Flow Control. Jean-Pierre Richard. joint work with
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1 Flow Control Jean-Pierre Richard joint work with Maxime Feingesicht (Department of Aerospace Engineering, The University of Michigan) Andrey Polyakov (Inria, centre de recherche Lille - Nord Europe) Franck Kerhervé (Univ. de Poitiers et CNRS Institut Pprime)
2 Flow Control
3 what we won t make here
4 now, control it? /a-drone-that-fliesalmost-like-a-bird/ what we won t make here, either
5 what we'll only discuss about
6 Flow Control Overview - General issues, passive vs active - Control issues: optimality and learning vs robustness and rough model - Model-based control: linear model, nonlinear control - Linear model, identification - Sliding Mode Control - Delay effect - Time-delay systems - Introduction to delay systems - Examples - Much a do about delay? Some special features + a bit of maths - Time-varying delay - Model-based control: nonlinear model, nonlinear control - Overview of MF s PhD: Sliding Mode Control - Application to the airfoil - Application to the Ahmed body (MF and CC PhDs) - Machine Learning and model-free control: + 4h with Thomas Gomez
7 Passive flow control H. Werlé. Document Onera but, also /vienne/des-avions-inspires-desbaleines-et-des-requins
8 Active flow control?
9 Active flow control?
10 Active flow control air flow Separated flow wing flap Attached flow Tomography 10
11 Active flow control PhD Feingesicht 2017 PhD Chabert 2014 Visualisation of a flow without control Visualisation of a flow with periodic (open loop) control (1 Hz, DC 50%)
12 Application domains
13 Application domains
14 I m no mechanical engineer please help me! (did I get it right?)
15 Flow control: Passive vs Active I m no mechanical engineer please help me! (did I get it right?) vortex shedding Kelvin Helmoltz instabilities flapping
16 Flow control: Passive vs Active passive and Sliding Mode Control (Feingesicht 2017) active
17 Flow control: Passive vs Active
18 Active flow control: Positioning Open loop: (periodic), not robust, not precise, energy consuming Linear+PID: not robust, not precise wrt perturbations, no proof of stability Machine Learning: needs learning, no proof of stability, robustness? SMC+delay model to be presented here
19 Active flow control: Positioning. Pros Cons Model-free saves identification time induces training time (machine learning) optimality (at least in open loop) no proof of stability easy to implement no proof of robustness fashionable (magic of A.I.) ;) NL+D model theoretical proofs identification (nonconvex opt.) (bilinear-delay +SMC) robustness mathematics of control easy to implement we did it ;) Both today : surpass passive aerodyn. TBD: energetic trade-off? commercial issue = actuators
20 Active flow control: model-based vs model-free
21 A regional consortium around Lille CPER ELSAT2020 (+ FR TTM) Aeronautics (ONERA) Micro-NanoTechnologies (IEMN) Fluid mechanics (LML, LAMIH) Control: CRIStAL / Inria Non-A + UPR PPRIME (Poitiers, F. Kerhervé) PhD Maxime Feingesicht 11/12/2017 (Andrey Polyakov, Franck Kerhervé, J.P. Richard)
22 Very Hardware (wind tunnels) +PIV data (LML) (particle image velocimetry) 22
23 Very Hardware (wind tunnels) Flow Control: Airfoil in the Onera L1 wind tunnel
24 Hardware (sensors, actuators) 24
25 Software! (control law = calculation code) here we are ;) control algorithm model u y yes, right there :) 25
26 Usual model for fluid mechanics Concerns: - existence of (strong) solutions in dim 3? (no formal proof) - no general control theory for such nonlinear PDEs - real-time issues (even for 2D simulation ) Question: how to control that? 26
27 Modèle pour le contrôle Answer: don t control that. 27
28 Simpler PDEs? Modèle pour le contrôle see Maxime s PhD, pdf p don t control that, either. 28
29 Input Output flow Control General ideas for control engineering: - start with the simplest model = the one you can control? - bad model quality? Compensate with robust control - if still needed, complexify the model and the control model u y control algorithm Advantages: - SISO (1 input 1 output) - 3D dynamics 1D dynamics (actuator sensor) - local models? (linearization, then PID) Still various issues: - on/off control (actuation technology) - nonlinear model (compressible fluid) - infinite dimension (diffusion) but rather control this.
Département AUTOMATIQUE
Bombardier Toyota Railway industry 10 000 employees 1 st European region for railway 4 International manufacturers leaders 1 Billion euros sales revenue LAMIH Alstom MCA Automobile industry 36 000 employees
- General issues, passive vs active - Control issues: optimality and learning vs robustness and rough model
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