EMship Master Thesis Sketched Parametric Modeling in CFD Optimization by Martin A.B. Gundelach Internship at: Supervisors: Dr.-Ing. S. Harries, Prof. R. Bronsart, M.Sc. S. Greshake Feb/2017, University of Rostock (Germany) 1/20
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EMship Master Thesis
Sketched Parametric Modeling in CFD Optimization
by Martin A.B. Gundelach
Internship at:
Supervisors: Dr.-Ing. S. Harries, Prof. R. Bronsart, M.Sc. S. Greshake
Feb/2017, University of Rostock (Germany) 1/20
Motivation: CAD vs CFD
2/20
CAD designers (hull design)
CFD engineers (hull optimization)
Different knowledge, different software!
Picture (R) from: http://simhub.autodesk.com/discussions/threads/330/post/5339381
Motivation: Simulation-driven design with parametric modeling
3/20
Source
Transformation
Image
Scaling vector [x,y,z] = [1.25,1,1]
Motivation: Simulation-driven design benefits and shortcomings
4/20
Flex
ibili
ty o
f sh
ape
crea
tion
Efficiency of variant creation
Traditional
Partially-parametric
Fully-parametric
Use
r ef
fort
Process phase
Traditional modeling
Parametric modeling
Variants
Motivation: Sketched Parametric Modeling
5/20
Source
Transformation
Image
Pictures from: KHO, Y. & GARLAND, M., 2005. Sketching Mesh Deformations. ACM SIGGRAPH 2005, USA.
Motivation: Sketched Parametric Modeling and Adjoint CFD Method
6/20 Pictures from: http://www.pointwise.com/theconnector/March-2015/Aircraft-Design-Using-SU2-Pointwise.shtml
Tools used: Friendship Systems’ CAESES
7/20 Pictures from: http://www.caeses.com/
Tools developed: Bulb Transformation Feature
8/20
Tools developed: Bulb Transformation Feature
9/20
Tools developed: Aft Waterline/Diagonal Feature
10/20
Tools developed: Aft Waterline/Diagonal Feature
11/20
Problem description: KCS hull resistance optimization
12/20
CT , RT : ↓
FN = 0.26 , 0.23
V ≥ V0 – 200m3 (0.4%)
-1 ≥ LCB ≥ -2 (-1.5%)
Tools used: NUMECA’s FINE/Marine, CPU24-7 cluster
13/20 Pictures from NUMECA’s brochure (T) and http://www.CPU-24-7.com (B)
Unstructured mesh
VOF
Finite volumes
2-eq. k-ω SST turbulence model
CFD Setup in FINE/Marine
14/20
Time steps: 2000
Average of last 10% for RT
Dynamic equilibrium (trim, sinkage)
Convergence analysis
1 sim (~35s) = 1h real time
200 variants
Design variables setup in CAESES
15/20
[-0.9 , +1.1]
[-1.4 , +1]
[100% , 110%]
[-0.3 , +0.1]
[0.0 , +0.25]
Optimization Results: geometry differences
16/20
Optimization Results: optimum hull
17/20
Lbulb : 13% increase
Zbulb : 15% decrease
Vbulb : 12% increase
High diagonal: 7cm in
Low diagonal: 14cm out
Original hull’s resistance results
18/20
Compared to experimental results [1]:
• CT_CFD : 2% smaller than CT_EXP
• Swdyn: 5% bigger than Swstatic
• Exp. hull has rudder
[1]: KLEINSORGE, L., LINDNER, H., & BRONSART, R., 2016. “A Computational Environment for Rapid CFD Ship Resistance Analyses”. Proceedings of PRADS2016, September 2016, Denmark.
Optimum hull’s resistance results
19/20
Compared to original (CFD) results:
• CT-OPTM : 1% smaller than CT-ORIG
Conclusions
20/20
Sketched Parametric Modeling: making it easier for CFD engineers to optimize hulls
Tools can be developed inside software, but requires knowledge of parametric modeling
Shape optimization applied here was successful: only 1%, but from conservative approach
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