vector including the flow variables and the radiative energy , and its adjoint. Further detail can be found in reference.
Mesh Description
The domain is discretized with an unstructured mesh with a total of 13484 nodes and 397 nodes in the body boundary, and a farfield that is 100 radiuses away from the circular body.
Flow Configuration File
We start the tutorial by definining the problem as an incompressible, Navier Stokes simulation
SOLVER = INC_NAVIER_STOKES
and we set the properties for the flow to obtain a Re = 40
based on the cylinder diameter. We use standard settings for the linear solver and a FDS scheme for the convective terms. The problem admits a relatively high CFL = 1.0E4
.
INC_DENSITY_MODEL = VARIABLE
INC_ENERGY_EQUATION = YES
INC_DENSITY_INIT = 0.0004
INC_VELOCITY_INIT = ( 1.0, 0.0, 0.0 )
INC_TEMPERATURE_INIT = 300
INC_NONDIM = DIMENSIONAL
FLUID_MODEL = INC_IDEAL_GAS
SPECIFIC_HEAT_CP = 1004.703
MOLECULAR_WEIGHT = 28.96
VISCOSITY_MODEL = SUTHERLAND
MU_REF = 1.0E5
MU_T_REF = 300.0
SUTHERLAND_CONSTANT = 110.4
CONDUCTIVITY_MODEL = CONSTANT_PRANDTL
PRANDTL_LAM = 0.072
MARKER_FAR = ( farfield )
MARKER_PLOTTING = ( body )
MARKER_MONITORING = ( body )
LINEAR_SOLVER = FGMRES
LINEAR_SOLVER_PREC = ILU
LINEAR_SOLVER_ERROR = 1E15
LINEAR_SOLVER_ITER = 20
NUM_METHOD_GRAD = WEIGHTED_LEAST_SQUARES
CONV_NUM_METHOD_FLOW = FDS
MUSCL_FLOW = YES
SLOPE_LIMITER_FLOW = NONE
TIME_DISCRE_FLOW = EULER_IMPLICIT
CFL_NUMBER = 1.0E4
The domain is centered in the center of the cylinder, and we set the reference properties as
REF_ORIGIN_MOMENT_X = 0.00
REF_ORIGIN_MOMENT_Y = 0.00
REF_ORIGIN_MOMENT_Z = 0.00
REF_LENGTH = 1.0
REF_AREA = 1.0
Finally, we are interested in the drag coefficient of the cylinder. Therefore, we will control the convergence of the problem by defining the as the main convergence parameter. We use a Cauchy criteria and determine that the problem will be considered as converged when the changes to are below 1.0E9 for more than 50 iterations.
INNER_ITER = 5000
CONV_CRITERIA = CAUCHY
CONV_FIELD = DRAG
CONV_CAUCHY_ELEMS = 50
CONV_CAUCHY_EPS = 1E9
At this stage, we run the test case without any thermal effects, to verify whether the mesh is appropriate for the case at hand. We set the boundary temperature at 300K
MARKER_ISOTHERMAL = ( body, 300.0 )
And now run the simulation without radiation model enabled. We obtain
Simulation Run using the Singlezone Driver
++
 Inner_Iter rms[P] rms[T] CD
++
 0 6.415080 0.935921 34.561397
 1 6.646410 1.167251 12.154547
 2 6.857411 1.378252 5.701560
 3 6.930843 1.451684 4.159827
 4 7.096321 1.617162 3.264458
 5 7.270927 1.791768 2.613077
 6 7.412334 1.933175 2.212900
 7 7.520525 2.041366 1.950251
 8 7.608128 2.128969 1.788462
 9 7.687862 2.208703 1.687670
 10 7.766279 2.287120 1.623157
...
 82 17.477938 11.998908 1.509865
 83 17.591619 12.112953 1.509865
 84 17.697791 12.218540 1.509865
 85 17.809936 12.330734 1.509865
 86 17.916902 12.437406 1.509865
 87 18.025352 12.546016 1.509865
 88 18.136105 12.655410 1.509865
 89 18.240080 12.761985 1.509865
 90 18.350192 12.872918 1.509865
 91 18.454120 12.975104 1.509865
 92 18.557342 13.082067 1.509865
We observe that the resulting drag coefficient is , which agrees well with results
Park, J., Kwon, K., Choi, H. (1998)  1.51 
Sen, S., Mittal, S., Biswas, G., (2009)  1.5093 
Economon, T. (2018)  1.507 
Incorporating Radiation effects
To incorporate radiation effects, we increase the temperature of the body boundary to 1500 K
