Adaptive modeling for free-surface flows

Simona Perotto

ESAIM: Mathematical Modelling and Numerical Analysis (2006)

  • Volume: 40, Issue: 3, page 469-499
  • ISSN: 0764-583X

Abstract

top
This work represents a first step towards the simulation of the motion of water in a complex hydrodynamic configuration, such as a channel network or a river delta, by means of a suitable “combination” of different mathematical models. In this framework a wide spectrum of space and time scales is involved due to the presence of physical phenomena of different nature. Ideally, moving from a hierarchy of hydrodynamic models, one should solve throughout the whole domain the most complex model (with solution u fine ) to accurately describe all the physical features of the problem at hand. In our approach instead, for a user-defined output functional , we aim to approximate, within a prescribed tolerance τ, the value ( u fine ) by means of the quantity ( u adapted ) , u adapted being the so-called adapted solution solving the simpler models on most of the computational domain while confining the complex ones only on a restricted region. Moving from the simplified setting where only two hydrodynamic models, fine and coarse, are considered, we provide an efficient tool able to automatically select the regions of the domain where the coarse model rather than the fine one are to be solved, while guaranteeing | ( u fine ) - ( u adapted ) | below the tolerance τ. This goal is achieved via a suitable a posteriori modeling error analysis developed in the framework of a goal-oriented theory. We extend the dual-based approach provided in [Braack and Ern, Multiscale Model Sim.1 (2003) 221–238], for steady equations to the case of a generic time-dependent problem. Then this analysis is specialized to the case we are interested in, i.e. the free-surface flows simulation, by emphasizing the crucial issue of the time discretization for both the primal and the dual problems. Finally, in the last part of the paper a widespread numerical validation is carried out.

How to cite

top

Perotto, Simona. "Adaptive modeling for free-surface flows." ESAIM: Mathematical Modelling and Numerical Analysis 40.3 (2006): 469-499. <http://eudml.org/doc/249756>.

@article{Perotto2006,
abstract = { This work represents a first step towards the simulation of the motion of water in a complex hydrodynamic configuration, such as a channel network or a river delta, by means of a suitable “combination” of different mathematical models. In this framework a wide spectrum of space and time scales is involved due to the presence of physical phenomena of different nature. Ideally, moving from a hierarchy of hydrodynamic models, one should solve throughout the whole domain the most complex model (with solution $u_\{\rm\{fine\}\}$) to accurately describe all the physical features of the problem at hand. In our approach instead, for a user-defined output functional $\{\cal F\}$, we aim to approximate, within a prescribed tolerance τ, the value $\{\cal F\}(u_\{\rm\{fine\}\})$ by means of the quantity $\{\cal F\}(u_\{\rm\{adapted\}\})$, $u_\{\rm\{adapted\}\}$ being the so-called adapted solution solving the simpler models on most of the computational domain while confining the complex ones only on a restricted region. Moving from the simplified setting where only two hydrodynamic models, fine and coarse, are considered, we provide an efficient tool able to automatically select the regions of the domain where the coarse model rather than the fine one are to be solved, while guaranteeing $|\{\cal F\}(u_\{\rm\{fine\}\}) -\{\cal F\}(u_\{\rm\{adapted\}\})|$ below the tolerance τ. This goal is achieved via a suitable a posteriori modeling error analysis developed in the framework of a goal-oriented theory. We extend the dual-based approach provided in [Braack and Ern, Multiscale Model Sim.1 (2003) 221–238], for steady equations to the case of a generic time-dependent problem. Then this analysis is specialized to the case we are interested in, i.e. the free-surface flows simulation, by emphasizing the crucial issue of the time discretization for both the primal and the dual problems. Finally, in the last part of the paper a widespread numerical validation is carried out. },
author = {Perotto, Simona},
journal = {ESAIM: Mathematical Modelling and Numerical Analysis},
keywords = {Modeling adaptivity; a posteriori error estimate; goal-oriented analysis; free-surface flows; dual problem; finite elements.; modeling adaptivity; a posteriori error estimate; goal-oriented analysis; free-surface flows; finite elements},
language = {eng},
month = {7},
number = {3},
pages = {469-499},
publisher = {EDP Sciences},
title = {Adaptive modeling for free-surface flows},
url = {http://eudml.org/doc/249756},
volume = {40},
year = {2006},
}

