Revolutionizing Optical Fibre Field Distribution with Linear Finite Element Method

[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n[if 2704 equals=”Yes”]n

n

Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

n[/if 2704]n

n

Year : 2025 [if 2224 equals=””]24/09/2025 at 5:08 PM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 32 42

n

n

nn

n

n

n

    By

    n

    [foreach 286]n

    n

    Ayesha Khalil Mulani, Kazi Kutubuddin Sayyad Liyakat,

    n t

  • n

    n[/foreach]

    n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Student, Professor & Head, Department of Electronics & Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Department of Electronics & Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, Maharashtra, India, India
  2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

n

n

nThis study investigates the use of the linear Finite Element Method (FEM) for the analysis of the field distribution in optical fibres. Understanding the distribution of electric and magnetic fields is necessary to characterise fibre properties including mode profiles, propagation constants, and dispersion, all of which are essential for optimising fibre performance in a range of applications like sensing and telecommunications. This study emphasises on applying a linear FEM formulation to efficiently solve the governing Helmholtz equation in order to precisely calculate the modal fields in optical fibres with different cross-sectional geometries and refractive index profiles. The accuracy and computational efficiency of the technique are evaluated, showing promise for complex fibre design analysis and simulation. This technique provides useful information on how light travels via optical fibres, which improves fibre performance and design. The scalar wave equation can be developed using an analytical simplification of Maxwell’s equations. For modes such as LP 01, LP 21, and LP 31, various field and contour distributions can be achieved by varying the core radius. The modal characteristics of an optical cable can be examined using the finite element method (FEM). After FEM analysis, Maxwell’s equation yields the traditional eigenvalue equation. The finite element method (FEM) can be used to approximate the scalar wave equation. A higher degree of precision can be achieved by increasing the number of elements and switching from the linear to the quadratic finite element technique. Any waveguide problem can be properly and comprehensively analysed using the finite element technique (FEM).nn

n

n

n

Keywords: Field distribution, optical fibre, finite element method, eigen value, eigen vector

n[if 424 equals=”Regular Issue”][This article belongs to Trends in Opto-electro & Optical Communication ]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Trends in Opto-electro & Optical Communication (toeoc)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article:
nAyesha Khalil Mulani, Kazi Kutubuddin Sayyad Liyakat. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Revolutionizing Optical Fibre Field Distribution with Linear Finite Element Method[/if 2584]. Trends in Opto-electro & Optical Communication. 10/09/2025; 15(03):32-42.

n

How to cite this URL:
nAyesha Khalil Mulani, Kazi Kutubuddin Sayyad Liyakat. [if 2584 equals=”][226 striphtml=1][else]Revolutionizing Optical Fibre Field Distribution with Linear Finite Element Method[/if 2584]. Trends in Opto-electro & Optical Communication. 10/09/2025; 15(03):32-42. Available from: https://journals.stmjournals.com/toeoc/article=10/09/2025/view=0

nn

n

n[if 992 equals=”Open Access”]Full Text PDF[/if 992]n

n

n[if 992 not_equal=”Open Access”]n

n

n[/if 992]n

nn

nnn

n[if 379 not_equal=””]nn

Browse Figures

n

n

n[foreach 379]

figures

[/foreach]n

n

n

n[/if 379]

