Advanced Search
Article Contents
Article Contents

Efficient numerical methods for gas network modeling and simulation

  • * Corresponding author: Yue Qiu

    * Corresponding author: Yue Qiu 
This work is partially funded by the European Regional Development Fund (ERDF/EFRE: ZS/2016/04/78156) within the Center Dynamic Systems (CDS)
Abstract Full Text(HTML) Figure(17) / Table(4) Related Papers Cited by
  • We study the modeling and simulation of gas pipeline networks, with a focus on fast numerical methods for the simulation of transient dynamics. The obtained mathematical model of the underlying network is represented by a system of nonlinear differential algebraic equations (DAEs). With our modeling approach, we reduce the number of algebraic constraints, which correspond to the $ (2,2) $ block in our semi-explicit DAE model, to the order of junction nodes in the network, where a junction node couples at least three pipelines. We can furthermore ensure that the $ (1, 1) $ block of all system matrices including the Jacobian is block lower triangular by using a specific ordering of the pipes of the network. We then exploit this structure to propose an efficient preconditioner for the fast simulation of the network. We test our numerical methods on benchmark problems of (well-)known gas networks and the numerical results show the efficiency of our methods.

    Mathematics Subject Classification: Primary: 65F08, 37M05, 37N30, 94C15.


    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  Separation of control volume $ C_i $

    Figure 2.  A typical gas network

    Figure 3.  Smoothed network of Figure 2 with an ordering of the pipes

    Figure 7.  Computational diagram for gas network simulation

    Figure 4.  An illustrative network example of a DAG

    Figure 5.  Big benchmark network in [34]

    Figure 6.  Sparsity pattern of $ J $ without and with DF ordering

    Figure 8.  Pipeline network in [16]

    Figure 9.  Comparison of FVM and FDM for a single pipe network

    Figure 10.  Medium size network

    Figure 11.  Comparison of FVM and FDM for a medium network

    Figure 12.  Mass flow at supply nodes for case 1

    Figure 13.  Mass flow at supply nodes for case 2

    Figure 14.  Mass flow for the pipe $ 31\rightarrow 37 $

    Figure 15.  Nonlinear residual at the first and tenth time step

    Figure 16.  Number of IDR($ 4 $) iterations at the first time step

    Figure 17.  Number of IDR($ 4 $) iterations at the $ 10 $-th time step

    Algorithm 1: Newton's method to solve (16)
    1: Input: maximal number of Newton steps $ n_{\max} $, stop tolerance $ \varepsilon_0 $, initial guess $ x_0 $
    2: $ m=0 $
    3: while $ m\leq n_{\max}\& \ \|F(x)\|\geq \varepsilon_0 $ do
    4:     Compute the Jacobian matrix $ D_F(x_m)=\frac{\partial}{\partial x}F|_{x=x_m} $
    5:     Solve $ F(x_m) + D_F(x_m)(x-x_m)=0 $
    6:     $ m\gets m+1 $, $ x_m\gets x $
    7: end while
    8: Output: solution $ x\approx x_m $
     | Show Table
    DownLoad: CSV

    Table 1.  Computational time (seconds) for Schur complement $ S^1 $

    $ h $ $ \# D_F $ with DF without DF
    20 2.01e+05 $ 8.12 $ 8.75
    10 3.97e+05 $ 17.84 $ 19.14
    5 7.91e+05 $ 38.44 $ 41.75
    2.5 1.58e+06 $ 81.42 $ 87.77
     | Show Table
    DownLoad: CSV

    Table 2.  Condition number of the Jacobian matrix $ D_{F} $ from FVM and FDM, 1st time step, $ h = 60 $

    Newton iter. 1 2 3 4
    FVM 1.56e+07 1.57e+07 1.57e+07 1.57e+07
    FDM 1.24e+08 1.25e+08 1.25e+08 1.25e+08
     | Show Table
    DownLoad: CSV

