AI-Assisted Design and Theoretical Aerodynamic Evaluation of a Multistage Conical Diffuser Micro Wind Turbine Fabricated from Polymeric Composites

Year : 2026 | Volume : 14 | Special Issue 02 | Page : 1111 1118
By

Ajai Vaidyanathan H.,

Ravikumar S.M.,

Adinarayanan A.,

Arul M.,

Yuvaraj N.,

Dinesh S.,

Ida G.,

Sharmila S.,

  1. Assistant Professor, Department of Electronics and Electronics Engineering, Dhanalakshmi College of Engineering, Chennai, Tamil Nadu, India
  2. Professor, Department of Mechanical Engineering, Vandayar Engineering College, Thanjavur, Tamil Nadu, India
  3. Professor, Department of Mechanical Engineering, AMET University, Chennai, Tamil Nadu, India
  4. Assistant Professor, Department of Mechanical Engineering, ARM College of Engineering and Technology, Tamil Nadu, India
  5. Associate Professor, Department of Computer Science Engineering, Adhi College of Engineering, Kanchipuram, Tamil Nadu, India
  6. Associate Professor, Department of Mechanical Engineering, Dhanalakshmi College of Engineering, Chennai, Tamil Nadu, India
  7. Assistant Professor, Department of Chemistry, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India
  8. Assistant Professor, Department of Computer Applications, Karpagam Academy of Higher Education, Echanaari, Coimbatore, Tamil Nadu, India

Abstract

The rising demand for compact, lightweight, and high-efficiency renewable energy systems has accelerated the development of micro wind turbines capable of delivering stable performance under low wind conditions. Traditional bare-rotor micro turbines suffer from limited aerodynamic efficiency, motivating the adoption of diffuser-augmented architectures and advanced polymer-based composite materials. This study presents the AI-assisted conceptual design and theoretical aerodynamic evaluation of a multistage conical diffuser micro wind turbine fabricated using a polymer-reinforced hybrid composite structure to achieve enhanced stiffness-to-weight ratio, corrosion resistance, and structural durability. A three-stage conical diffuser integrated with co-axial rotors was developed through iterative model generation, refinement, and aerodynamic reasoning using ChatGPT as a generative design assistant. Performance assessment was conducted through analytical relations and extrapolation of validated CFD data from prior diffuser-augmented turbine studies, focusing on velocity amplification, pressure gradients, and stage-wise power enhancement. The proposed multistage configuration achieved cumulative airflow acceleration of approximately 2.38× and an estimated theoretical power improvement of nearly 13× compared to a bare rotor, outperforming conventional single-stage diffuser systems. The use of polymer hybrid composites further enables reduced mass, manufacturability, and suitability for portable, urban, and off-grid applications. Overall, the study demonstrates the aerodynamic advantages of sequential diffuser staging and highlights the promising role of generative AI in accelerating composite-based micro-wind turbine design workflows.

Keywords: Hybrid renewable energy system, multistage conical diffuser, polymer hybrid composite micro wind turbine, AI-assisted design, off-grid power generation.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

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How to cite this article: Ajai Vaidyanathan H., Ravikumar S.M., Adinarayanan A., Arul M., Yuvaraj N., Dinesh S., Ida G., Sharmila S.. AI-Assisted Design and Theoretical Aerodynamic Evaluation of a Multistage Conical Diffuser Micro Wind Turbine Fabricated from Polymeric Composites. Journal of Polymer & Composites. 2026; 14(02):1111-1118.
How to cite this URL: Ajai Vaidyanathan H., Ravikumar S.M., Adinarayanan A., Arul M., Yuvaraj N., Dinesh S., Ida G., Sharmila S.. AI-Assisted Design and Theoretical Aerodynamic Evaluation of a Multistage Conical Diffuser Micro Wind Turbine Fabricated from Polymeric Composites. Journal of Polymer & Composites. 2026; 14(02):1111-1118. Available from: https://journals.stmjournals.com/jopc/article=2026/view=240564

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Special Issue Subscription Original Research
Volume 14
Special Issue 02
Received 08/12/2025
Accepted 29/12/2025
Published 23/04/2026
Publication Time 136 Days

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