KU Leuven vibro-acoustics activities in an Industry 4.0 context

Transcription

1 KU Leuven vibro-acoustics activities in an Industry 4.0 context Wim Desmet KU Leuven Department of Mechanical Engineering Flanders Make - Virtual Department Mechatronics & Design

2 overview KU Leuven team Industry research strategy and approach some vibro-acoustic innovations: o virtual sensing o metamaterials by design o model based geometry characterisation o model based material characterisation

3 who we are KU Leuven founded in students 15 faculties, 50 departments 62 academic programmes 800 MEUR total revenues

4 team KU Leuven o Department of Mechanical Engineering Division of Production engineering, Machine design and Automation (PMA) Noise and Vibration Research Group (MOD) research staff o o o o 5 academic and 1 associated 1 industrial research manager 11 postdoctoral researchers 61 PhD incl. 10 industrial PhD res. areas of research application domains o vibro-acoustics - energy and environment o aero-acoustics - transport and mobility o multi-body dynamics o smart system dynamics - health o - advanced manufacturing structural reliability & uncertainty core lab of the Strategic Research Centre for Smart Manufacturing (Flanders Make)

5 Industry 4.0 Industry 4.0 full digitization of the value chain cyber-physical systems enablers smart connected customized Model Based System Engineering digital twin

6 strategy creating added value by embedding dynamic behavior information in a digital twin during every phase (design manufacturing operations)

7 approach physical behaviour models vibro-acoustics (flexible) multibody dynamics multi-physical mechatronic system models aero-acoustics model usage purely virtual (virtual prototyping) blended with sensors (virtual sensing) research innovations methodological MBSE applications

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9 overview KU Leuven team Industry research strategy and approach some vibro-acoustic innovations: o virtual sensing o metamaterials by design o model based geometry characterisation o model based material characterisation

10 model based virtual sensing objectives obtain information on hard-to-measure quantities from highfidelity first-principles dynamic models, of which the inputs and parameters are estimated on-line and in-situ using affordable/non-intrusive sensor data to be used in all phase (design manufacturing operational life) of a cyber-physical system

11 model based virtual sensing approach non-intrusive sensors models virtual sensing Kalman Filter EKF/UKF time-stable estimators Moving Horizon Estimators HW/SW architecture Naets, F., Croes, J., Desmet, W. (2015). An online coupled state/input/parameter estimation approach for structural dynamics. Computer Methods in Applied Mechanics and Engineering. 283 (1)

12 model based virtual sensing approach Input Kalman filter Measurements h measured h predicted Estimator h estimated Model x estimated x estimated

13 model based virtual sensing approach

14 model based virtual sensing objectives obtain information on hard-to-measure quantities from highfidelity first-principles dynamic models, of which the inputs and parameters are estimated on-line and in-situ using affordable/non-intrusive sensor data approach stable observers high-fidelity physical behaviour models non-intrusive sensors challenges efficient, time stable physical behaviour models observability

15 model based virtual sensing time-stable coupled vibro-acoustic model order reduction vibro-acoustic finite element model too expensive X standard MOR: becomes unstable! X Stability-preserving MOR Time-Domain simulation Reduced-Order Model van de Walle, A., Naets, F., Deckers, E., Desmet, W. (2017). Stability-preserving model order reduction for time-domain simulation of vibro-acoustic FE models. International Journal for Numerical Methods in Engineering. (in press)

16 model based virtual sensing time-stable coupled vibro-acoustic model order reduction n equations in n unknowns t + t + t = (t) t + t + t = (t) r equations in r unknowns

17 model based virtual sensing time-stable coupled vibro-acoustic model order reduction coupled vibroacoustic FE system of equations: is stable! MOR: with reduced matrices after projection of K, C and M matrices: partial symmetry and definiteness is destroyed, resulting in a loss of stability

18 model based virtual sensing time-stable coupled vibro-acoustic model order reduction one-sided projection transform into linear descriptor formulation stiffness matrices : symmetric positive (semi)definite mass matrices : symmetric positive definite damping matrices : positive (semi)definite

19 model based virtual sensing time-stable coupled vibro-acoustic model order reduction finite element model reduced-order model number of DOFs model reduction time n/a 2,7h FRF computation time 76,4h 3,6s