TY - JOUR
AU - Perotto, Simona
TI - Adaptive modeling for free-surface flows
JO - ESAIM: Mathematical Modelling and Numerical Analysis
DA - 2006/7//
PB - EDP Sciences
VL - 40
IS - 3
SP - 469
EP - 499
AB - This work represents a first step towards the simulation of the motion of water in a complex hydrodynamic configuration, such as a channel network or a river delta, by means of a suitable “combination” of different mathematical models. In this framework a wide spectrum of space and time scales is involved due to the presence of physical phenomena of different nature. Ideally, moving from a hierarchy of hydrodynamic models, one should solve throughout the whole domain the most complex model (with solution $u_{\rm{fine}}$) to accurately describe all the physical features of the problem at hand. In our approach instead, for a user-defined output functional ${\cal F}$, we aim to approximate, within a prescribed tolerance τ, the value ${\cal F}(u_{\rm{fine}})$ by means of the quantity ${\cal F}(u_{\rm{adapted}})$, $u_{\rm{adapted}}$ being the so-called adapted solution solving the simpler models on most of the computational domain while confining the complex ones only on a restricted region. Moving from the simplified setting where only two hydrodynamic models, fine and coarse, are considered, we provide an efficient tool able to automatically select the regions of the domain where the coarse model rather than the fine one are to be solved, while guaranteeing $|{\cal F}(u_{\rm{fine}}) -{\cal F}(u_{\rm{adapted}})|$ below the tolerance τ. This goal is achieved via a suitable a posteriori modeling error analysis developed in the framework of a goal-oriented theory. We extend the dual-based approach provided in [Braack and Ern, Multiscale Model Sim.1 (2003) 221–238], for steady equations to the case of a generic time-dependent problem. Then this analysis is specialized to the case we are interested in, i.e. the free-surface flows simulation, by emphasizing the crucial issue of the time discretization for both the primal and the dual problems. Finally, in the last part of the paper a widespread numerical validation is carried out.
LA - eng
KW - Modeling adaptivity; a posteriori error estimate; goal-oriented analysis; free-surface flows; dual problem; finite elements.; modeling adaptivity; a posteriori error estimate; goal-oriented analysis; free-surface flows; finite elements
UR - http://eudml.org/doc/249756
ER -

