Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-06-01T00:52:46.141Z Has data issue: false hasContentIssue false

A Novel Low-Dimensional Method for Analytically Solving Partial Differential Equations

Published online by Cambridge University Press:  09 September 2015

Jie Sha
Affiliation:
Department of Engineering Mechanics, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
Lixiang Zhang
Affiliation:
Department of Engineering Mechanics, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
Chuijie Wu*
Affiliation:
State Key Laboratory of Structural Analysis for Industrial Equipment School of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, China
*
*Corresponding author. Email: shajie9981@gmail.com (J. Sha), zlxzcc@126.com (L. X. Zhang), cjwudut@dlut.edu.cn (C. J. Wu)
Get access

Abstract

This paper is concerned with a low-dimensional dynamical system model for analytically solving partial differential equations (PDEs). The model proposed is based on a posterior optimal truncated weighted residue (POT-WR) method, by which an infinite dimensional PDE is optimally truncated and analytically solved in required condition of accuracy. To end that, a POT-WR condition for PDE under consideration is used as a dynamically optimal control criterion with the solving process. A set of bases needs to be constructed without any reference database in order to establish a space to describe low-dimensional dynamical system that is required. The Lagrangian multiplier is introduced to release the constraints due to the Galerkin projection, and a penalty function is also employed to remove the orthogonal constraints. According to the extreme principle, a set of ordinary differential equations is thus obtained by taking the variational operation of the generalized optimal function. A conjugate gradient algorithm by FORTRAN code is developed to solve the ordinary differential equations. The two examples of one-dimensional heat transfer equation and nonlinear Burgers’ equation show that the analytical results on the method proposed are good agreement with the numerical simulations and analytical solutions in references, and the dominant characteristics of the dynamics are well captured in case of few bases used only.

Type
Research Article
Copyright
Copyright © Global-Science Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Roweis, Sam T. and Saul, Lawrence K., Nonlinear dimensionality reduction by locally linear embedding, Science, 209 (2000), pp. 2323.Google Scholar
[2]Tenenbaum, Joshua B., de Silva, Vin and Langford, John C., A global geometric framework for nonlinear dimensionality reduction, Science, 290 (2000), pp. 2319.CrossRefGoogle ScholarPubMed
[3]Węel, Gabriel, Ostrowski, Ziemowit and Białecki, Ryszard A., A novel approach of evaluating absorption line black body distribution function employing proper orthogonal decomposition, J. Quantitative Spectroscopy Radiative Transfer, 111 (2010), pp. 309317.Google Scholar
[4]Liberge, E. and Hamdouni, A., Reduced order modelling method via proper orthogonal decomposition (POD) for flow around an oscillating cylinder, J. Fluids Structures, 26 (2010), pp. 778Google Scholar
[5]Khalil, Mohammadd, Adhikari, Sondipon and Sarkar, Abhijit, Linear system identification using proper orthogonal decomposition, Mechanical Systems and Signal Processing, 21 (2007), pp. 31233145.Google Scholar
[6]Atwell, J. A. and King, B. B., Proper orthogonal decomposition for reduced basis feedback controllers for parabolic equations, Math. Comput. Model., 33 (2001), pp. 119.Google Scholar
[7]Bourguet, Rémi, Braza, Marianna and Dervieux, Alain, Reduced-order modeling of transonic flows around an airfoil submitted to small deformations, J. Comput. Phys., 230 (2011), pp. 159184.CrossRefGoogle Scholar
[8]Rapún, María-Luisa and Vega, José M., Reduced order models based on local POD plus Galerkin projection, J. Comput. Phys., 229 (2010), pp. 30463063.CrossRefGoogle Scholar
[9]Yvonnet, J., Zahrouni, H. and Potier-Ferry, M., A model reduction method for the post-buckling analysis of cellular microstructures, Comput. Methods Appl. Mech. Eng., 197 (2007), pp. 265280.Google Scholar
[10]Hay, A., Borggaard, J., Akhtar, I. and Pelletier, D., Reduced-order models for parameter dependent geometries based on shape sensitivity analysis, J. Comput. Phys., 229 (2010), pp. 13271352.Google Scholar
[11]Krasnyk, Mykhaylo, Mangold, Michael and Kienle, Achim, Reduction procedure for parametrized fluid dynamics problems based on proper orthogonal decomposition and calibration, Chemical Eng. Sci., 65 (2010), pp. 62386246.CrossRefGoogle Scholar
[12]Kalashnikova, Irina, Bloemen Waanders, Bart Van, Arunajatesan, Srinivasan and Barone, Matthew, Stabilization of projection-based reduced order models for linear time-invariant systems via optimization-based eigenvalue reassignment, Comput. Methods Appl. Mech. Eng., 272 (2014), pp. 251270.CrossRefGoogle Scholar
[13]Luo, Zhendong, Yang, Xiaozhong and Zhou, Yanjie, A reduced finite difference scheme based on singular value decomposition and proper orthogonal decomposition for Burgers equation, J. Comput. Appli. Math., 229 (2009), pp. 97107.Google Scholar
[14]Alonso, D., Velazquez, A. and Vega, J. M., A method to generate computationally efficient reduced order models, Comput. Methods Appl. Mech. Eng., 198 (2009), pp. 26832691.Google Scholar
[15]Wang, Xiao-Long and Jiang, Yao-Lin, Model order reduciton methods for coupled systems in the time domain using Laguerre polynomials, Comput. Math. Appl., 62 (2011), pp. 32413250.Google Scholar
[16]Ammar, A., Mokdad, B., Chinesta, F. and Keunings, R., A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids, J. Non-Newtonian Fluid Mech., 139 (2006), pp. 153176.Google Scholar
[17]Ammar, A., Mokdad, B., Chinesta, F. and Keunings, R., A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modelling of complex fluids Part II: Transient simulation using space-time separated representations, J. Non-Newtonian Fluid Mech., 144 (2007), pp. 98121.Google Scholar
[18]González, David, Ammar, Amine, Chinesta, Francisco and Cueto, Elías, Recent advances on the use of separated representations, Int. J. Numer. Methods Eng., 81 (2010), pp. 637659.Google Scholar
[19]Jie Wu, Chui, Large optimal truncated low-dimensional dynamical systems, Discrete and Continuous Dynamical Systems, 2 (1996), pp. 559583.Google Scholar
[20]Heyberger, Ch., Boucard, P.-A. and Néron, D., A rational strategy for the resolution of parametrized problems in the PGD framework, Comput. Methods Appl. Mech. Eng., 259 (2013), pp. 4049.Google Scholar
[21]González, D., Masson, F., Poulhaon, F., Leygue, A., Cueto, E. and Chinesta, F., Proper generalized decomposition based dynamic data driven inverse identification, Math. Comput. Simulation, 82 (2012), pp. 16771695.Google Scholar
[22]Dumon, A., Allery, C. and Ammar, A., Proper general decomposition (PGD) for the resolution of Navier-Stokes equations, J. Comput. Phys., 230 (2011), pp. 13871407.Google Scholar
[23]Chinesta, F., Ammar, A., Leygue, A. and Keunings, R., An overview of the proper generalized decomposition with applications in computational rheology, J. Non-Newtonian Fluid Mech., 166 (2011), pp. 578592.Google Scholar