Shape optimization for aerodynamic design: Dassault Aviation challenges and new trends
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1 Direction Générale Technique Shape optimization for aerodynamic design: Dassault Aviation challenges and new trends Forum TERATEC 014 Atelier «Conception numérique optimale des systèmes complexes : état de l'art et verrous technologiques» École Polytechnique - Palaiseau- France - July nd 014
2 CFD Optimization Team Frédéric Chalot Laurent Daumas Quang Dinh Steven Kleinveld Ximun Loyatho Gilbert Rogé
3 page : 3 Civil > 50 % Rafale Falcon 7X UAV Spatial NEURON
4 Outline F8X CFD Aerodynamic Shape Optimization in the framework of Aircraft Design Control Theory, Adjoint, AD SBJ, Sonic Boom, Engine Integration High Lift Configuration Air Duct, Topology + Sizing Opt, Unsteady New Trends Abstract: The aim of this talk is to discuss various cutting edge techniques developed for the aerodynamic shape optimization of Dassault Aviation future products.
5 (et AMENAGEMENT) (et CENTRAGE) Controleur Controleur level 3 level level 1 5 MDO: Multidisciplinarity and Multi-level MDO: The art of efficiently managing the design parameters between disciplines and levels Level 1 Global Optimization, Comparative assessments Trade off studies ARCHITECTURE MOTEUR Open framework Surrogate models FORMES MASSES AERO GV PERFOS GV Local Optimization Critical point investigation Quantitative assessments Level & 3 Fine analysis Airframe (icl. weight estimation, aeroelasticity optimisation) Aerodynamics & Sonic Boom Layout (icl. certfication issues) Engine cycle
6 Airplane design cycle 6 Mission (market requirements) Conceptual design phase (size, range, weight,...) Preliminary configuration Aerodynamic shape optimization Preliminary design phase Structure defined in complete details, control systems,... Detailed design phase Improvements of structural and aerodynamic behavior (wind tunnel testing)
7 Optimization Process Baseline volume mesh Modified surface mesh & gradient Volume mesh deformation Volume mesh displacement Modified volume mesh Aerodynamic variables Adjoint CFD solver CFD solver Aerodynamic observations Adjoint Volume mesh deformation Aerodynamic observations gradient CAD Modeler Cost - Gradient Baseline geometry design variables Optimizer Cost, constraints & gradients Objective & constraints Starting point
8 8 Geometric Modeller Large deformation Intersection. Untrimmed surfaces.
9 9 Geometric Modeller Shape (e.g. fuselage) Osculator parameters: point, tangent, curvature
10 10 Unstructured Meshes y+=1 ~ 10 Millions vertices ~ 60 Millions tetrahedral elements
11 11 Navier-Stokes solver RANS, GLS, entropic variables, implicit scheme, GMRES Turbulence modeling: k-epsilon, k-omega, DRSM, 10 Millions vertices: 0mn on Purflex 51 procs
12 Tera 10 1 Peta Performance Development 10 Years: Power*100 1 Eflop/s 100 Pflop/s 10 Pflop/s 1 Pflop/s 100 Tflop/s 10 Tflop/s 1 Tflop/s 100 Gflop/s 10 Gflop/s 1 Gflop/s 100 Mflop/s 10.5 PFlop/s supercomputer CURIE PFlop/s SUM N=1 N=500 Dassault Aviation follows Moore law MPI, OPENMP, GPU acceleration
13 Implementation strategy Formulation for derivatives 1 et Notations: E W J X , ],.[ 1 V G M V V G G M V V G M V V V M D T T T G G L Non linear system for fluid (Euler or Navier-Stokes) State, solution of Observation Linear system for mesh deformation Second derivative operator Fluid Adjoint, solution of Mesh Adjoint, solution of Aerodynamic parameters Geometric parameters W J W E T X E X J X L T T E x Volume coordinates Surface coordinates PDE control theory J-L Lions Dunod, 1968
14 Implementation strategy Formulation for derivatives 1 et E J d dj T,1].[,1].[,, W E D W J D d J d W T W x x L d dj T,, ],.[ ],.[ x x L X W E D X W J D d J d T X W T X W Explicit Implicit CFD Explicit Implicit CFD Implicit deformation Ludovic Martin PhD, 010, CANUM Award
15 3D CFD Optimization using Newton Cz > 0.01 ONERA m6 wing Mach = 0,84, aoa= 3,06 BFGS: cost = 0 (19 NF + 10 Ng) Newton: cost = 16 (15 NF + 8 Ng + 8 Nh ) BFGS Newton BFGS Newton
