Minimum residual based model order reduction approach for
unsteady nonlinear aerodynamic problems M. Ripepi1, R. Zimmermann2 and S. GΓΆrtz1
1German Aerospace Center (DLR), Institute of Aerodynamics and Flow Technology, Braunschweig, Germany 2Institute Computational Mathematics, TU Braunschweig, Braunschweig, Germany
Data-driven Model Order Reduction and Machine Learning Workshop University of Stuttgart, Germany 30 March 2016 β 1 April 2016
1. Motivation and objectives
2. Model order reduction for unsteady CFD
3. Accelerated greedy MPE procedure
4. Aeronautical application: the LANN wing
5. Summary and outlook
DLR.de β’ Chart 2
Overview
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Prediction of: Aerodynamic performance "Handling Qualities" Structural loads
Input for: Aerodynamic design Structural design and sizing Design of control surfaces
and flight control system Flight simulators
flaps up
stall area
stall area
Negative limit load factor
positive limit load factor
Cruise Velocity
Motivation From design to certification of an aircraft many aerodynamic data are
needed β for the entire flight envelope β All required aerodynamic data can be derived from the pressure and
shear stress distribution on the aircraft surface, from steady and unsteady simulations
DLR.de β’ Chart 3
Compressibility
Unsteady effects
Separated flows
normal operation range
CFD is mostly limited to the cruise point
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
CFD too expensive to procure all the necessary Aero Data (in particular in multidisciplinary settings requiring repeated simulations)
Objectives
Development of a simulation environment, where ALL the necessary aerodynamic data can be determined numerically
with high accuracy with Hi-Fi methods
Result!
Solution(ππ1, ππ2, β¦)?
DLR.de β’ Chart 4
ROMs for compressible flows
To reduce the computational complexity, Reduced Order Models based on Hi-Fi CFD should replace (e.g. in the MultiDisciplinary Optimization)
high quality models and methods
ROM
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
CFD
Objectives
Development of a simulation environment, where ALL the necessary aerodynamic data can be determined numerically
with high accuracy with Hi-Fi methods
DLR.de β’ Chart 5
ROMs for compressible flows
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Reduced Order Model
Snapshots ππππ(ππππ1, β¦ )
Prediction πποΏ½(ππ1, β¦ )
πΌπΌ = 0Β°
πΌπΌ = 2Β°
πΌπΌ = 6Β°
πΌπΌ = 4Β° Offline
Online
Restricted range of validity
Low-dim. (r << n ODEs) description of large-scale
system dynamics
Wide range of validity
Full-order model, high-fidelity CFD data (system of n ODEs)
ROMs for the prediction of steady aerodynamic flows
Model order reduction approach for unsteady aerodynamic applications
Model order reduction for unsteady CFD
π°π° t10 π°π° t60 π°π° t110 β― β―
Search for an approximated solution π°π° = [Ο, Οπ―π―, ΟπΈπΈπ‘π‘] β βππ in the POD subspace ππ β βππ x ππ , ππ βͺ ππ minimizing the unsteady residual in the L2 norm
π°π° βοΏ½ππππππππ
ππ
ππ=1
+ π°π° = ππππ + π°π°
π°π° =β π°π° tiππππ=1ππ
: average of the snapshots
ππ: unknown coefficients
DLR.de β’ Chart 6 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
N: order of CFD model (variables x nodes)
r: order of the ROM (i.e. number of POD modes)
Proper Orthogonal Decomposition
ππ = π°π° t1 , . . . ,π°π° tm βπ°π°
SVD: ππ = ππππππππ EVD: ππ = ππππππ = ππππππππππ
minππ
β₯ πποΏ½ ππππ + π°π° β₯L22
πποΏ½ β ππ π°π° t + πππππ°π° tπππ
= 0 β βππ ππ: cell volumes Semi-discrete Navier-Stokes Eqs.
