Air Flow Analysis in Sensor-Based Aircraft Wings Design

Year : 2025 | Volume : 12 | Issue : 02 | Page : 29 39
    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

Airflow analysis, at its core, is an application of fluid mechanics principles to the specific study of air movement. Airflow analysis plays a crucial role in diverse fields, impacting everything from the comfort of our homes to the efficiency of jet engines. The pursuit of more efficient, safer, and more responsive aircraft wings is a constant driver of innovation in the aerospace industry. The quest for more efficient, safer, and adaptable aircraft is a never-ending one. Traditionally, wind tunnels and computational fluid dynamics (CFD) have been the cornerstones of aerodynamic design and analysis. However, these methods often fall short in capturing the complex, real-world conditions experienced by aircraft in flight. Traditional methods of aerodynamic analysis, while valuable, often fall short in capturing the dynamic and complex realities of airflow behavior under real-world flight conditions. This is where sensor-based aircraft wings, equipped with a network of strategically placed sensors, offer a paradigm shift, providing a treasure trove of data for in-depth airflow analysis. Enter the era of sensor-based aircraft wings, offering a revolutionary approach to understanding and optimizing airflow. Sensor- based aircraft wings represent a paradigm shift in aerodynamic design and analysis. By providing real- time, in-flight data, this technology is paving the way for more efficient, safer, and adaptable aircraft. It is anticipated that the use of sensor technology in the aviation industry will expand quickly as it developsfurther, revolutionizing flight in the future. Aircraft autonomy and decision-making are further improved by combining these sensor networks with AI and machine learning. Analyzing data in real time can assist spot performance irregularities, maximize fuel efficiency, and guarantee structural integrity throughout flight. This lowers maintenance costs, increases operating life, and increases aircraft reliability. Real-time monitoring of vital factorslike air speed, altitude,structural stress, engine performance, and external environmental variables is made possible by sophisticated sensors integrated into aircraft systems. Predictive maintenance, operational efficiency, and flight safety are all greatly improved by this ability to “sense the skies” and react quickly to changing conditions. As sensor technology endures to advance, we may expect to see even more widespread adoption of this innovative approach, transforming the future of flight. The ability to “sense the skies” and react in real- time will undoubtedly lead to remarkable breakthroughs in aircraft performance and safety. This article explores how this innovative technology is transforming aircraft design and performance.

Keywords: Fluid mechanics, air flow, aircraft wings, aerodynamic, sensors

[This article belongs to Recent Trends in Fluid Mechanics ]

How to cite this article:
Nikat Rajak Mulla, Kazi Kutubuddin Sayyad Liyakat. Air Flow Analysis in Sensor-Based Aircraft Wings Design. Recent Trends in Fluid Mechanics. 2025; 12(02):29-39.
How to cite this URL:
Nikat Rajak Mulla, Kazi Kutubuddin Sayyad Liyakat. Air Flow Analysis in Sensor-Based Aircraft Wings Design. Recent Trends in Fluid Mechanics. 2025; 12(02):29-39. Available from: https://journals.stmjournals.com/rtfm/article=2025/view=222898