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21 model based virtual sensing validation: (real-time) vibro-acoustic digital twin

22 model based virtual sensing validation: (real-time) vibro-acoustic digital twin

23 model based virtual sensing validation: (real-time) vibro-acoustic digital twin close-up Virtual sensing at microphone R2

24 model based virtual sensing validation: (real-time) vibro-acoustic digital twin Virtual sensing at microphone R2

25 model based virtual sensing validation: (real-time) vibro-acoustic digital twin

26 model based virtual sensing validation: microphone based parameter estimation virtual measurement of plate stiffness = 0 coupling matrix acoustic stiffness matrix structural stiffness matrix estimate scaling factor on structural stiffness matrix => identify plate E-modulus = 0

27 model based virtual sensing validation: microphone based parameter estimation different start values converge to the same estimate identified value initial guess Computational time 2 minutes

28 model based virtual sensing validation: microphone based parameter estimation

29 overview KU Leuven team Industry research strategy and approach some vibro-acoustic innovations: o virtual sensing o metamaterials by design o model based geometry characterisation o model based material characterisation

30 metamaterials by design objectives material systems with good noise and vibration insulation properties at o low-mass o low-volume o low-frequency o low-manufacturing cost approach resonant meta-materials stopband behaviour at selected design frequencies

31 +20% mass (local) +20% mass (spread) Target Average Displacement [db] Frequency [Hz]

32 metamaterials by design unit cell modelling Propagation Direction Stop Band

33 metamaterials by design Resonant Inclusions

34 metamaterials by design 12.5 mm 12.5 mm mass spring

35 metamaterials by design

36 metamaterials by design unit cell modelling what about attenuation factors topology optimisation sound transmission loss predictions Propagation direction? Finite structure modelling

37 metamaterials by design unit cell modelling - damping from classical inverse approach to direct solution approach based on Wave FEM Approach, allowing o imaginary and real wavenumbers o and inclusion of damping in materials L. Van Belle, C. Claeys, E. Deckers, W. Desmet, Modelling, analysis and experimental validation of locally resonant metamaterials including damping, Journal of Sound and Vibration, under review

38 metamaterials by design unit cell modelling topology optimization density-based topology optimization: single phase layouts (solid-void) are considered. o starting from solid plate o enforcing resonant behaviour L. Noël, C. Claeys, E. Deckers, W. Desmet, WCSMO 12 (5-9 June 2017, Braunschweig, Germany): Designing metamaterials for enhanced noise and vibration properties.

39 metamaterials by design unit cell modelling sound transmission loss hybrid method o wave based method: acoustic domains o FEM: structural domain predicts absorption and transmission for 2D infinite periodic structures WBM FEM WBM E. Deckers, S. Jonckheere, L. Van Belle, C. Claeys, W. Desmet, Prediction of transmission, reflection and absorption coefficients of periodic structures using a hybrid Wave Based - Finite Element unit cell method Journal of Comp.Physics, under review

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42 70 mm 70 mm

43 overview KU Leuven team Industry research strategy and approach some vibro-acoustic innovations: o virtual sensing o metamaterials by design o model based geometry characterisation o model based material characterisation

44 model based geometrical characterisation objectives dimensional quality control of manufactured structures through vibro-acoustic testing approach vibro-acoustic models with a direct link to the geometrical parameters vibro-acoustic testing (dynamic input accelero/mic responses) inverse method: retrieve geometrical parameters from dynamic response measurements challenge isogeometrical analysis models linking digital geometry with functional (vibro-acoustic) performance

45 model based geometrical characterisation IGA IsoGeometrical Analysis for vibro-acoustics CAE bottleneck = pre-processing: CAD geometry CAE geometry design through analysis

46 model based geometrical characterisation IGA IsoGeometrical Analysis for vibro-acoustics isogeometric: use CAD descriptions directly in CAE NURBS for both geometry & field variable no meshing smoother shape functions less numerical dispersion quadratic splines quadratic polynomials

47 model based geometrical characterisation IGA IsoGeometrical Analysis for vibro-acoustics lack of volumetric discretizations: CAD = object envelope = surface complex representations of free-form geometries NURBS = tensor product free-form free-form CAD = multipatch NURBS = trimmed

