IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i16p3524-d1217646.html
   My bibliography  Save this article

Stress Analysis of the Radius and Ulna in Tennis at Different Flexion Angles of the Elbow

Author

Listed:
  • Yan Chen

    (Department of Physical Education, Northwest A&F University, Xi’an 712100, China)

  • Qiang Du

    (School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China)

  • Xiyang Yin

    (School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China)

  • Renjie Fu

    (School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China)

  • Yiyun Zhu

    (School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China)

Abstract

In this paper, based on the finite element method, the stresses of the radius and ulna are analyzed at different flexion angles of the elbow when playing tennis. The finite element model is presented for the elbow position with flexion angles of 0°, 25°, 60°, and 80° according to the normal human arm bone. In this model, the whole arm with metacarpals, radius, ulna, humerus and scapula is considered. The calculation is simplified by setting the scapula and metacarpals as rigid bodies and using Tie binding constraints between the humerus and the radius and ulna. This model is discretized using the 10-node second-order tetrahedral element (C3D10). This model contains 109,765 nodes and 68,075 elements. The hitting forces applied to the metacarpal bone are 100 N and 300 N, respectively. The numerical results show that the highest principal stresses are at the points of 1/4 of the radius, the elbow joint, and the points of 1/10 of the ulna. The results of the maximum principal stress show that the external pressures are more pronounced as the elbow flexion angle increases and that the magnitude of the hitting force does not affect the principal stress distribution pattern. Elbow injuries to the radius can be reduced by using a stroke with less elbow flexion, and it is advisable to wear a reinforced arm cuff on the dorsal 1/4 of the hand, a radial/dorsal hand wrist, and an elbow guard to prevent radial ulnar injuries.

Suggested Citation

  • Yan Chen & Qiang Du & Xiyang Yin & Renjie Fu & Yiyun Zhu, 2023. "Stress Analysis of the Radius and Ulna in Tennis at Different Flexion Angles of the Elbow," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3524-:d:1217646
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/16/3524/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/16/3524/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing Cheng & Xiaowei Luo, 2022. "Analyzing the Land Leasing Behavior of the Government of Beijing, China, via the Multinomial Logit Model," Land, MDPI, vol. 11(3), pages 1-14, March.
    2. Frantisek Vaverka & Jiri Nykodym & Jan Hendl & Jiri Zhanel & David Zahradnik, 2018. "Association between serve speed and court surface in tennis," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(2), pages 262-272, March.
    3. Afxentios Kekelekis & Pantelis Theodoros Nikolaidis & Isabel Sarah Moore & Thomas Rosemann & Beat Knechtle, 2020. "Risk Factors for Upper Limb Injury in Tennis Players: A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-18, April.
    4. Cheng, Jing, 2022. "Analysis of the factors influencing industrial land leasing in Beijing of China based on the district-level data," Land Use Policy, Elsevier, vol. 122(C).
    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. Huijun Tang & Jufeng Wang & Le Wang, 2023. "Mining Significant Utility Discriminative Patterns in Quantitative Databases," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    2. Pei Yin & Miaojuan Peng, 2023. "Station Layout Optimization and Route Selection of Urban Rail Transit Planning: A Case Study of Shanghai Pudong International Airport," Mathematics, MDPI, vol. 11(6), pages 1-29, March.
    3. Yue Zhao & Xuelian Guo & Botong Su & Yamin Sun & Yiyun Zhu, 2023. "Multi-Lane Traffic Load Clustering Model for Long-Span Bridge Based on Parameter Correlation," Mathematics, MDPI, vol. 11(2), pages 1-19, January.
    4. Heng Cheng & Zebin Xing & Yan Liu, 2023. "The Improved Element-Free Galerkin Method for 3D Steady Convection-Diffusion-Reaction Problems with Variable Coefficients," Mathematics, MDPI, vol. 11(3), pages 1-19, February.
    5. Sun, Fengxin & Wang, Jufeng & Xu, Ying, 2024. "An improved stabilized element-free Galerkin method for solving steady Stokes flow problems," Applied Mathematics and Computation, Elsevier, vol. 463(C).
    6. Yumin Cheng, 2022. "Preface to the Special Issue on “Numerical Computation, Data Analysis and Software in Mathematics and Engineering”," Mathematics, MDPI, vol. 10(13), pages 1-5, June.
    7. Pei Yin & Jing Cheng & Miaojuan Peng, 2022. "Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    8. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    9. Zebin Xing & Heng Cheng & Jing Cheng, 2023. "Deep Learning Method Based on Physics-Informed Neural Network for 3D Anisotropic Steady-State Heat Conduction Problems," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
    10. Gao, Feng & Yi, Shiyi & Li, Xiaonuo & Chen, Weiping, 2024. "Ensemble intelligence algorithms and soil environmental quality to model economic quantity of land resource allocation and spatial inequality," Land Use Policy, Elsevier, vol. 141(C).

    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:11:y:2023:i:16:p:3524-:d:1217646. 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.