You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: _tutorials/design_features/Unsteady_Shape_Opt_NACA0012.md
+6-5Lines changed: 6 additions & 5 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -16,14 +16,15 @@ Consequently, the following capabilities of SU2 will be showcased in this tutori
16
16
- Unsteady Optimization
17
17
- Code parallelism (optional)
18
18
19
-
This tutorial uses the windowing techniques explained in [here](../compressible_flow/Unsteady_NACA0012.md), to compute meaningful optimization objectives. Hence it is recommended to read this tutorial first.
19
+
This tutorial uses the windowing techniques explained in [here](../Unsteady_NACA0012.md), to compute meaningful optimization objectives. Hence it is recommended to read this tutorial first.
20
20
21
21
22
22
## Resources ##
23
23
24
24
The resources for this tutorial can be found in the [Unsteady_NACA0012](https://github.com/su2code/su2code.github.io/tree/master/Unsteady_Shape_Opt_NACA0012) directory in the [project website repository](https://github.com/su2code/su2code.github.io).
25
25
26
-
You will need the configuration file ([unsteady_naca0012_opt.cfg](../../Unsteady_NACA0012/unsteady_naca0012_opt.cfg)) and the mesh file ([unsteady_naca0012_FFD.su2](../../Unsteady_NACA0012/unsteady_naca0012_FFD.su2)).
26
+
You will need the configuration file ([unsteady_naca0012_opt.cfg](../../Unsteady_Shape_Opt_NACA0012/unsteady_naca0012_opt.cfg)) and
27
+
the mesh file ([unsteady_naca0012_FFD.su2](../../Unsteady_Shape_Opt_NACA0012/unsteady_naca0012_FFD.su2)).
27
28
28
29
## Tutorial ##
29
30
@@ -87,9 +88,9 @@ Figure (2): Instantaneous drag and drag sensitivity shown. The time frame to ave
87
88
88
89
Using the midpoint rule for above integral, we arrive at the following constrained optimization problem
The optimization constraint is given by the windowed time-averaged lift, that should be greater than a specific value $$c$$. We choose arbitrarily as $$c=0.96$$, which is the windowed
95
96
time-averaged lift of the baseline geometry.
@@ -100,7 +101,7 @@ time-averaged lift of the baseline geometry.
100
101
To compute the unsteady shape-optimization, we set up the unsteady simulation according to our test case above. More information about unsteady simulations can be found [here](../Unsteady_NACA0012.md)
0 commit comments