MARKER_ISOTHERMAL = ( body, 1500.0 )
Now, we follow the same approach as for the incompressible CFDRHT tutorial. We define the radiative model to P1, and the absorption coefficient of the problem. Both the body and the farfield are considered emissive. Finally, the CFL number for the P1 equation is set to 1.0E4.
RADIATION_MODEL = P1
ABSORPTION_COEFF = 0.01
MARKER_EMISSIVITY = ( body, 1.0, farfield, 1.0 )
CFL_NUMBER_RAD = 1.0E4
It only remains to set the output of the problem using
SCREEN_OUTPUT = (INNER_ITER, RMS_PRESSURE, RMS_TEMPERATURE, RMS_RAD_ENERGY, DRAG)
OUTPUT_FILES = (RESTART, PARAVIEW)
SOLUTION_FILENAME = solution_rht
RESTART_FILENAME = restart_rht
VOLUME_FILENAME = volume_rht
TABULAR_FORMAT = CSV
CONV_FILENAME= history
Follow the links provided to download the config and mesh files.
Execute the code with the standard command
$ SU2_CFD config_rht_primal.cfg
which will show the following convergence history:
++
 Inner_Iter rms[P] rms[T] rms[E_Rad] CD
++
 0 6.415080 1.372397 2.891724 66.901414
 1 5.565021 0.322939 2.658304 5.940759
 2 5.984899 0.770693 2.514670 6.198429
 3 6.294298 1.163968 2.470076 6.175578
 4 5.963378 1.169388 2.439879 4.752942
 5 6.001536 1.157978 2.410318 3.907486
 6 6.141469 1.150189 2.381443 3.502636
 7 6.218988 1.125991 2.352114 3.104089
 8 6.255746 1.103723 2.323083 2.641888
 9 6.313092 1.077849 2.293892 2.169670
 10 6.395249 1.052317 2.264864 1.726404
...
 272 14.409785 6.525393 5.330613 2.768221
 273 14.437955 6.555088 5.359712 2.768221
 274 14.467762 6.583369 5.388589 2.768221
 275 14.495931 6.613064 5.417688 2.768221
 276 14.525739 6.641345 5.446565 2.768221
 277 14.553908 6.671040 5.475664 2.768221
 278 14.583715 6.699321 5.504541 2.768221
 279 14.611885 6.729016 5.533640 2.768221
 280 14.641693 6.757297 5.562517 2.768221
 281 14.669862 6.786992 5.591616 2.768221
 282 14.699669 6.815274 5.620493 2.768221
The code is stopped as soon as the cauchy criteria set for has been met
All convergence criteria satisfied.
++
 Convergence Field  Value  Criterion  Converged 
++
 Cauchy[CD] 9.39203e10 < 1e09 Yes
++
And we observe that the higher temperature has a significant impact in the drag coefficient. The problem presents a very uniform and fast convergence, as it is shown next
The resulting velocity and temperature fields are
Computing the drag adjoint
Next, we will compute the adjoint of the drag coefficient. Go ahead and download the adjoint config file, where you will observe there is very little change with respect to the primal case. Only the fact that we will be computing a discrete adjoint problem is specified using
MATH_PROBLEM = DISCRETE_ADJOINT
and the choice of function for which the adjoint will be computed is set by using
OBJECTIVE_FUNCTION = DRAG
The adjoint simulation requires the solution of the primal problem as an input. We define
SOLUTION_FILENAME = solution_rht
where the name of the solution of the primal is solution_rht.dat
. Therefore, we need to rename the restart file that we have just obtained from the primal run as restart_rht.dat
→ solution_rht.dat
.
Finally, the file names for the output of the adjoint simulation are set using
RESTART_ADJ_FILENAME = restart_rht_adj
VOLUME_ADJ_FILENAME = volume_rht_adj
CONV_FILENAME= history_adjoint
We run the discrete adjoint simulation with
$ SU2_CFD_AD config_rht_adjoint.cfg
The adjoint version of SU2 is executed, and the first output that we obtain for the solution method is as follows