    Table 3.  Computational time for the 1st Newton iteration

    $ h $ $ \# D_F $ $ t_{S^1} $ IDR($ 4 $) backslash
    40 1.03e+05 $ 3.85 $ 0.25 0.13
    20 2.01e+05 $ 8.12 $ 0.52 0.36
    10 3.97e+05 $ 17.84 $ 1.06 1.18
    5 7.91e+05 $ 38.44 $ 2.13 1054.62
    2.5 1.58e+06 $ 81.42 $ 4.34 -
     | Show Table
    DownLoad: CSV
  • [1] N. Banagaaya, S. Grundel and P. Benner, Index-aware MOR for gas transport networks with many supply inputs, in IUTAM Symposium on Model Order Reduction of Coupled Systems (eds. J. Fehr and B. Haasdonk), Springer International Publishing, Cham, 2020,191–207.
    [2] J. Bang-Jensen and G. Z. Gutin, Digraphs: Theory, Algorithms and Applications, Springer-Verlag, London, 2008. doi: 10.1007/978-1-84800-998-1.
    [3] P. Benner, S. Grundel, C. Himpe, C. Huck, T. Streubel and C. Tischendorf, Gas network benchmark models, in Differential-Algebraic Equations Forum, Springer, Berlin, Heidelberg, 2018.
    [4] M. BenziG. H. Golub and J. Liesen, Numerical solution of saddle point problems, Acta Numer., 14 (2005), 1-137.  doi: 10.1017/S0962492904000212.
    [5] A. BermúdezX. López and M. E. Vázquez-Cendón, Finite volume methods for multi-component Euler equations with source terms, Comput. Fluids, 156 (2017), 113-134.  doi: 10.1016/j.compfluid.2017.07.004.
    [6] M. Chaczykowski, Sensitivity of pipeline gas flow model to the selection of the equation of state, Chem. Eng. Res. Des., 87 (2009), 1596-1603.  doi: 10.1016/j.cherd.2009.06.008.
    [7] R. DemboS. Eisenstat and T. Steihaug, Inexact Newton methods, SIAM J. Numer. Anal., 19 (1982), 400-408.  doi: 10.1137/0719025.
    [8] H. Egger, A robust conservative mixed finite element method for isentropic compressible flow on pipe networks, SIAM J. Sci. Comput., 40 (2018), A108–A129. doi: 10.1137/16M1094373.
    [9] H. Egger, T. Kugler and N. Strogies, Parameter identification in a semilinear hyperbolic system, Inverse Probl., 33 (2017), 055022. doi: 10.1088/1361-6420/aa648c.
    [10] H. ElmanD. Silvester and  A. WathenFinite Elements and Fast Iterative Solvers, Oxford University Press, New York, 2014.  doi: 10.1093/acprof:oso/9780199678792.001.0001.
    [11] A. Fügenschuh, et al., Physical and technical fundamentals of gas networks, in Evaluating Gas Network Capacities
    [12] T. G. GrandónH. Heitsch and R. Henrion, A joint model of probabilistic/robust constraints for gas transport management in stationary networks, Comput. Manag. Sci., 14 (2017), 443-460.  doi: 10.1007/s10287-017-0284-7.
    [13] S. Grundel, N. Hornung, B. Klaassen, P. Benner and T. Clees, Computing surrogates for gas network simulation using model order reduction, in Surrogate-Based Modeling and Optimization doi: 10.1007/978-1-4614-7551-4_9.
    [14] S. Grundel, N. Hornung and S. Roggendorf, Numerical aspects of model order reduction for gas transportation networks, in Simulation-Driven Modeling and Optimization, Springer Proceedings in Mathematics & Statistics, 153, 2016, 1–28.
    [15] S. Grundel and L. Jansen, Efficient simulation of transient gas networks using IMEX integration schemes and MOR methods, in 2015 54th IEEE Conference on Decision and Control (CDC), 2015, 4579–4584. doi: 10.1109/CDC.2015.7402934.
    [16] S. Grundel, L. Jansen, N. Hornung, T. Clees, C. Tischendorf and P. Benner, Model order reduction of differential algebraic equations arising from the simulation of gas transport networks, in Progress in Differential-Algebraic Equations, Differential-Algebraic Equations Forum, Springer Berlin Heidelberg, 2014,183–205. doi: 10.1007/978-3-642-34928-7_2.
    [17] M. GugatF. M. HanteM. Hirsch-Dick and G. Leugering, Stationary states in gas networks, Netw. Heterog. Media, 10 (2015), 295-320.  doi: 10.3934/nhm.2015.10.295.
    [18] F. M. Hante, G. Leugering, A. Martin, L. Schewe and M. Schmidt, Challenges in Optimal Control Problems for Gas and Fluid Flow in Networks of Pipes and Canals: From Modeling to Industrial Applications, Springer Verlag, Singapore, 2017, 77–122.
    [19] A. Herrán-GonzálezJ. M. D. L. CruzB. D. Andrés-Toro and J. L. Risco-Martín, Modeling and simulation of a gas distribution pipeline network, Appl. Math. Model., 33 (2009), 1584-1600. 
    [20] M. Herty, Modeling, simulation and optimization of gas networks with compressors, Netw. Heterog. Media, 2 (2007), 81-97.  doi: 10.3934/nhm.2007.2.81.
    [21] M. Herty, Coupling conditions for networked systems of Euler equations, SIAM J. Sci. Comput., 30 (2008), 1596-1612.  doi: 10.1137/070688535.
