POD basis interpolation via Inverse Distance Weighting on Grassmann manifolds

Rolando Mosquera; Aziz Hamdouni; Abdallah El Hamidi; Cyrille Allery

doi:10.3934/dcdss.2019115

Article Contents

2019, Volume 12, Issue 6: 1743-1759. Doi: 10.3934/dcdss.2019115

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POD basis interpolation via Inverse Distance Weighting on Grassmann manifolds

Laboratoire LaSIE, Université de La Rochelle, Avenue M. Crépeau, 17042 La Rochelle, France

* Corresponding author: Abdallah El Hamidi
* Corresponding author: Abdallah El Hamidi

Received: January 31, 2018

Revised: March 31, 2018

Published: April 2019

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Abstract

An adaptation of the Inverse Distance Weighting (IDW) method to the Grassmann manifold is carried out for interpolation of parametric POD bases. Our approach does not depend on the choice of a reference point on the Grassmann manifold to perform the interpolation, moreover our results are more accurate than those obtained in [7]. In return, our approach is not direct but iterative and its relevance depends on the choice of the weighting functions which are inversely proportional to the distance to the parameter. More judicious choices of such weighting functions can be carried out via kriging technics [23], this is the subject of a work in progress.

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Mathematics Subject Classification: 65K05, 65D18, 65F15.

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Figure 1. Geometrical properties of the flow around a circular cylinder

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Figure 2. Isovalue of the velocity magnitude for Re = 180 at the first snapshot

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Figure 3. Influence of the power $p$ of the IDW-G interpolation on the prediction of the drag and lift for Reynolds number $Re = 160$ and $Re = 190$

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Figure 4. Comparison of the drag and lift coefficients obtained by the two interpolations methods IDW-G and Grassmann and those obtained with the full model for Re = 140 and 160

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Figure 5. Comparison of the drag and lift coefficients obtained by the two interpolations methods IDW-G and Grassmann and those obtained with the full model for Re = 170 and 190