References

  1.  Mishra SB, Liyakat KK. AI-Driven-IoT (AIIoT) Based Decision-Making in Molten Metal Processing. Journal of Industrial Mechanics. 2024 Nov 21;9(2):45–56.
  2.  Yüksel S, Dinçer H. A decision-making framework for the development of molten-salt reactors: prioritizing environmental and technological factors. J Soft Comput Decis Anal. 2024;3(1):18–25.
  3.  Liyakat KK, Halli UM. Nanotechnology in IoT security. Journal of Nanoscience, Nanoengineering & Applications (JONSNEA). 2022;12(3):11–6.
  4.  Devanand WA, Raghunath RD, Baliram AS, Kazi K. Smart agriculture system using IoT. Int J Innov Res Technol. 2019;5(10):480–3.
  5.  Liyakat KK. Detection of Malicious Nodes in IoT Networks based on packet loss using ML. J Mobile Comput Commun Mobile Netw. 2022;9:9–17.
  6.  Liyakat KK. Model for Agricultural Information system to improve crop yield using IoT. Journal of Open Source Development (JoOSD). 2022;9(2):16–24.
  7. Aavula R, Deshmukh A, Mane VA, Chavhan GH, Liyakat KK. Design and Implementation of sensor and IoT based Remembrance system for closed one. Telematique. 2022;21(1):2769–78.
  8. Kazi Kutubuddin SL. A novel design of IoT based ‘ representation and ’ system to loved ’ . Gradiva Rev J. 2022;8(12):377–83.
  9.  Kazi KS. IoT based healthcare system for home quarantine people. J Instrum Innov Sci 2023;8(1):1–8.
  10. Sayyad Liyakat KK, Krishna KH, Shirashyad VV, Ishwar J, Aavula R. Pattern Recognition – An Approach Towards Machine Learning. 1st ed. Lambert Publication’s; 2022. ISBN: 978-93-91265- 58–8.
  11. Kumar M, Mukherjee P, Verma K, Verma S, Rawat DB. Improved deep convolutional neural network based malicious node detection and energy-efficient data transmission in wireless sensor networks. IEEE Transactions on Network Science and Engineering. 2021;9(5):3272–81.
  12. Kazi KS. IoT-based healthcare monitoring for COVID-19 home quarantined patients. Recent Trends in Sensor Research & Technology (RTSRT). 2022;9(3):26–32.
  13. Liyakat KK, Mohiuddin KG, Shaikh MC, Kothawale PJ, Shirashyad VV. Machine learning-based system, food quality inspection and grading in food industry. Int J Food Nutr Sci. 2022;11(10): 723–730.
  14. Chinthamu N, Prasad M, Chinchawade AJ, Sayyad Liyakat KK, Deepti K, Karukuri M, Manohar Kumar C. Self-secure firmware model for blockchain-enabled IoT environment to embedded system. Eur Chem Bull. 2023;12(S3):653–60.
  15.  Kutubuddin K. A Study HR Analytics Big Data in Talent Management. Research and Review: Human Resource and Labour Management (RRHRLM). 2023;4(1):16–28.
  16. Chinthamu N, Prasad M, Chinchawade AJ, Liyakat KK, Deepti K, Karukuri M, Kumar CM. Self- Secure firmware model for Blockchain-Enabled IOT environment to Embedded system. Eur. Chem. Bull. 2023;12:S3.
  17.  Nerkar PM, Shinde SS, Liyakat KK, Desai S, Kazi SS. Monitoring fresh fruit and food using IoT and machine learning to improve food safety and quality. Tuijin Jishu/J Propuls Technol. 2023;44(3):2927–31.
  18.  Suryagan AA, Nemte AL, Thorat KD, Khadake SB. IoT Based Flood Monitoring System by using Thing Speak Cloud. IJARSCT. 2025 May;5(4): 666–686.
  19. Liyakat KK. Multimedia Technology in Healthcare: A Study. Journal of Multimedia Technology Recent Advancements (JoMTRA). 2025;12(01):23–9.
  20. Kumar M, Sul SS, Lakhara JS, Kashid PJ, Bhinge SR, Waghmode AS, Khadake SB. Small Wind Electric System Energy Saver. IJARSCT. 2025 May;5(5):447.
  21. Liyakat KK. VHDL Programming for Secure True Random Number Generators in IoT Security. Research & Review: Electronics and Communication Engineering (RRECE). 2025 Mar;2(1): 38–47.
  22.  Liyakat KK. E-Comers and AI: Product Recommendation and Pricing. J Artif Intell Res Adv. 2025;12(2):44–52p.
  23.  Mulani AO, Bang AV, Birajadar GB, Deshmukh AB, Jadhav HM, Liyakat KK. IoT Based Air, Water, and Soil Monitoring System for Pomegranate Farming. Ann Agri-Bio Res. 2024;29(2): 71–86.
  24. 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. In2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) 2024 Dec 12 (pp. 1735–1744). IEEE.
  25. Kazi KS, Mahant MA. Machine Learning-Driven Internet of Things (MLIoT)-Based Healthcare Monitoring System. InDigitalization and the Transformation of the Healthcare Sector 2025 (pp. 205–236). IGI Global Scientific Publishing.
  26.  Mulani AO, Liyakat KK, Warade NS, Patil A, Kolte MT, Kinage K, Rana M, Salunkhe SS, Jadhav VS, Nagrale M. ML-powered Internet of Medical Things Structure for Heart Disease Prediction. J Pharmacol Pharmacother. 2025:0976500X241306184.
  27.  Nerkar PM, Dhaware BU, Liyakat KS. Predictive data analytics framework based on heart healthcare system (HHS) using machine learning. J Adv Zool. 2023;44(2): 3673:3686.
Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 01/06/2025
Accepted 02/06/2025
Published 10/07/2025
Publication Time 39 Days
123

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