    [22] M. HertyJ. Mohring and V. Sachers, A new model for gas flow in pipe networks, Math. Methods Appl. Sci., 33 (2010), 845-855.  doi: 10.1002/mma.1197.
    [23] C. HuckL. Jansen and C. Tischendorf, A topology based discretization of PDAEs describing water transportation networks, Proc. Appl. Math. Mech., 14 (2014), 923-924.  doi: 10.1002/pamm.201410442.
    [24] C. Johnson and J. Pitkäranta, An analysis of the discontinuous Galerkin method for a scalar hyperbolic equation, Math. Comp., 46 (1986), 1-26.  doi: 10.1090/S0025-5718-1986-0815828-4.
    [25] C. Kelley, Solving Nonlinear Equations with Newton's Method, Society for Industrial and Applied Mathematics, Philadelphia, 2003. doi: 10.1137/1.9780898718898.
    [26] A. Osiadacz, Simulation of transient gas flows in networks, Internat. J. Numer. Methods Fluids, 4 (1984), 13-24.  doi: 10.1002/fld.1650040103.
    [27] A. Osiadacz, Simulation and Analysis of Gas Networks, Gulf Publishing, Houston, TX, 1987.
    [28] A. J. Osiadacz and M. Yedroudj, A comparison of a finite element method and a finite difference method for transient simulation of a gas pipeline, Appl. Math. Model., 13 (1989), 79-85.  doi: 10.1016/0307-904X(89)90018-8.
    [29] J. W. Pearson, On the development of parameter-robust preconditioners and commutator arguments for solving Stokes control problems, Electron. Trans. Numer. Anal., 44 (2015), 53-72. 
    [30] J. Pestana and A. J. Wathen, Natural preconditioning and iterative methods for saddle point systems, SIAM Rev., 57 (2015), 71-91.  doi: 10.1137/130934921.
    [31] M. Porcelli, V. Simoncini and M. Tani, Preconditioning of active-set Newton methods for PDE-constrained optimal control problems, SIAM J. Sci. Comput., 37 (2015), S472–S502. doi: 10.1137/140975711.
    [32] Y. Qiu, Preconditioning Optimal Flow Control Problems Using Multilevel Sequentially Semiseparable Matrix Computations, Ph.D thesis, Delft Institute of Applied Mathematics, Delft University of Technology, 2015.
    [33] T. Rees, Preconditioning Iterative Methods for PDE-Constrained Optimization, Ph.D thesis, University of Oxford, 2010.
    [34] S. Roggendorf, Model Order Reduction for Linearized Systems Arising from the Simulation of Gas Transportation Networks, Master's thesis, Rheinischen Friedrich-Wilhelms-Universität Bonn, Germany, 2015.
    [35] Y. Saad, Iterative Methods for Sparse Linear Systems, Society for Industrial and Applied Mathematics, Philadelphia, 2003. doi: 10.1137/1.9780898718003.
    [36] P. Sonneveld and M. B. van Gijzen, IDR(s): A family of simple and fast algorithms for solving large nonsymmetric systems of linear equations, SIAM J. Sci. Comput., 31 (2008), 1035-1062.  doi: 10.1137/070685804.
    [37] M. C. Steinbach, On PDE solution in transient optimization of gas networks, J. Comput. Appl. Math., 203 (2007), 345-361.  doi: 10.1016/j.cam.2006.04.018.
    [38] M. Stoll and T. Breiten, A low-rank in time approach to PDE-constrained optimization, SIAM J. Sci. Comput., 37 (2015), B1–B29. doi: 10.1137/130926365.
    [39] W. Q. Tao and H. C. Ti, Transient analysis of gas pipeline network, Chem. Eng. J., 69 (1998), 47-52.  doi: 10.1016/S1385-8947(97)00109-5.
    [40] E. F. Toro and S. J. Billett, Centred TVD schemes for hyperbolic conservation laws, IMA J. Numer. Anal., 20 (2000), 47-79.  doi: 10.1093/imanum/20.1.47.
    [41] M. B. van Gijzen and P. Sonneveld, Algorithm 913: An elegant IDR(s) variant that efficiently exploits biorthogonality properties, ACM Trans. Math. Software, 38 (2011), 5: 1–5: 19. doi: 10.1145/2049662.2049667.
    [42] A. J. Wathen, Preconditioning, Acta Numer., 24 (2015), 329-376.  doi: 10.1017/S0962492915000021.
    [43] M. Wathen, C. Greif and D. Schötzau, Preconditioners for mixed finite element discretizations of incompressible MHD equations, SIAM J. Sci. Comput., 39 (2017), A2993–A3013. doi: 10.1137/16M1098991.
    [44] J. Zhou and M. A. Adewumi, Simulation of transients in natural gas pipelines using hybrid TVD schemes, Internat. J. Numer. Methods Fluids, 32 (2000), 407-437.  doi: 10.1002/(SICI)1097-0363(20000229)32:4<407::AID-FLD945>3.0.CO;2-9.
    [45] A. Zlotnik, M. Chertkov and S. Backhaus, Optimal control of transient flow in natural gas networks, in 2015 54th IEEE Conference on Decision and Control (CDC), 2015, 4563–4570. doi: 10.1109/TCNS.2014.2367360.
  • 加载中




Article Metrics

HTML views(619) PDF downloads(709) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint