Optimization of jet parameters to control the flow over a ramp€¦ · 2 Eqs: K-e, K-w...

Optimization ofjet parameters tocontrol the flow

over a ramp

EmmanuelGUILMINEAU ,

RégisDUVIGNEAU ,

JérémieLABROQUERE

Numerical toolsFlow solver: ISIS-CFD

Optimizer: FAMOSA

Numerical dataInitial configuration

Configuration with asynthetic jet

Configuration A

Configuration B

Streamline in the lastperiod

Conclusions &Perspectives

Optimization of jet parameters to controlthe flow over a ramp

Emmanuel GUILMINEAU 1, Régis DUVIGNEAU 2,Jérémie LABROQUERE 2

1LHEEA, CNRS UMR 6598,Ecole Centrale de Nantes

2 Opale Project-Team,INRIA Sophia Antipolis

3rd GDR Symposium Flow Separation Control7-8 November 2013

Lille


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



NUMERICAL TOOLS


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



THE FLOW SOLVER: ISIS-CFD


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



The flow solver: ISIS-CFD

Incompressible viscous flows

immiscible phases

Reynolds averaged Navier Stokes equationsFully implicit finite volume discretization

Arbitrary shaped control volumeSecond order in space and timeSIMPLE-like algorithm: pressure equationParallel version

Turbulence modeling1 Eq : Spalart-Allmaras2 Eqs: K-ε, K-ω Wilcox/Menter, EASM, ARSM7 Eqs: Rij −ω

Hybrid LES model: DES


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



OPTIMIZER: FAMOSA (INRIA)


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



The optimizer: FAMOSA

Efficient Global Optimization (EGO) algorithm

Iterative construction of a database and related Gaussianmodel

Enrichment driven by a statistical merit functionAlgorithm organized in two phases

An initial a priori database is constructed, that gathers theflow response corresponding to different jet parametervalues. The control parameters are chosen in order toexplore uniformly the search space, according to a DesignOf Experiments (DOE) method.A kriging model is constructed on the basis of available dataand is used to determine which flow simulations should becarried out and added into the database. This phase is thenrepeated until convergence of the algorithm.


over a ramp


RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Efficient Global Optimization loop


over a ramp


RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



NUMERICAL DATA


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Computational domain

Physical parameters:

Height of the step: h = 100 mm

Upstream velocity: Uref = 20 m/s


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Boundary conditions

Numerical parameters:

Turbulence model: K-ω SST

Time step: ∆t = 5e-5 s


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Mesh

Number of nodes 73 502Number of cells 71 375


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Evolution of y+

Y+ Y+Distance from the wall on the step on the other walls

3.6e-3 mm ≤ 0.15 ≤ 0.51


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



RESULTS


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



INITIAL CONFIGURATION


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Velocity profile at X/h = -2.4


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Streamlines

Xc /h 2.659Yc /h 0.455Lr /h 5.333


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



CONFIGURATION WITH A SYNTHETIC JET


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configurations

Two positions of the jet

Configuration A Configuration BWidth of the slot: s = 0.5 mm = 0.05h

Normal direction to the wall

Simulation time: 30 periods of the jet (T)

Number of time step / T: 200

Mean flow on the last 5 periods of the jet


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Design of experiments

A priori database used to explore the design space defined bythe following jet parameters ranges

Amplitude: 15 m/s ≤ Ujet ≤ 100 m/s

Frequency: 50 Hz ≤ fjet ≤ 600 Hz


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration A


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration A: Optimization history


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration A: Meta model at the last step ofthe optimization


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration A : Best results

Amplitude : 63.760 m/s

Frequency : 77.330 Hz ( St = fjet hU∞

∼ 0.387 )

Recirculation length: 4.040h (∼ -24%)


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration B


over a ramp


RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration B: Optimization history


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration B: Meta model at the last step ofthe optimization


over a ramp


RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Configuration B: Best results

Amplitude : 88.193 m/s

Frequency : 62.086 Hz ( St = fjet hU∞

∼ 0.310 )

Recirculation length: 4.061h (∼ -24%)


over a ramp


RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Streamline in the last period


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Streamlines at t = t0

Configuration A

Configuration B


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Streamlines at t = t0 + 15T

Configuration A

Configuration B


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B




Configuration A

Configuration B


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



CONCLUSIONS & PERSPECTIVES


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RégisDUVIGNEAU ,

JérémieLABROQUERE


Optimizer: FAMOSA



Configuration A

Configuration B



Conclusions & Perspectives

Conclusions:2 positions of the synthetic jet studied

One just before the corner of the stepOne just after the corner of the step

Direction of jet: normal to the wallThe position of the jet does not influence the flowThe meta model begins to oscillates after the iteration 4.

Perspectives:Influence of the bounds of the design space

Amplitude: 15 m/s ≤ Ujet ≤ 100 m/sFrequency: 50 Hz ≤ fjet ≤ 600 Hz

Configuration A: : Ujet = 123.58 m/s & fjet = 615.41 HzInfluence of the turbulence model

Turbulence model Recirculation lengthK - ω SST 5.333 hEARSM 6.594 h

Optimization of jet parameters to control the flow over a ramp€¦ · 2 Eqs: K-e, K-w...

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