16 16 Lift Lift (pt) Z/C Wing twist and camber optimization Supersonic cruise at Mach= Symmetric airfoil 5 Polar curve Optimized airfoil 0 15 Optimized wing 10 5 Prescribed CL improvement X/C Symmetric wing Drag (count) 9 control sections 4*9=36 design variables 36+1=37 optimization variables
17 Sonic Boom minimisation process 17 Shape before / after minimisation 67 optimization variables: Angle of attack (aoa) fuselage: scale, thickness, camber angle for 18 sections, 1 dihedral angle (nose) wing: twist angle, camber parameters for 3 sections, dihedral angles (inner and outer wing) Nose camber Wing dihedral Extraction / propagation
18 Same Sonic Boom optimization drag increase allowed ASEL (dba) 18 HISAC project (IP - 6th FP) Optim Ref The drag constraint has been relaxed at different levels in order to see if more noise reduction (dba and dbc) could be achieved. meteo conditions are considered => Rise time of the ground overpressure is either 1ms or 5 ms. Pareto front (dba vs drag) 88, ,5 87 Ref (1ms) Ref (5ms) Optim (1ms) Optim (5ms) 86, , , ,5 -,0% 0,0%,0% 4,0% 6,0% 8,0% 10,0% drag coefficient increase (%) - Ref=99.16 count
19 Engine integration optimization HISAC project (IP - 6th FP) Gain: 60 % on zero-lift drag Z Y X Y 19 X Z Z KP-G X Z KP-G Y X Nose Rear fuselage Cp distribution VA OÙ TON RÊVE TE PORTE Y
20 DESIREH- DASSAULT-HIGH LIFT OPTIMIZATION Optimization of High Lift configuration within European project DESIREH (Design, Simulation and Flight Reynolds Number testing for advanced High Lift Solutions) Presentation with kind permission of activities funded within Seventh Framework Programme EC Grant Agreement N ACP8-GA Steven Kleinveld
21 DESIREH- DASSAULT-HIGH LIFT OPTIMIZATION.5D High Lift optimization Use of various techniques to search for improved performance at take-off and landing SOM FD MOGA
22 DESIREH- DASSAULT-HIGH LIFT OPTIMIZATION.5D High Lift optimization Example of performance improvement through optimization at Take-Off conditions for classical high lift configuration using setting variables (gap,overlap,deflection angle) AoA
23 3D High Lift optimization DESIREH- DASSAULT-HIGH LIFT OPTIMIZATION Example of flap position optimization at Take-Off conditions of 3D configuration taking into account variations of the intersection with fuselage
24 Control and optimization of separated flows 4 PhD thesis J. Chetboun : Dassault Aviation / Polytechnique / DGA. Development of automated methods for the control and the optimization of separated flows. Application to curved air ducts for UCAV. Use of mechanical vortex generators (VG).
25 Topological derivative and parametric optimization 5 Vortex generators modelled by source term added to NS equations. Creation of a new VG : topological derivative. Sizing : parametric optimization. State equation and cost function : E( V, f ( V, l)) Adjoint state equation : P T E V E f f V Topological and parametric derivatives : g P T f ( V, l) J l J(, l) j( V(, l)) dj ( V, f ( V, l)) ( V ) dv P T E f f ( V, f ( V, l)) l
26 Applications : S-duct, U-duct, unsteady computations 6 Topological derivative DES Swirl Complete optimization
27 Robust Design (1) Cl < 0.3 Minimal drag Cl > 0.3 Final Population Minimal probability ONERA M6 wing, design parameters: twist and TE camber angles Euler, RSM RBF like but with 1rst and nd derivatives (original approach, Duchon extension) MOGA, Robust design. Objectives: to control Drag and P(CL<0.3) Ludovic Martin PhD 7
28 P(Cl<0.3) Robust Design () Minimal drag Pareto Front Initial population Final population E(CD) Minimal probability lift 8
29 P(Cl<0.3) Robust Design (3) Decision: we accept a probability lift of p = x% with minimal drag Determination of drag mean CD (Pareto front) Determination of nominal values of geomerical parameters a1 and a (camber and twist angles) Minimal drag Minimal drag p Cd E(CD) Minimal probability a1 a Minimal probability 9
30 Conclusion Aerodynamic Shape Design Optimization based on CAD (geometric constraints, ) and Adjoint Euler, RANS, cruise, TO, Unsteady, turbulence and transition modelling Bidisciplinary Optimization (Mass, Flutter, RCS, ) Robust Design, Uncertainties Falcon ++ UCAV ++ Aircraft Fighter ++
31 31 Q & A
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