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Scilab @ OpenFOAM User Conference 2017

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Scilab Optimization process control with mesh morphing
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Airfoil Shape Optimization

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Scilab @ OpenFOAM User Conference 2017

  1. 1. 1www.esi-group.com Copyright © ESI Group, 2017. All rights reserved.Copyright © ESI Group, 2017. All rights reserved. www.esi-group.com Scilab Optimization process control with mesh morphing G/EO/17,014 Yann Debray & Hugues-Arthur Garioud – Scilab – ESI Group 2017/10/17
  2. 2. 2www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Agenda Introduction 1. Design of Experiment 2. Model Reduction 3. Optimization Results & Conclusion 2
  3. 3. 3www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Introduction OpenFOAM + Scilab = CFD automation Mesh Perturbations - Hicks-Henne Sine Bumps - Mesh Generation - Mesh Morphing - DOE Generation Surrogate Modeling DOE Simulations - simpleFoam - Model reduction - POD - Optimization Optimization - Gradient/GA - Validation - simpleFoam -
  4. 4. 4www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Shape parametrization Scilab – Hicks & Henne sine bumps Based on initial airfoil I 𝑦 = 𝑦 𝑏𝑎𝑠𝑖𝑠 + 𝑖=1 𝑁 α𝑖 𝑓𝑖(𝑥) 𝑓𝑖 𝑥 = sin π𝑥 log 0.5 log 𝑡1𝑖 𝑡2𝑖 One perturbation = One parameter αi NACA0012 perturbated with 3 sine bumps. Width t2 = 4 and position t1i = [0.33 0.66 upper 0.66 lower]
  5. 5. 5www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Mesh Morphing OpenFOAM – Mesh Morphing POD need mesh with same topology - LaplacianDisplacement - 1] Set template for PointDisplacement and DynamicMesh files 2] Copy of the Scilab computed perturbations 3] Manually adjust with frozen points, hence avoiding non orthogonal cells I Perturbated NACA0012 mesh and pointDisplacement File
  6. 6. 6www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Design Of Experiment OpenFOAM – DOE simulations DOE set up 1] From template case, create new cases 2] Change the constant/polymesh/points file I 2-level full factorial + center point DOE for 3 parameters varying pressure
  7. 7. 7www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Model reduction with POD method Scilab – Proper Orthogonal Decomposition II First 4 dominant POD modes for pressure DOE case pressure field projection on 4 modes POD basis 4 modes 99.96% of the global energy Modesenergy
  8. 8. 8www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. • Minimize cost function: 𝑓 𝑥 = 𝐶 𝐷 𝐶𝐿 • Considering 3 shape parameters: (Sine bumps amplitude) 𝑥 = (α1, α2, α3) • Under the constraints: −0.05 ≤ α1 ≤ 0.05 −0.02 ≤ α2 ≤ 0.02 −0.02 ≤ α3 ≤ 0.02 And CL > 0 Airfoil Case • NACA0012 • Re = 3e6 • M = 0.15 • α = 0 Optimization process Scilab - Optimization problem expression III
  9. 9. 9www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Optimization process Scilab – Optimization cycle III Initialization Starting parameter x0 Cost function evaluation POD field prediction New parameter value CD, CL computation Cost function evaluation Minimization following the gradient Minimization using selection, mutation and cross-over Random population of starting parameters Cost function gradient evaluation New population of parameters CYCLE / GENERATION Gradient Method Genetic Algorithm Shared Process
  10. 10. 10www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Optimization process Scilab – Pressure field prediction III Projection coefficients (ai) interpolation with RBF 𝑢 ≃ 𝑖=1 𝑁 𝑃𝑂𝐷 𝑎𝑖φ𝑖 Assumption : Field can be decomposed on the POD basis Field error around airfoil for different prediction methods, within and out of DOE
  11. 11. 11www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Results: Global Optimum Within DOE - Linear behavior of the pressure IV [0.05 0.02 0.02] - Logical !
  12. 12. 12www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Results: Reduced model limits Out of DOE - Low error in lift prediction IV v [0.07 0.04 0.05] - Model not trained for flow reattachment and recirculation zone
  13. 13. 13www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Results: Reduced model limits Out of DOE - High error in pressure drag prediction IV [0.07 0.04 0.05] – Iso-pressure on suction side are left-oriented for predicted case (left)
  14. 14. 14www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Optimization • 3D/Unsteady/Multiphysic • Transonic/Hypersonic (control of pressure drop) • Real time optimization for morphing wing Leveraging other Scilab capabilities • Image & signal processing • Control system • Statistics • GUI & automation Conclusion Go further Modeling • Model based design OpenFOAM  Scilab-Xcos • Reduced order model Scilab  OpenFOAM
  15. 15. 15www.esi-group.com Copyright © ESI Group, 2017. All rights reserved. Thank you yann.debray@esi-group.com

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