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References

	P. A. Absil , R. Mahony and R. Sepulchre , Riemannian geometry of Grassmann manifolds with a view on algorithmic computation, Acta Applicandae Mathematicae, 80 (2004) , 199-220. doi: 10.1023/B:ACAP.0000013855.14971.91.
	B. Afsari , Riemannian L^p center of mass: Existence, uniqueness, and convexity, Proc. Amer. Math. Soc., 139 (2011) , 655-673. doi: 10.1090/S0002-9939-2010-10541-5.
	N. Akkari , A. Hamdouni , E. Liberge and M. Jazar , A mathematical and numerical study of the sensitivity of a reduced order model by POD (ROM-POD), for a 2D incompressible fluid flow, Journal of Computational and Applied Mathematics, 270 (2014) , 522-530. doi: 10.1016/j.cam.2013.11.025.
	N. Akkari , A. Hamdouni and M. Jazar , Mathematical and numerical results on the sensitivity of the POD approximation relative to the Burgers equation, Applied Mathematics and Computation, 247 (2014) , 951-961. doi: 10.1016/j.amc.2014.09.005.
	N. Akkari , A. Hamdouni , E. Liberge and M. Jazar , On the sensitivity of the POD technique for a parameterized quasi-nonlinear parabolic equation, Advanced Modeling and Simulation in Engineering Sciences, 2 (2014) , 1-16.
	Y. Aminov, The Geometry of Submanifolds, Gordon and Breach Science Publishers, 2001.
	D. Amsallem and C. Farhat , An interpolation method for adapting reduced order models and application to aeroelasticity, Amer. Inst. Aeronaut. Astronaut., 46 (2008) , 1803-1813.
	M. Azaïez , F. Ben Belgacem and T. Chacón Rebollo , Error bounds for POD expansions of parameterized transient temperatures, Comput. Methods Appl. Mech. Engrg., 305 (2016) , 501-511. doi: 10.1016/j.cma.2016.02.016.
	M. Azaïez and F. Ben Belgacem , Karhunen-Loève's truncation error for bivariate functions, Comput. Methods Appl. Mech. Engrg., 290 (2015) , 57-72. doi: 10.1016/j.cma.2015.02.019.
	B. Denis de Senneville , A. El Hamidi and C. Moonen , A direct PCA-based approach for real-time description of physiological organ deformations, IEEE Transactions on Medical Imaging, 34 (2015) , 974-982.
	B. Haasdonk , M. Ohlberger and G. Rozza , A reduced basis method for evolution schemes with parameter-dependent explicit operators, Electron. Trans. Numer. Anal., 32 (2008) , 145-161.
	D. Hömberg and S. Volkwein , Control of laser surface hardening by a reduced-order approach utilizing proper orthogonal decomposition, Math. Comput. Model., 38 (2003) , 1003-1028. doi: 10.1016/S0895-7177(03)90102-6.
	H. Karcher , Riemannian center of mass and mollifier smoothing, Comm. Pure Appl. Math., 30 (1977) , 509-541. doi: 10.1002/cpa.3160300502.
	S. E. Kozlov , Geometry of real Grassmannian manifolds, Zap. Nauchn. Semin. POMI, 246 (1997) , 108-129.
	K. Kunisch and S. Volkwein , Control of Burgers equation by a reduced order approach using proper orthogonal decomposition, J. Optim. Theory Appl., 102 (1999) , 345-371. doi: 10.1023/A:1021732508059.
	H. Le , Estimation of Riemannian barycenters, LMS J. Comput. Math., 7 (2004) , 193-200. doi: 10.1112/S1461157000001091.
	C. Leblond , C. Allery and C. Inard , An optimal projection method for the reduce order modeling of incompressible flows, Comp. Meth, in Applied Mechanics and Engineering, 200 (2011) , 2507-2527. doi: 10.1016/j.cma.2011.04.020.
	E. Longatte , E. Liberge , M. Pomarède , J. F. Sigrist and A. Hamdouni , Parametric study of flow-induced vibrations in cylinder arrays under single-phase fluid cross flows using POD-ROM, Journal of Fluids and Structures, 78 (2018) , 314-330.
	Y. Lu , N. Blal and A. Gravouil , Space time POD based computational vademecums for parametric studies: Application to thermo-mechanical problems, Advanced Modeling and Simulation in Engineering Sciences, 5 (2018) , 1-27.
	W. Milnor and J. D. Stasheff, Characteristic Classes, Ann. Math. Studies, Princeton University Press, 1974.
	A. T. Patera and G. Rozza, A Posteriori Error Estimation for Parametrized Partial Differential Equations, MIT Pappalardo Graduate Monographs in Mechanical Engineering, 2007.
	P. Petersen, Riemannian Geometry, Springer-Verlag, 2006.
	D. Pigoli , A. Menafoglio and P. Secchi , Kriging prediction for manifold-valued random fields, Journal of Multivariate Analysis, 145 (2016) , 117-131. doi: 10.1016/j.jmva.2015.12.006.
	S. Roujol , M. Ries , B. Quesson , C. Moonen and B. Denis de Senneville , Real-time MR-thermometry and dosimetry for interventional guidance on abdominal organs, Magnetic Resonance in Medicine, 63 (2010) , 1080-7.
	L. Sirovich , Turbulence and the dynamics of coherent structures, parts Ⅰ-Ⅲ, Quart. Appl. Math., 45 (1987) , 561-571. doi: 10.1090/qam/910462.
	A. Tallet , C. Allery , C. Leblond and E. Liberge , A minimum residual projection to build coupled velocity-pressure POD-ROM for incompressible Navier-Stokes equations, Comm. in Nonlin. Science and Num. Simulation, 22 (2015) , 909-932. doi: 10.1016/j.cnsns.2014.09.009.
	S. Volkwein , Optimal control of a phase-field model using the proper orthogonal decomposition, Z. Angew. Math. Mech., 81 (2001) , 83-97. doi: 10.1002/1521-4001(200102)81:2<83::AID-ZAMM83>3.0.CO;2-R.
	Y. C. Wong , Differential geometry of Grassmann manifolds, Proc Natl Acad Sci U S A., 57 (1967) , 589-594. doi: 10.1073/pnas.57.3.589.