A modified and efficient phase field model for the biological transport network

被引:13
作者
Xia, Qing [1 ]
Jiang, Xiaoyu [1 ]
Li, Yibao [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China
基金
中国国家自然科学基金;
关键词
Energy-dissipation-rate preserving; Biological transport networks; Gradient flow; Adaption; Unconditional energy stability; PDE SYSTEM; PRINCIPLE;
D O I
10.1016/j.jcp.2023.112192
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper aims to establish the biological transport network based on the phase field model. In order to ensure that the topological shape is formed under the guidance of electrical conductivity, we generate the biological networks with sufficient information based on the distributions of the venation of the leaf represented by the reaction-diffusion model. We modify the original energy of the network generating model by considering the auxin gradient property. By applying the gradient flow method to minimize the modified energy, we derive the Poisson type equation for pressure, the reaction-diffusion type equation for the network conductance, and the Allen-Cahn type equation for the phase field. The proposed model is significant on the investigation of phase transitions by considering the gradient properties on the boundaries. We have innovatively added conductivity and phase-field coupling terms that inhibit perpendicular transport of nutrients, making it easy to generate thin branches from the trunk through this model. In order to obtain the second-order temporal accuracy, we take the Crank-Nicolson method for the governing system. To obtain the second-order spatial accuracy, we discretize the coupling system with the central finite difference method and linearize the nonlinear terms semi-explicitly to form a linear system at each time step. The discrete energy dissipation is provably preserved and we can use a larger time step. We apply the preconditioned conjugate gradient method with the multigrid method as a preconditioner to implement a practical algorithm with only linear algebraic complexity. The proposed algorithm is easy to implement and achieves a fast convergence. Various numerical tests are demonstrated to verify the efficiency, stability, and robustness of the proposed method.(c) 2023 Elsevier Inc. All rights reserved.
引用
收藏
页数:19
相关论文
共 59 条
  • [41] FLUCTUATIONS AND IRREVERSIBLE PROCESSES
    ONSAGER, L
    MACHLUP, S
    [J]. PHYSICAL REVIEW, 1953, 91 (06): : 1505 - 1512
  • [42] Fronthaul Compression for Cloud Radio Access Networks [Signal processing advances inspired by network information theory]
    Park, Seok-Hwan
    Simeone, Osvaldo
    Sahin, Onur
    Shamai , Shlomo
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (06) : 69 - 79
  • [43] Optimal Noise-Canceling Networks
    Ronellenfitsch, Henrik
    Dunkel, Joern
    Wilczek, Michael
    [J]. PHYSICAL REVIEW LETTERS, 2018, 121 (20)
  • [44] Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks
    Ronellenfitsch, Henrik
    Katifori, Eleni
    [J]. PHYSICAL REVIEW LETTERS, 2016, 117 (13)
  • [45] Modeling and visualization of leaf venation patterns
    Runions, A
    Fuhrer, M
    Lane, B
    Federl, P
    Rolland-Lagan, AG
    Prusinkiewicz, P
    [J]. ACM TRANSACTIONS ON GRAPHICS, 2005, 24 (03): : 702 - 711
  • [46] Log-periodic oscillations due to discrete effects in complex networks
    Sienkiewicz, Julian
    Fronczak, Piotr
    Holyst, Janusz A.
    [J]. PHYSICAL REVIEW E, 2007, 75 (06):
  • [47] Rules for Biologically Inspired Adaptive Network Design
    Tero, Atsushi
    Takagi, Seiji
    Saigusa, Tetsu
    Ito, Kentaro
    Bebber, Dan P.
    Fricker, Mark D.
    Yumiki, Kenji
    Kobayashi, Ryo
    Nakagaki, Toshiyuki
    [J]. SCIENCE, 2010, 327 (5964) : 439 - 442
  • [48] Trottenberg U., 2000, MULTIGRID
  • [49] Centralities for networks with consumable resources
    Ushijima-Mwesigwa, Hayato
    Khan, Zadid
    Chowdhury, Mashrur A.
    Safro, Ilya
    [J]. NETWORK SCIENCE, 2019, 7 (03) : 376 - 401
  • [50] Fast Image Restoration Method Based on the L0, L1, and L2 Gradient Minimization
    Wang, Jin
    Xia, Qing
    Xia, Binhu
    [J]. MATHEMATICS, 2022, 10 (17)