n

n

n

n

n

References n

n[if 1104 equals=””]n

  1. Kazi KS Liyakat. Analysis for field distribution in optical waveguide using linear FEM method. J Opt Commun Electron. 2023; 9(1): 23–8.
  2. Patil RM. Modelo De Apariencia Discriminatorio Para Un Sólido Seguimiento En Línea De Múltiples Objetivos. Telematique. 2023; 22(1): 24–43.
  3. Sayyad SL. Dispersion compensation in optical fiber: a review. J Telecommun Stud. 2023; 8(3): 14–9.
  4. Sayyad SL. Quantum key distribution in optical fiber communication: a study. Trends Opto-electro Opt Commun. 2025; 15(1): 30–40.
  5. Kazi KS. Fake cryptocurrency detection using Python. Recent Trends Program Lang. 2025; 12(1): 1–7.
  6. Parihar B, Ajmeera K, Valaboju S, Rashid SZ, Liz DRA S. Enhancing data security in distributed systems using homomorphic encryption and secure computation techniques. ITM Web Conf. 2025; 76: 02010. doi:10.1051/itmconf/20257602010
  7. Tamboli DA, Sawant VA, M HM, Sathe S. AI-driven-IoT (AIIoT) based decision-making-KSK approach in drones for climate change study. 2024 4th Int Conf Ubiquitous Comput Intell Inf Syst (ICUIS); Gobichettipalayam, India. 2024; 1735–44. doi:10.1109/ICUIS64676.2024.10866450
  8. Kazi KS Liyakat. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. 2023 Int Conf Emerg Smart Comput Inform (ESCI); Pune, India. 2023; 1–5. doi:10.1109/ESCI56872.2023.10099544
  9. Kazi KS. Malicious node detection in IoT networks using artificial neural networks: a machine learning approach. In: Singh VK, Kumar Sagar A, Nand P, Astya R, Kaiwartya O, editors. Intelligent networks: techniques and applications. CRC Press; 2024; 1–31. doi:10.1201/9781003541363
  10. Kasat K, Shaikh N, Rayabharapu VK, Nayak M. Implementation and recognition of waste management system with mobility solution in smart cities using internet of things. 2023 2nd Int Conf Augmented Intell Sustain Syst (ICAISS); 2023; Trichy, India. 2023; 1661–5. doi:10.1109/ ICAISS58487.2023.10250690
  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: E-Mobility in Electrical Energy Systems for Sustainability. IGI Global Scientific Publishing; 2024; 295–320.
  12. Kazi KS. 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 A, editors. Driving quality education through AI and data science. IGI Global Scientific Publishing; 2025; 531–56. doi:10.4018/978-8-3693-8292-9.ch023
  13. Kazi KS. 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. IGI Global Scientific Publishing; 2025; 357– 94. doi:10.4018/978-8-3693-8814-3.ch013
  14. Kazi KS. A study on AI-driven internet of battlefield things (IoBT)-based decision making: KSK approach in IoBT. In: Tariq M, editor. Merging artificial intelligence with the internet of things. IGI Global Scientific Publishing; 2025; 203–38. doi:10.4018/978-8-3693-8547-0.ch007
  15. Kazi KS. KK approach to increase resilience in internet of things: a T-cell security concept. In: Almaiah M, Salloum S, editors. Cryptography, biometrics, and anonymity in cybersecurity management. IGI Global Scientific Publishing; 2025; 199–228. doi:10.4018/978-8-3693-8014- 7.ch010
  16. Kazi S. Machine learning-based pomegranate disease detection and treatment. In: Haq MZU, Ali I, editors. Revolutionizing pest management for sustainable agriculture. IGI Global; 2024; 469–98. doi:10.4018/978-8-3693-3061-6.ch019
  17. Kazi S. Computer-aided diagnosis in ophthalmology: a technical review of deep learning applications. In: Garcia M, de Almeida R, editors. Transformative approaches to patient literacy and healthcare innovation. IGI Global; 2024; 112–35. doi:10.4018/978-8-3693-3661-8.ch006
  18. Kazi KKS Liyakat. 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. IGI Global; 2024. doi:10.4018/978-8-3693-3033-3.ch002
  19. K z k KS h G T: ch ’ g g I : B R h k A Hafaz Ngah A, Rabby F, Jain A, editors. AI algorithms and ChatGPT for student engagement in online learning. IGI Global; 2024; 1–20. doi:10.4018/978-8-3693-4268-8.ch001
  20. Liyakat KKS. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Udgata SK, Sethi S, Gao XZ, editors. Intelligent systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Singapore: Springer; 2024; 1–10. doi:10.1007/978-981- 99-3932-9_12
  21. Sayyad (2025). KK approach to increase resilience in internet of things: a T-cell security concept. In: Darwish D, Charan K, editors. Analyzing privacy and security difficulties in social media: new challenges and solutions. IGI Global Scientific Publishing; 2025; 87–120. doi:10.4018/978-8-3693-9491-5.ch005
  22.  Sayyad (2025). KK approach for IoT security: T-cell concept. In: Kumar R, Peng S-L, Elngar A, editors. Deep learning innovations for securing critical infrastructures. IGI Global Scientific Publishing; 2025.
  23.  Shinde SS, Nerkar PM, Kazi SS, Kazi VS. Machine learning for brand protection: a review of a proactive defense mechanism. In: Khan M, Amin Ul Haq M, editors. Avoiding ad fraud and supporting brand safety: programmatic advertising solutions. IGI Global Scientific Publishing;2025; 175–220. doi:10.4018/978-8-3693-7041-4.ch007
  24. Upadhyaya AN, Surekha C, Malathi P, Suresh G, Suriyan K, Liyakat KKS. Pioneering cognitive computing for transformative healthcare innovations. SSRN Electron J. 2025. doi:10.2139/ssrn. 5086894

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
  2. n[/foreach]

n[/if 1104]

n


nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n[if 1746 equals=”Retracted”]n

n

n

n

[/if 1746]n[if 4734 not_equal=””]

n

n

n

[/if 4734]n

n

Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 05/08/2025
Accepted 17/08/2025
Published 10/09/2025
Retracted
Publication Time 36 Days

n

n

nn


n

Login

n
My IP
n

PlumX Metrics

nn

n

n

n[if 1746 equals=”Retracted”]n

[/if 1746]nnn

nnn”}]