Analysis of Field Distribution in Optical Fibre Using FEM Method

Year : 2025 | Volume : 15 | Issue : 02 | Page : 31 40
    By

    Nikat Rajak Mulla,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Student, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

The application of the linear Finite Element Method (FEM) to the analysis of the field distribution in optical fibres is examined in this work. Characterizing fibre characteristics like mode profiles, propagation constants, and dispersion, all of which are critical for maximizing fibre performance in a variety of applications like sensing and telecommunications, requires an understanding of the distribution of electric and magnetic fields. In order to accurately determine the modal fields in optical fibres with various cross-sectional geometries and refractive index profiles, this study focusses on using a linear FEM formulation to effectively solve the governing Helmholtz equation. The method’s accuracy and computing efficiency are assessed, demonstrating its promise for complicated fibre design analysis and simulation. This method contributes to better fibre performance and design by offering insightful information about how light propagates across optical fibres. An analytical simplification of Maxwell’s equations allows the development of the scalar wave equation. By adjusting the core radius, different field and contour distributions can be obtained for modes like LP 01, LP 21, and LP 31. The finite element technique (FEM) can be used to analyse the modal properties of an optical cable. The conventional eigenvalue equation is obtained from Maxwell’s equation following FEM analysis. The scalar wave equation can be approximated using the finite element method (FEM). Increasing the number of elements and moving from the linear to the quadratic finite element approach will result in a higher level of accuracy. The finite element method (FEM) can be used to thoroughly analyse any waveguide problem.

Keywords: Field distribution, optical fibre, finite element method, eigen value, eigen vector, Finite Element Method (FEM)

[This article belongs to Trends in Opto-electro & Optical Communication ]

How to cite this article:
Nikat Rajak Mulla, Kazi Kutubuddin Sayyad Liyakat. Analysis of Field Distribution in Optical Fibre Using FEM Method. Trends in Opto-electro & Optical Communication. 2025; 15(02):31-40.
How to cite this URL:
Nikat Rajak Mulla, Kazi Kutubuddin Sayyad Liyakat. Analysis of Field Distribution in Optical Fibre Using FEM Method. Trends in Opto-electro & Optical Communication. 2025; 15(02):31-40. Available from: https://journals.stmjournals.com/toeoc/article=2025/view=215300