References

top
  1. R.L. Actis, B.A. Szabo and C. Schwab, Hierarchic models for laminated plates and shells. Comput. Methods Appl. Mech. Engrg.172 (1999) 79–107.  
  2. V.I. Agoshkov, D. Ambrosi, V. Pennati, A. Quarteroni and F. Saleri, Mathematical and numerical modelling of shallow water flow. Comput. Mech.11 (1993) 280–299.  
  3. V.I. Agoshkov, A. Quarteroni and F. Saleri, Recent developments in the numerical simulation of shallow water equations I: boundary conditions. Appl. Numer. Math.15 (1994) 175–200.  
  4. M. Amara, D. Capatina-Papaghiuc and D. Trujillo, Hydrodynamical modelling and multidimensional approximation of estuarian river flows. Comput. Visual. Sci.6 (2004) 39–46.  
  5. W. Bangerth and R. Rannacher, Adaptive finite element techniques for the acoustic wave equation. J. Comput. Acoust.9 (2001) 575–591.  
  6. Z.P. Bažant, Spurious reflection of elastic waves in nonuniform finite element grids. Comput. Methods Appl. Mech. Engrg.16 (1978) 91–100.  
  7. R. Becker and R. Rannacher, An optimal control approach to a posteriori error estimation in finite element methods, in Acta Numerica 2001, A. Iserles Ed., Cambridge University Press, Cambridge, UK (2001).  
  8. J.P. Benque, A. Haugel and P.L. Viollet, Numerical methods in environmental fluid mechanics, in Engineering Applications of Computational Hydraulics, M.B. Abbott and J.A. Cunge Eds., Vol. II (1982).  
  9. M. Braack and A. Ern, A posteriori control of modeling errors and discretizatin errors. Multiscale Model. Simul.1 (2003) 221–238.  
  10. Ph. Ciarlet, The Finite Element Method for Elliptic Problems. North-Holland Publishing Company, Amsterdam (1978).  
  11. J.M. Cnossen, H. Bijl, B. Koren and E.H. van Brummelen, Model error estimation in global functionals based on adjoint formulation, in International Conference on Adaptive Modeling and Simulation, ADMOS 2003, N.-E. Wiberg and P. Díez Eds., CIMNE, Barcelona (2003).  
  12. A. Ern, S. Perotto and A. Veneziani, Finite element simulation with variable space dimension. The general framework (2006) (in preparation).  
  13. M. Feistauer and C. Schwab, Coupling of an interior Navier-Stokes problem with an exterior Oseen problem. J. Math. Fluid. Mech.3 (2001) 1–17.  
  14. L. Formaggia and A. Quarteroni, Mathematical Modelling and Numerical Simulation of the Cardiovascular System, in Handbook of Numerical Analysis, Vol. XII, North-Holland, Amsterdam (2004) 3–127.  
  15. L. Formaggia, F. Nobile, A. Quarteroni and A. Veneziani, Multiscale modelling of the circolatory system: a preliminary analysis. Comput. Visual. Sci.2 (1999) 75–83.  
  16. M.B. Giles and N.A. Pierce, Adjoint equations in CFD: duality, boundary conditions and solution behaviour, in 13th Computational Fluid Dynamics Conference Proceedings (1997) AIAA paper 97–1850.  
  17. M.B. Giles and E. Süli, Adjoint methods for PDEs: a posteriori error analysis and postprocessing by duality. Acta Numerica11 (2002) 145–236.  
  18. E. Godlewski and P.A. Raviart, Numerical Approximation of Hyperbolic Systems of Conservation Laws. Springer-Verlag, New York (1996).  
  19. A. Griewank and A. Walther, Revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation. ACM T. Math. Software26 (2000) 19–45.  
  20. I. Harari, Reducing spurious dispersion, anisotropy and reflection in finite element analysis of time-harmonic acoustics. Comput. Methods Appl. Mech. Engrg.140 (1997) 39–58.  
  21. J.L. Lions and E. Magenes, Non-Homogeneous Boundary Value Problems and Applications. Volume I. Springer-Verlag, Berlin (1972).  
  22. G.I. Marchuk, Adjoint Equations and Analysis of Complex Systems. Kluwer Academic Publishers, Dordrecht (1995).  
  23. G.I. Marchuk, V.I. Agoshkov and V.P. Shutyaev, Adjoint equations and perturbation algorithms in nonlinear problems. CRC Press (1996).  
  24. S. Micheletti and S. Perotto (2006) (in preparation).  
  25. E. Miglio, S. Perotto and F. Saleri, A multiphysics strategy for free-surface flows, Domain Decomposition Methods in Science and Engineering, R. Kornhuber, R.H.W. Hoppe, J. Périaux, O. Pironneau, O. Widlund, J. Xu Eds., Springer-Verlag, Lect. Notes Comput. Sci. Engrg.40 (2004) 395–402.  
  26. E. Miglio, S. Perotto and F. Saleri, Model coupling techniques for free-surface flow problems. Part I. Nonlinear Analysis63 (2005) 1885–1896.  
  27. J.T. Oden and S. Prudhomme, Estimation of modeling error in computational mechanics. J. Comput. Phys.182 (2002) 469–515.  
  28. J.T. Oden and K.S. Vemaganti, Estimation of local modeling error and goal-oriented adaptive modeling of heterogeneous materials. I. Error estimates and adaptive algorithms. J. Comput. Phys.164 (2000) 22–47.  
  29. J.T. Oden and K.S. Vemaganti, Estimation of local modeling error and goal-oriented adaptive modeling of heterogeneous materials. II. A computational environment for adaptive modeling of heterogeneous elastic solids. Comput. Methods Appl. Mech. Engrg.190 (2001) 6089–6124.  
  30. J.T. Oden, S. Prudhomme, D.C. Hammerand and M.S. Kuczma, Modeling error and adaptivity in nonlinear continuum mechanics. Comput. Method. Appl. M.190 (2001) 6663–6684.  
  31. A. Quarteroni and L. Stolcis, Heterogeneous domain decomposition for compressible flows, in Proceedings of the ICFD Conference on Numerical Methods for Fluid Dynamics, M. Baines and W.K. Morton Eds., Oxford University Press, Oxford (1995) 113–128.  
  32. A. Quarteroni and A. Valli, Domain decomposition methods for partial differential equations. Oxford University Press Inc., New York (1999).  
  33. M. Schulz and G. Steinebach, Two-dimensional modelling of the river Rhine. J. Comput. Appl. Math.145 (2002) 11–20.  
  34. E. Stein and S. Ohnimus, Anisotropic discretization- and model-error estimation in solid mechanics by local Neumann problems. Comput. Methods Appl. Mech. Engrg.176 (1999) 363–385.  
  35. G.S. Stelling, On the construction of computational models for shallow water equations. Rijkswaterstaat Communication35 (1984).  
  36. C.B. Vreugdenhil, Numerical Methods for Shallow-Water Flows. Kluwer Academic Press, Dordrecht (1998).  
  37. G.B. Whitham, Linear and Nonlinear Waves. Wiley, New York (1974).  
  38. F.W. Wubs, Numerical solution of the shallow-water equations. CWI Tract, 49, F.W. Wubs Ed., Amsterdam (1988).  

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.