48 IGA for vibro-acoustics: main challenges Lack of volumetric discretizations: CAD = object envelope = surface isogeometric BEM Complex representations of free-form geometries NURBS = tensor product free-form free-form CAD = multipatch NURBS = trimmed multipatch coupling techniques L. Coox, O. Atak, D. Vandepitte, W. Desmet. An isogeometric indirect boundary element method for solving acoustic problems in open-boundary domains, Comput. Methods Appl. Mech. Engrg., 316: , L. Coox, F. Greco, O. Atak, D. Vandepitte, W. Desmet. A robust patch coupling method for NURBS-based isogeometric analysis of non-conforming multipatch surfaces, Comput. Methods Appl. Mech. Engrg., 316: , 2017.

49 Isogeometric BEM: bass-reflex loudspeaker NURBS patches 72 interfaces

50 Isogeometric BEM: bass-reflex loudspeaker 50

51 Isogeometric BEM: bass-reflex loudspeaker Directivity plot [db] 1m radius Hz Hz 51

52 model based geometrical characterisation to be exploited for geometrical characterization to be exploited for dimensional quality control.

53 model based geometrical characterisation starting from a reference CAD model of the component, the geometry is updated using the information from the measurements. following the framework of IGA shape optimization, the control points can be directly used as optimization variables.

54 model based geometrical characterisation an optimization problem is solved and the exact geometry can be extracted from the updated IGA model. since a high accuracy is required, many control points are used for the optimization and MOR is applied.

55 overview KU Leuven team Industry research strategy and approach some vibro-acoustic innovations: o virtual sensing o metamaterials by design o model based geometry characterisation o model based material characterisation

56 model based material characterisation objectives retrieving material parameters through vibro-acoustic testing approach vibro-acoustic model including parameterized material models vibro-acoustic testing (dynamic input accelero/mic responses) inverse method: retrieve material parameters from dynamic response measurements challenge efficient vibro-acoustic models for a family of material parameter values... parametric Model Order Reduction (pmor)

57 model based material characterisation parametric Model Order Reduction (pmor) o accurate model for large parameter range o high on-line performance small reduced order model stable o off-line calculation time is not that important goal: where

58 model based material characterisation parametric Model Order Reduction (pmor) two main approaches: 1) global basis and concatenate the local bases 2) interpolation of local information local bases or local reduced order matrices p 3 p 2 p 1

59 pmor application model updating by parameter optimization

60 pmor application model updating by parameter optimization Full order model DOF : Computational time : 5h parametric reduced order model DOF : 180 Computational time : 2s

61 pmor application model updating by parameter optimization = arg min, ( ), ( ), ( ), ( ) log ( ) Initial design parameters Optimized design parameters

62 Matrix-free MOR scheme - Basics = Reduced Order Modelling scheme to further speed-up FRF calculations,,,, Iterative Rational Krylov Matrix-free Adaptively enriched Rational interpolation functions Based on system responses (non-modal) No explicit system matrices necessary = BLACK BOX

63 Matrix-free MOR scheme Procedure 1. Get the system transfer functions for the input-output pairs you are interested in, e.g. from LTI formulation: 2. Apply matrix-free formulation of rational Krylov projection to build the ROM matrices [ ] using left (i) and right (j) projection vectors [ ] = ω H ω ω H ω ω ω [ ] =H ω [ ] = H ω H ω ω ω [ ] =H ω 3. Calculate the approximated full system response from the ROM at all frequencies H =H ω ω + H ω 4. (Iterative enrichment until convergence)

64 Matrix-free MOR scheme Application Plate (0.5x0.25x0.0006m) Steel Boundary condtions o Symmetry edges (red) o Clamped edges (green) Boundary acceleration Center point response Treatment (0.49x0.34x0.0015m) CLD o 1.373mm soft rubber o 127µm aluminium sheet) Frequency [Hz]

65 Matrix-free MOR scheme Results Bare Constr. # Full DOF # Iterations # Frequencies (red.) # Frequencies (full) Speed-up 18.5x 45.5x

66 thank you Wim Desmet Celestijnenlaan 300B box Leuven, Belgium tel mobile