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25th ICAS Congress Convention Centre Hamburg (Germany) - 3-8 September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 1 Optimization Loop Based.

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Presentation on theme: "25th ICAS Congress Convention Centre Hamburg (Germany) - 3-8 September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 1 Optimization Loop Based."— Presentation transcript:

1 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 1 Optimization Loop Based on a CFD RANS Code F. Duchaine*, L.Y.M. Gicquel* & T. Poinsot** * CERFACS, Toulouse FRANCE ** IMFT, Toulouse FRANCE

2 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 2 Aim: reduce design turnaround time by performing an automatic search of the optimal design of the cooling system. 70% Total Air Mass flow rate premixed with fuel 30 % for Cooling + control of the exit temperature profile  lean combustion  excess of air in the reacting zone LOW NOx:Reduce flame temperature ONERA CERFACS  Design of Pre-diffuser, injection system (ONERA)  Design of cooling System (CERFACS) Industrial Context : INTELLECT D.M. Automatic chain optimization for low No x injection system and combustor

3 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 3 Presentation Contents: 1. CFD & Optimization 2. Management of an Integrated Platform for auTomatic Optimization: 3. Industrial Application: a TURBOMECA combustion chamber Overview of the solution proposed by CERFACS Application to a simple test case Motivations and specificities Design optimization & 3D mesh manipulations

4 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 4 CFD & Optimization: Determinist methods vs Stochastic methods One fitness evaluation = one CFD computation + post processing  Often lead to expensive methods due to the high number of fitness function evaluations : the choice of the method is important Simplex Gradient Branch and bound … Tabou search Simulated annealing Evolutionary algorithms … Key problem : control of 3D complex geometries and meshes Shape parameterization Mesh generation Interpretation of optimum parameters for designers  Mesh quality, time requested to automatically generate a mesh and acceptable design are important aspect to keep into account

5 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 5 Requested features of MIPTO : The solution proposed by CERFACS : Management of an Integrated Platform for auTomatic Optimization The computation of the objective function is done with a black box application (no direct access to gradients of the fitness function) High level of flexibility : ease of interfacing different optimization algorithms and analysis codes to compute the fitness function High Performance Computing : parallel applications Robust, global, non specific optimization algorithms Adaptation of these algorithms to engineering design : minimization of computing cost

6 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 6 Overview of MIPTO : PALM PALM: a dynamic coupler of parallel codes (http://www.cerfacs.fr/~palm) Optimization algorithm CFD preprocessing CFD postprocessing CFD run

7 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 7 Surrogate based Method: a way to reduce the high computational cost of the CFD runs Initial Database : Design Of Experiment or hot start Construction of a surrogate model of the fitness function Optimization on the surrogate model CFD computations for optimum points N sample points = N CFD runs Overview of MIPTO :

8 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 8 Test case : Configuration : 2D channel T T (y) : Target temperature profile (user input) T C (y) : Computed temperature profile (CFD solution) Cooling Q cu T cu D cu  cu Q cl T cl D cl  cl L cu L cl Hot gas Q h T h Target temperature profile Computed exit temperature profile Estimation of the objective function : New running parameters

9 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 9 Test case : Cooling Q cu T cu D cu  cu Q cl T cl D cl  cl L cu L cl Fixed input: hot gas D cu = m D cl = m  cu = 0 rad  cl = 0 rad Q cu = Kg/s T cu = 300 K Q Fcl = Kg/s T cl = 300 K Q h = Kg/s T h = 1500 K Large deformation of the shape  re-meshing techniques (J. Muller mesher: ipol and delaundo) L cu and L cl in [0.02 ; 0.15] m Fixed parameters: Optimization parameters: Configuration : 2D channel

10 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 10 Test case : Results : surrogate method vs Simplex algorithm Convergence histories of Simplex optimizations compared to the surrogate model : chaotic behavior of the Simplex method

11 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 11 Simplex : a mean of 35 CFD runs for one local convergence / Surrogate : 91 CFD runs Surrogate method gives a better optimum than Simplex Use of the outcome of the optimization to better understand the design space (i.e. Orientation of major attraction region : L cu - L cl = k ) Test case : Results : surrogate method vs Simplex algorithm

12 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 12 Reduce the number of CFD runs towards convergence Reduce clock time due to simultaneous CFD runs Conserve the computing efforts through hot start Robust and efficient Global convergence and information about local optimum Yields an estimate of the tendency from the fitness function and over the decision space Surrogate based Method: a way to reduce the high computational cost of the CFD runs Advantages of the method :

13 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 13 TURBOMECA combustion chamber: Industrial Application : on going work Industrial configuration with MIPTO: Motivation: get more symmetrical and uniform Radial Temperature Distribution Function (RTDF) The way to go: optimization of the angular position of the primary jets

14 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 14 Design optimization: definition of the geometric parameters E cp E jd I cp & I jd Key points :  Angular position of external (E cp & E jd ) and internal (I cp & I jd ) primary jets Industrial Application : automatic control of the 3D geometry and its corresponding mesh respect of the chamber’s CAD when moving the jets (annularity) Solutions : automatic mesher or automatic mesh deformer

15 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 15 Industrial Application : Design optimization: development of a parallel mesh deformer which respect CAD constraints Deformation of the mesh colored by the nodal displacement

16 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 16 Optimization on the industrial configuration : design and operating point thought mass flow balances Local / Global remeshing techniques Enhance the CAD-Based approach Perspectives: D. Bissières, N. Savary and C. Bérat from TURBOMECA T. Morel and S. Buis from CERFACS The INCKA Team J. Muller from Queens University The authors express their gratitude to: This work is sponsored by the E.C. under grant No. FP INTELLECT D.M.

17 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 17

18 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 18 Test case :

19 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 19 TURBOMECA combustion chamber: P = Pa Dp = 2,63% T = 652,2 K FAR = Low emission reverse flow combustion chamber High performance HP single stage turbine Industrial Application :

20 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 20 RTDF :

21 25th ICAS Congress Convention Centre Hamburg (Germany) September 2006 F. Duchaine, L.Y.M. Gicquel, T. Poinsot - CERFACS 21 Optimization Loop Design optimization: CAD-based methods are more realistic in an industrial context than CAD-free methods CAD Model CAD Parameterization through design variables Mesher - Surface Grid - Domain Grid Grid deformation - Surface Grid - Domain Grid Parallel CFD Solver Post processing Specific CAD Preprocessing Two different ways to generate a suitable mesh for CFD computations Set of design variables Initial MESH Specific MESH Industrial Application :


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