This chapter was written by Zilong Li and reviewed by Ping He.
Learning Objectives:
After reading this chapter, you should be able to:
- Setup unsteady aerodynamic shape optimizations
Overview of the Cylinder - unsteady aerodynamic shape optimization
The following is an unsteady aerodynamic shape optimization case for a cylinder
Case: Unsteady flow over a cylinder Geometry: Cylinder Objective function: Time-averaged CD Design variables: 16 FFD points moving in the x direction Constraints: Cylinder volume does not decrease; FFD symmetry wrt z=0 and y=0 Inlet velocity: 10 m/s Mesh cells: 2450 Solver: DAPimpleFoam
Fig. 1. Mesh and FFD points for the Cylinder case
To run this case, first download tutorials and untar it. Then go to tutorials-main/Cylinder_Unsteady and run the “preProcessing.sh” script to generate the mesh:
./preProcessing.sh
Follow similar steps as in the NACA0012 airfoil case, We use the pyHyp package to generate the mesh. After the mesh generation is done, we are ready to run the simulation. Because in this case we run the unsteady aerodyanmic shape optimization, we need to first run the steady simulation to prepare the initial flow fields for the unsteady optimization. We use the simpleFoam solver to run the steady simulation and reconstruct the flow fields. As a side note, in this case we run the potentialFoam solver to generate the initial flow fields for the simpleFoam.
# run simpleFoam
cp -r system/controlDict_simple system/controlDict
cp -r system/fvSchemes_simple system/fvSchemes
cp -r system/fvSolution_simple system/fvSolution
potentialFoam
mpirun -np 4 python runPrimalSimple.py
# reconstruct the simpleFoam fields
reconstructPar
rm -rf processor*
rm -rf 0
mv 500 0
rm -rf 0/uniform 0/polyMesh
After the simpleFoam is done, we run the pimpleFoam primal to get equilibrium initial fields.
# run the pimpleFoam primal to get equilibrium initial fields
cp -r system/controlDict_pimple_long system/controlDict
cp -r system/fvSchemes_pimple system/fvSchemes
cp -r system/fvSolution_pimple system/fvSolution
mpirun -np 4 python runScript.py -task=run_model
reconstructPar -latestTime
rm -rf processor*
rm -rf 0
mv 10 0
rm -rf 0/uniform 0/polyMesh
cp -r system/controlDict_pimple system/controlDict
We recommend running this case with 4 CPU cores:
mpirun -np 4 python runScript_v2.py 2>&1 | tee logOpt.txt
We ran this case using the SNOPT optimizer. The case ran for 14 major iterations and took about 10 hours. According to “opt_snopt_summary.txt”, the initial CD is 6.5587285E-01 and the optimized CD is 5.3605074E-01 with a percentage decrease of 18%. Comparison of the unsteady velocity animation and CD time series between the baseline and optimized designs are shown as follows.
Fig. 2. Time-series of CD for the baseline and optimized design
Fig. 3. Animation of velocity contours for the baseline (left) and optimized (right) designs.