Direct iteration to store the primal computational graph.
Compute residuals to check the convergence of the direct problem.
log10[U(0)]: 14.7278, log10[U(1)]: 14.3765, log10[U(2)]: 14.5019.
log10[U(3)]: 6.84497.
log10[E(rad)]: 5.64959

The solution fields have been reconstructed from the converged primal solution and read from solution_rht.dat
. One further primal iteration is run, which will be recorded using the AD tool CoDiPack, to compute the adjoint path. Therefore, the residuals printed above correspond to those of the direct iteration used for the recording. We observe that the order of magnitude of the residual agrees well with the last iterations in the primal run and the convergence trend. Further information on the recording process can be found on reference, exemplified for an FSI test case, and in reference.
An iterative process is then started by running in reverse mode through the adjoint path. The convergence of the adjoint variables is printed to screen:
++
 Inner_Iter rms[A_P] rms[A_T] rms[A_P1]
++
 0 0.536827 5.533690 7.531835
 1 0.408385 5.610806 7.654074
 2 0.335350 5.589543 7.728060
 3 0.269327 5.721695 7.755364
 4 0.187740 5.874327 7.802688
 5 0.104252 6.001200 7.932024
 6 0.033401 6.154544 8.079773
 7 0.027314 6.300318 8.234414
 8 0.082738 6.400961 8.389625
 9 0.134240 6.479417 8.539712
 10 0.183190 6.556494 8.661314
...
 238 8.673651 15.009655 15.952779
 239 8.707716 15.043698 15.984012
 240 8.741777 15.077716 16.015307
 241 8.775835 15.111755 16.046587
 242 8.809889 15.145764 16.077962
 243 8.843942 15.179796 16.109124
 244 8.877993 15.213834 16.140440
 245 8.912040 15.247850 16.171762
 246 8.946085 15.281872 16.202980
 247 8.980126 15.315881 16.234266
 248 9.014163 15.349893 16.265597
Some convergence properties of the primal problem are inherited in the adjoint run, as further explained by Albring et al. This can be observed in the convergence of the adjoint variables
FFD Box
Now, we want to convert the solution of our adjoint problem into usable magnitudes for a shape optimization problem. In order to do so, we will define a FreeForm Deformation (FFD) box around the body such as
where the design parameters to correspond to the vertical deformation of the FFD box for 50 nodes.
In order to build the FFD box, please download the FFD config file. First, we need to define the mesh file as the input, and we choose the name for the resulting mesh file that will include the FFD box information to be mesh_adjoint_rht_ffd.su2
:
MESH_FORMAT = SU2
MESH_FILENAME = mesh_adjoint_rht.su2
MESH_OUT_FILENAME = mesh_adjoint_rht_ffd.su2
The code requires all boundaries to be indicated in the config file, it is sufficient if we use
MARKER_CUSTOM = ( body, farfield )
to indicate SU2 that there are 2 boundaries, body
and farfield
in our mesh file. Next, we set up the keyword
DV_KIND = FFD_SETTING
to determine that the objective of this config file is to set up an FFD box. The boundary for which the FFD box is body
, and we are interested in having 24 boxes in direction and 1 box in direction according to our definition above, that will move in direction (0,1)
DV_MARKER = ( body )
DV_PARAM = ( MAIN_BOX, 24, 1, 0.0, 1.0 )
The FFD box will range in , and in . There is no dimension as we are solving a 2D problem.
FFD_DEFINITION = (MAIN_BOX, 0.55, 0.55, 0.0, 0.55, 0.55, 0.0, 0.55, 0.55, 0.0, 0.55, 0.55, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
Finally, the parameters for the computation of the FFD box are set.
FFD_TOLERANCE = 1E8
FFD_ITERATIONS = 500
FFD_DEGREE = ( 24, 1, 0)
FFD_CONTINUITY = 2ND_DERIVATIVE
We run the following command
$ SU2_DEF config_rht_ffd.cfg
which returns
 Write deformed grid files 
SU2 mesh mesh_adjoint_rht_ffd.su2 
Adding any FFD information to the SU2 file.
In order to confirm that the FFD information has been added to the mesh file, we open mesh_adjoint_rht_ffd.su2
and check that the FFD information is appended below
FFD_NBOX= 1
FFD_NLEVEL= 1
FFD_TAG= MAIN_BOX
FFD_LEVEL= 0
FFD_DEGREE_I= 24
FFD_DEGREE_J= 1
FFD_BLENDING= BEZIER
FFD_PARENTS= 0
FFD_CHILDREN= 0
FFD_CORNER_POINTS= 4
0.55 0.55
0.55 0.55
0.55 0.55
0.55 0.55
FFD_CONTROL_POINTS= 100
0 0 0 0.55 0.55 0.5
0 0 1 0.55 0.55 0.5
0 1 0 0.55 0.55 0.5
0 1 1 0.55 0.55 0.5
...
24 0 0 0.55 0.55 0.5
24 0 1 0.55 0.55 0.5
24 1 0 0.55 0.55 0.5
24 1 1 0.55 0.55 0.5
FFD_SURFACE_POINTS= 396
body 0 4.545454545454555e02 5.000000000000000e01 5.000000000000000e01
body 20 4.551176014851176e02 5.072118017603070e01 5.000000000000000e01
body 21 4.568338982691677e02 5.144217879880573e01 5.000000000000000e01
body 22 4.596939128289383e02 5.216281436075275e01 5.000000000000000e01
...
body 409 4.596939127778600e02 4.783718564996967e01 5.000000000000000e01
body 410 4.568338982421771e02 4.855782120969652e01 5.000000000000000e01
body 411 4.551176014774048e02 4.927881982883025e01 5.000000000000000e01
FFD Projection
Finally, we project the solution we obtained from the adjoint simulation into the design parameters of the FFD box. Starting from the adjoint config file, only minor modifications are required to run the projection, which have been included to the projection config file.
First, it is necessary to use the mesh that includes the FFD information
MESH_FILENAME = mesh_adjoint_rht_ffd.su2
MESH_FORMAT = SU2
The code will read the adjoint solution computed in the previous steps. The name of the solution file for the adjoint is set as
SOLUTION_ADJ_FILENAME = solution_rht_adj
and we need to rename the restart file from the adjoint run as restart_rht_adj_cd.dat
→ solution_rht_adj_cd.dat
. Note that the objective function cd
is appended to the filename.
Next, the information on Design Variables (DV) is provided. We need to indicate that all 50 points correspond to an FFD_CONTROL_POINT_2D
and refer to the marker body
.
DV_KIND = FFD_CONTROL_POINT_2D, FFD_CONTROL_POINT_2D, ..., FFD_CONTROL_POINT_2D, FFD_CONTROL_POINT_2D,
DV_MARKER = ( ( body ) )
The FFD parameters are indicated in structures of the form
(BOX NAME, XINDEX, YINDEX, XDISP, YDISP)
,
separated by semicolons. In this case, we have 25 positions in X direction, , and 2 in Y direction, , and their range of movement is only allowed in Y direction XDISP = 0.0
, YDISP = 1.0
DV_PARAM = ( MAIN_BOX, 0.0, 0.0, 0.0, 1.0) ; ( MAIN_BOX, 1.0, 0.0, 0.0, 1.0) ; ... ; ( MAIN_BOX, 23.0, 0.0, 0.0, 1.0) ; ( MAIN_BOX, 24.0, 0.0, 0.0, 1.0) ; ( MAIN_BOX, 0.0, 1.0, 0.0, 1.0) ; ( MAIN_BOX, 1.0, 1.0, 0.0, 1.0) ; ... ; ( MAIN_BOX, 23.0, 1.0, 0.0, 1.0) ; ( MAIN_BOX, 24.0, 1.0, 0.0, 1.0)
Finally, we need to provide a value to the 50 design points. Initially, we project the gradients for the undeformed FFD box
DV_VALUE = 0.0, 0.0, ..., 0.0, 0.0
Running the code with
$ SU2_DOT_AD config_rht_project.cfg
we obtain the following printout to screen
 FFD technique (parametric > cartesian) 
Checking FFD box dimension.
Checking FFD box intersections with the solid surfaces.
SU2 is fixing the planes to maintain a continuous 2nd order derivative.
Update cartesian coord  FFD box: MAIN_BOX. Max Diff: 1.04738e15.
Design variable (FFD_CONTROL_POINT_2D) number 0.
DRAG gradient : 0.000954173