References

  1.  Liyakat KK. Analysis for field distribution in optical waveguide using linear Fem method. Journal of Optical communication Electronics. 2023; 9(1): 23–8.
  2.  Mulani AO, Patil RM, Liyakat KK. A discriminatory appearance model for robust online multi- target tracking. Télématique. 2023;22:24–43.
  3. Liyakat KS, Liyakat KK. Dispersion Compensation in Optical Fiber: A Review. Journal of Telecommunication Study (JTS). 2023; 8(3): 14–9.
  4. Liyakat SS. Quantum Key Distribution in Optical Fiber Communication: A Study. Trends in Opto- electro & Optical Communication (TOEOC). 2025; 15(1): 30–40.
  5.   Sudake SS, Khadake SB, Khedekar SV, Kawade AM, Vyavahare SS. Solar Based Wireless Electric Vehicle Charging System. Int J Adv Res Sci Commun Technol. 2025 May; 5(5): 325–348.
  6. Parihar B, Kiran A, Valaboju S, Rashid SZ, Liyakat KK, DR AS. Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques. In: ITM Web of Conferences. EDP Sciences. 2025; 76: 02010.
  7. Liyakat KK, Khadake SB, Tamboli DA, Sawant VA, HM M, Sathe S. AI-Driven-IoT (AIIoT) Based Decision-Making-KSK Approach in Drones for Climate Change Study. In 2024 IEEE 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). 2024 Dec 12; 1735–1744.
  8.  Liyakat KK. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. In 2023 IEEE International Conference on Emerging Smart Computing and Informatics (ESCI). 2023 Mar 1; 1–5.
  9.  Kazi Kutubuddin Sayyad Liyakat. Malicious node detection in IoT networks using artificial neural networks: A machine learning approach. In: Singh VK, Sagar AK, Nand P, Astya R, Kaiwartya O, editors. Intelligent Networks: Techniques, and Applications. Boca Raton: CRC Press; 2024. p. 182–97.
  10.  Kasat K, Shaikh N, Rayabharapu VK, Nayak M, Liyakat KK. Implementation and recognition of waste management system with mobility solution in smart cities using Internet of Things. In 2023 IEEE Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). 2023 Aug 23; 1661–1665.
  11. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for E-mobility ecosystems. In: Lakshmi D, Nagpal N, Kassarwani N, Varthanan GV, Siano P, editors. E-Mobility in Electrical Energy Systems for Sustainability. Pennsylvania: IGI Global Scientific Publishing; 2024. p. 295–320. DOI: 10.4018/979-8-3693-2611-4.ch014.
  12.  Kazi KSL. Braille-Lippi numbers and characters detection and announcement system for blind children using KSK approach: AI-driven decision-making approach. In: Murugan T, KP, Abirami AM, editors. Driving Quality Education Through AI and Data Science. Pennsylvania (USA): IGI Global Scientific Publishing; 2025. p. 531–56. DOI: 10.4018/979-8-3693-8292-9.ch023.
  13.  Kazi KSL. Advancing towards sustainable energy with hydrogen solutions: Adaptation and challenges. In: Özsungur F, Chaychi Semsari M, Küçük Bayraktar H, editors. Geopolitical Landscapes of Renewable Energy and Urban Growth. Pennsylvania (USA): IGI Global Scientific Publishing; 2025. p. 357–94. DOI: 10.4018/979-8-3693-8814-3.ch013.
  14. Kazi KSL. A study on AI-driven Internet of battlefield things (IoBT)-based decision making: KSK approach in IoBT. In: Tariq MU, editor. Merging Artificial Intelligence with the Internet of Things. Pennsylvania (USA): IGI Global Scientific Publishing; 2025. p. 203–38. DOI: 10.4018/979-8- 3693-8547-0.ch007.
  15. Benson K. Enabling resilience in the Internet of Things. 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), St. Louis, MO, USA. 2015. p. 230–2. DOI: 10.1109/PERCOMW.2015.7134032.
  16.  Kazi KSL. Machine learning-based pomegranate disease detection and treatment. In: Zia Ul Haq M, Ali I, editors. Revolutionizing Pest Management for Sustainable Agriculture. Pennsylvania (USA): IGI Global; 2024. p. 469–98. DOI: 10.4018/979-8-3693-3061-6.ch019.
  17.  Kazi KSL. Computer-aided diagnosis in ophthalmology: a technical review of deep learning applications. In: Garcia MB, Almeida RPP, editors. Transformative Approaches to Patient Literacy and Healthcare Innovation. Pennsylvania: IGI Global Scientific Publishing; 2024. p. 112–35. DOI: 10.4018/979-8-3693-3661-8.ch006.
  18. Kazi KSL. Machine learning (ML)-based Braille Lippi characters and numbers detection and announcement system for blind children in learning. In: Sart G, editor. Social Reflections of Human-Computer Interaction in Education, Management, and Economics. Pennsylvania (USA): IGI Global; 2024. p. 16–39. DOI: 10.4018/979-8-3693-3033-3.ch002.
  19.  Kazi KS, Sayyad L. ChatGPT: an automated teacher’s guide to learning. In: Bansal R, editor. AI Algorithms and ChatGPT for Student Engagement in Online Learning. Pennsylvania: IGI Global Scientific Publishing; 2024. p. 1–
  20. DOI: 10.4018/979-8-3693-4268-8.ch001. 20. Liyakat KK. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: International Conference on Machine Learning, IoT and Big Data. Singapore: Springer Nature Singapore; 2023 Mar 10; 123–134.
  21.  Kazi KS. KK Approach to Increase Resilience in Internet of Things: A T-Cell Security Concept. In: Analyzing Privacy and Security Difficulties in Social Media: New Challenges and Solutions. IGI Global Scientific Publishing; Pennsylvania, United States, 2025; 87–120.
  22.  Kazi KSL, Shinde SS, Nerkar PM, Kazi SS, Kazi VS. Machine learning for brand protection: A review of a proactive defense mechanism. In: Khan MI, Amin Ul Haq M, editors. Avoiding Ad Fraud and Supporting Brand Safety: Programmatic Advertising Solutions. Pennsylvania (USA): IGI Global Scientific Publishing; 2025. p. 175–220. DOI: 10.4018/979-8-3693-7041-4.ch007.
  23. Xing L. Cascading failures in Internet of things: Review and perspectives on reliability and resilience. IEEE Internet Things J. 2021;8:44–64. DOI: 10.1109/JIOT.2020.3018687.
  24.  Xiaolin ES. Cyber security: Basics in fighting computer attacks and crimes. J Inf Knowl Manag. 2017;7:21–7.
Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 31/05/2025
Accepted 31/05/2025
Published 30/06/2025
Publication Time 30 Days
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