πποΏ½ β ππ π°π°rom tn+1 + ππ3π°π°rom tn+1 β 4π°π°rom tn + π°π°rom tnβ1
2Ξπ
Per each physical time step, find the POD coefficients minimizing the unsteady residual at the time step n+1, by solving a nonlinear least squares problem: π°π°rom tn+1 β ππππ+ π°π°
ππTππ + ππππ Ξππ = βππTπποΏ½
DLR.de β’ Chart 7
Model order reduction for unsteady CFD
Broydenβs method for updating ππ
ππππ+1 = ππππ +βπποΏ½ β ππππ Ξππ
Ξππ 2 ΞππT
N: order of CFD model (variables x nodes)
r: order of the ROM (i.e. number of POD modes)
Updating pseudo-Hessian matrix ππ β‘ ππTππ β βππΓππ β πͺπͺ Nr2
ππππ+1 β‘ ππππ+1T ππππ+1
= ππππ + ππππT βπποΏ½ β ππππ Ξππ
Ξππ 2 ΞππT + ΞππβπποΏ½T ππππ β ΞππTππππ
Ξππ 2
+ ΞππβπποΏ½TβπποΏ½ β βπποΏ½Tππππ Ξππ β ΞππTππππT βπποΏ½ + ΞππTππππΞππ
Ξππ 4 ΞππT
β πͺπͺ Nr
ππ β βπ΅π΅Γππ
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
ππ β ππ + Ξππ
until
co
nver
genc
e
Training maneuvers Schroeder multisine with 0.01 < k < 0.2
16430 elements 21454 points
RANS equations with Spalart-Allmaras turbulence model Mach = 0.8, Re = 7.5 106
Training maneuvers exciting up to k β ππππππβ
= 0.2
Prediction for periodic motions at different frequencies and amplitudes (using the same ROM)
Linear chirp signal with kmax = 0.2
NACA 64A010
DLR.de β’ Chart 8
2D Test Case: NACA airfoil Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
2D Test Case: NACA airfoil Periodic pitching motion prediction at different oscillation frequencies
DLR.de β’ Chart 9 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
ROM built with 50 POD modes
0.08
0.24 k
0.08
0.24
k
DLR.de β’ Chart 10 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Online Performances TAU ROM
Chirp Schroeder
Average CPU Time
total 15 min 58 s 8 min 58 s 8 min 43 s per time step 6.39 s 3.56 s 3.49 s speed-up 1 1.79 1.83
ROM built with 50 POD modes
2D Test Case: NACA airfoil Periodic pitching motion prediction at different oscillation amplitudes
2Β°
3Β°
Ξ±A Ξ±A
3Β°
2Β°
Ways to improve the performances:
Hyper-reduction (e.g. Gappy POD) Different selection of the subspace
(e.g. Dynamic Mode Decomposition)
I. compute the approx. solution
II. evaluate the unsteady residual
III. solve the LS problem
DLR.de β’ Chart 11 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Model order reduction for unsteady CFD Curse of dimensionality in ROMs of nonlinear systems
πποΏ½ π°π° t β ππ π°π° t + πππππ°π° tπππ
π°π° t β ππ ππ t + π°π° β πͺπͺ Nr N: order of CFD model (variables x nodes)
r: order of the ROM (i.e. number of POD modes)
Jacobian Matrix ππ β βπ΅π΅Γππ
nonlinear least squares problem ππππππππ
β₯ πποΏ½ ππππ + π°π° β₯L22
The computational cost scales linearly with the dimension N of the full order model. No significant speedup can be expected when solving the minimum residual ROM.
ππTππ + ππππ Ξππ = βππTπποΏ½ β πͺπͺ Nr
i. Compute RHS βππTπποΏ½ ii. Broydenβs method for updating ππ iii. Updating pseudo-Hessian matrix
ππ β‘ ππTππ β βππΓππ
DLR.de β’ Chart 12 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Hyper-reduction approaches
Selecting the subset indices Gappy POD Missing Point Estimation (Discrete) Empirical Interpolation Method
Complexity reduction by sampling (or compute only a few entries of) the nonlinear unsteady residual vector πποΏ½
πποΏ½
πποΏ½οΏ½
πποΏ½β
Reconstruction approximation of the entire
vector, by interpolation or by least-squares projection onto a subspace ππ = U ππππP ππππU β1ππππP
Collocation omission of many components non intrusive
= V
ππ
mask matrix
ππTπποΏ½
U ππππP ππππU β1ππππP ππππ= 1/ππmin ππππππ
Greedy: minimize
The complete nonlinear unsteady residual vector πποΏ½ is evaluated, but only a small subset of its entries are used in the minimization process
Exhaustive greedy MPE maximize ππmin πππ π +1ππ ππ Input: ππ β βππΓππ: basis of a r-dim subspace,
πππ π β βπ π Γ1: index set, πππ π β βππΓπ π : mask matrix with s indices, where s β₯ r.
