IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2660-d661376.html
   My bibliography  Save this article

Hierarchical Structural Analysis Method for Complex Equation-Oriented Models

Author

Listed:
  • Chao Wang

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Li Wan

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Tifan Xiong

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yuanlong Xie

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Guangdong Intelligent Robotics Institute, Dongguan 523808, China)

  • Shuting Wang

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Guangdong Intelligent Robotics Institute, Dongguan 523808, China)

  • Jianwan Ding

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Liping Chen

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Structural analysis is a method for verifying equation-oriented models in the design of industrial systems. Existing structural analysis methods need flattening of the hierarchical models into an equation system for analysis. However, the large-scale equations in complex models make structural analysis difficult. Aimed to address the issue, this study proposes a hierarchical structural analysis method by exploring the relationship between the singularities of the hierarchical equation-oriented model and its components. This method obtains the singularity of a hierarchical equation-oriented model by analyzing a dummy model constructed with the parts from the decomposing results of its components. Based on this, the structural singularity of a complex model can be obtained by layer-by-layer analysis according to their natural hierarchy. The hierarchical structural analysis method can reduce the equation scale in each analysis and achieve efficient structural analysis of very complex models. This method can be adaptively applied to nonlinear-algebraic and differential-algebraic equation models. The main algorithms, application cases and comparison with the existing methods are present in this paper. The complexity analysis results show the enhanced efficiency of the proposed method in the structural analysis of complex equation-oriented models. Compared with the existing methods, the time complexity of the proposed method is improved significantly.

Suggested Citation

  • Chao Wang & Li Wan & Tifan Xiong & Yuanlong Xie & Shuting Wang & Jianwan Ding & Liping Chen, 2021. "Hierarchical Structural Analysis Method for Complex Equation-Oriented Models," Mathematics, MDPI, vol. 9(21), pages 1-26, October.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2660-:d:661376
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2660/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/21/2660/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mattsson, Sven Erik, 1995. "Simulation of object-oriented continuous time models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(5), pages 513-518.
    2. Cinzia Bernardeschi & Pierpaolo Dini & Andrea Domenici & Maurizio Palmieri & Sergio Saponara, 2020. "Formal Verification and Co-Simulation in the Design of a Synchronous Motor Control Algorithm," Energies, MDPI, vol. 13(16), pages 1-23, August.
    3. H. W. Kuhn, 2005. "The Hungarian method for the assignment problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 7-21, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. İsmail Güzel & Atabey Kaygun, 2022. "A new non-archimedean metric on persistent homology," Computational Statistics, Springer, vol. 37(4), pages 1963-1983, September.
    2. Carpanzano, Emanuele & Maffezzoni, Claudio, 1998. "Symbolic manipulation techniques for model simplification in object-oriented modelling of large scale continuous systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(2), pages 133-150.
    3. Zhuxi Chen & Xiaoping Li & Jatinder N.D. Gupta, 2016. "Sequencing the storages and retrievals for flow-rack automated storage and retrieval systems with duration-of-stay storage policy," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 984-998, February.
    4. Ze Jiang & Xiaoyan Huang & Wenping Cao, 2022. "RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions," Energies, MDPI, vol. 15(10), pages 1-17, May.
    5. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef & Qian, Yaguan & Allaoui, Hamid, 2023. "Modelling and analysis of online ride-sharing platforms – A sustainability perspective," European Journal of Operational Research, Elsevier, vol. 304(2), pages 577-595.
    6. Pierpaolo Dini & Sergio Saponara, 2022. "Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems," Energies, MDPI, vol. 15(23), pages 1-29, November.
    7. Wenyi Li & Yalin Wang & Yi Ding & Yi Yin, 2022. "Optimization Design of Packaging Insulation for Half-Bridge SiC MOSFET Power Module Based on Multi-Physics Simulation," Energies, MDPI, vol. 15(13), pages 1-19, July.
    8. Rubing Chen & Jinjiang Yuan & C.T. Ng & T.C.E. Cheng, 2019. "Single‐machine scheduling with deadlines to minimize the total weighted late work," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(7), pages 582-595, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2660-:d:661376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.