Design variable (FFD_CONTROL_POINT_2D) number 1.
DRAG gradient : 0.00432376

...

Design variable (FFD_CONTROL_POINT_2D) number 48.
DRAG gradient : 0.00391057

Design variable (FFD_CONTROL_POINT_2D) number 49.
DRAG gradient : 0.00131275

To finalize this tutorial, the sensitivities for the FFD points are written to the file of_grad.dat
and are plotted next
References
Sanchez, R. et al. (2020), Adjointbased sensitivity analysis in hightemperaturefluid flows with participating media, (Submitted to) Modeling, Simulation and Optimization in the Health and EnergySector, SEMA SIMAI SPRINGER SERIES
Sanchez, R. et al. (2018), Coupled AdjointBased Sensitivities in LargeDisplacement FluidStructure Interaction using Algorithmic Differentiation, Int J Numer Meth Engng, Vol 111, Issue 7, pp 10811107. DOI: 10.1002/nme.5700
Albring, T. et al. (2015): Development of a consistent discrete adjoint solver in an evolving aerodynamic design framework, AIAA paper 20153240 DOI: 10.2514/6.20153240
Attribution
If you are using this content for your research, please kindly cite the following reference in your derived works (reference in the text above):
Sanchez, R. et al. (2020), Adjointbased sensitivity analysis in hightemperaturefluid flows with participating media, (Submitted to) Modeling, Simulation and Optimization in the Health and EnergySector, SEMA SIMAI SPRINGER SERIES

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