Output: next index πππ π +1 = πππ π βͺ ππopt , πππ π +1ππ = πππ π , ππππ, opt
DLR.de β’ Chart 13 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Exhaustive greedy missing point estimation procedure
πππ π 2 5 7
πππ π
ππ2 ππ5 ππ7
Greedy point selection algorithms minimize an error indicator by sequentially looping over all entries costly β > πͺπͺ Nr3
Singular Value Decomposition
π―π―ππ β ππππ, π π +1ππ πππ½π½π π
ππ
Rank-one SVD update
πππππππ π +1πππ π +1ππ ππ = π½π½ππ πΊπΊπ π 2 + π―π―πππ―π―ππππ π½π½π π ππ
0 < ππππ < 1 β ππππππ
ππ=1 = 1 ππππ = ππππ2 + ππππ π―π― 2
Eigenvalues
Symmetric rank-one eigenvalue modification
R. Zimmermann and K. Willcox. An accelerated greedy missing point estimation procedure. SIAM Journal on Scientific Computing, 2/2016. Preprint.
The penultimate singular value bounds the growth of ππmin
Observation 2)
Observation 1)
DLR.de β’ Chart 14 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Accelerated greedy missing point estimation procedure
R. Zimmermann and K. Willcox. An accelerated greedy missing point estimation procedure. SIAM Journal on Scientific Computing, 2/2016. Preprint.
Select the vector π―π―ππ leading to the largest growth in the smallest eigenvalue ππmin π―π―ππ of ππ β πΊπΊπ π 2 + π―π―πππ―π―ππππ β βππΓππ
Build fast approximations that sort the set of candidate vectors that induce the rank-one modifications ( without solving the modified eigenvalue pb.)
Fast greedy MPE with target switching based on the growth potential β πͺπͺ Nr2 Input: ππ β βππΓππ: basis of a r-dim subspace, πππ π β βπ π Γ1: index set, s β₯ r.
1. οΏ½Μ οΏ½πs = 1, β¦ ,ππ \ πππ π 2. while πππ π β€ maxpoints do 3. target the largest growth for ππmin (i.e. ππr) or the next biggest ππrβ1, etc. 4. determine ππopt by sorting π―π―ππ , ππ β οΏ½Μ οΏ½πs according to the fast estimate 5. update: ππs+1 = πππ π βͺ ππopt , οΏ½Μ οΏ½πs+1 = οΏ½Μ οΏ½πs \ ππopt , π π = π π + 1 Output: index set πππ π
Idea:
ππrβππ < β±(ππππβππ2 ,ππππβππ+12 , π£π£ππ) Error bound
Select the vectors π―π―ππ, opt that feature the largest absolute values in the ultimate component while all other components are comparably small.
DLR.de β’ Chart 15 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
500 MPE nodes 50 POD modes Total number of grid nodes: 21454
2D Test Case: NACA airfoil Nodes selected with the fast greedy MPE
2D Test Case: NACA airfoil Periodic pitching motion prediction at k=0.16
DLR.de β’ Chart 16 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Maximum lift minimum lift
1000 nodes selected by the
greedy MPE
< 5% of the total number of grid nodes
DLR.de β’ Chart 17 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Online Performances (1 core)
CFD TAU
ROM (built with 50 POD modes)
w/o MPE with MPE
100 nodes 500 nodes 1000 nodes
CPU Time
Total [min] ~18 ~9 ~5 ~5 ~6 per time step [s] 6.98 3.65 2.10 2.17 2.30
speed-up 1 1.91 3.31 (+42%)
3.22 (+41%)
3.03 (+37%)
Offline Performances (1 core) Compute 400 snapshots by a training unsteady CFD simulation: ~47 min POD ROM building: 1min 32s Fast greedy MPE (selecting 1000 nodes): ~2 min
2D Test Case: NACA airfoil Performances
Process CFD TAU
ROM (built with 50 POD modes) w/o MPE with MPE (1000 nodes)
CPU Time
Building the model β ~49 min ~51 min Simulate 7 parameters (5 reduced frequencies and 2 amplitudes)
2 h 5 min 1 h 3 min 42 min
450560 elements 469213 points
3D test case: LANN wing
DLR.de β’ Chart 18
RANS equations with SA-neg turbulence model Vβ = 271.66 m/s, Mach = 0.82, Re = 7.17 106
Chirp training maneuver exciting up to k β ππππππππβ
= 0.35
Predicted periodic pitching oscillation: πΌπΌ ππ = 2.6Β° + 0.25 sin(π€π€ ππ) , Ο β ππβπ‘π‘ππππ
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
CFD settings Min residual: 1e-5 Max inner iterations: 100
ROM settings 18 POD modes 5000 MPE nodes
DLR.de β’ Chart 19 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Total number of grid nodes: 469213
5000 nodes selected by the greedy MPE
3D test case: LANN wing
~1% of the total number of grid nodes
k=0.3
DLR.de β’ Chart 20 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
3D test case: LANN wing
Online Performances CFD
(10 cores)
ROM (10 cores)
w/o MPE with MPE
Average Wall Time
total 25 h 50 min 2 h 44 min 1 h 29 min per time step ~16 min 1 min 36 s 36 s speed-up 1 9.5 26.7
5000
Compute snapshots: 4d 10h 40min POD ROM building: ~5 min Fast greedy MPE: ~6 min
Offline Performances (10 cores)
The effectiveness of reduced order models for unsteady aerodynamic problems have been demonstrated for a NACA airfoil and the LANN wing in transonic flows.
Remarks
Training maneuvers may be an issue: the quality of the resulting model will reflect the input signal quality
Further analyses are required (e.g. moving window, full-aircraft config., β¦)
Summary and Outlook
Source: DLR-AS
MDO: 2.5g maneuver with aileron deflection
Assessment of Spoiler Concepts
DLR.de β’ Chart 21
Aeroelastic Gust Loads
Possible improvements Different selection of the subspace (e.g. Dynamic Mode
Decomposition, Isomap) Different ordering of the modes, focusing more on the
input-output behavior (e.g. subspace rotation) Hybrid strategy by switching in time ROM/CFD Sparsity promoting techniques (L1 norm, clustering)
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Scenarios
Multidisciplinary applications
Real-time applications
Design and optimization
Probabilistic applications
ROMs
DLR.de β’ Chart 22 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Acknowledgement to the EU: Part of the research leading to this work was supported by the AEROGUSTproject, funded by the European Commission under grant number 636053. COST Action TD1307: EU-MORNET, Model Reduction Network.
German National Research Projects Lufo-IV joint research project AeroStruct
Internal DLR Projects DLR multidisciplinary project Digital-X
Acknowledgements
DLR.de β’ Chart 23 Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016
Bibliography
DLR.de β’ Chart 24
T. Franz, R. Zimmermann, S. GΓΆrtz, and N. Karcher. Interpolation-based reduced-order modelling for steady transonic flows via manifold learning. InternationalJournal of Computational Fluid Dynamics, 28(3β4):106β121, 2014.
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R. Zimmermann and S. GΓΆrtz. Non linear reduced order models for steadyaerodynamics. In Procedia Computer Science, volume 1, pages 165β174, 2010.Proceedings of the International Conference on Computational Science, ICCS 2010.
R. Zimmermann and K. Willcox. An accelerated greedy missing point estimationprocedure. SIAM Journal on Scientific Computing, 2/2016. Preprint.
Model order reduction for unsteady aerodynamic problems > Matteo Ripepi β’ MORML 2016 workshop > University of Stuttgart, Germany. 30 Mar.β1 Apr. 2016