Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
AI-Powered Velocity Prediction and Enhancement in Invelox Wind Turbine

AI-Powered Velocity Prediction and Enhancement in Invelox Wind Turbine

Kumar, K. Ramesh
Raj, M. Selva

Paperback

Technology & Engineering

ISBN10: 6203029602
ISBN13: 9786203029604
Publisher: LAP Lambert Academic Publishing
Published: Dec 22 2024
Pages: 168
Weight: 0.56
Height: 0.39 Width: 6.00 Depth: 9.00
Language: English
Wind energy is a sustainable and viable alternative to fossil fuels. A novel high-performance design, I2NS2F (Integrated omni-directional Intake funnel, Natural fan, Straight diffuser, Splitter, and Flange), has been developed for wind turbines. This design features an intake funnel, natural fan, flow section, exit splitter, and flange. Four configurations were analyzed and optimized using MATLAB Simulink and Ansys Fluent.The I2NS2F design achieved a remarkable wind velocity of 53 m/s at the turbine region with an inlet speed of 5.5 m/s, outperforming other configurations. To address the intermittent nature of wind velocity, an enhanced Deep Learning (DL) model utilizing Long Short-Term Memory (LSTM) optimized with Black Widow and Mayfly algorithms was implemented for velocity prediction. Validated through subsonic wind tunnel tests using 3D-printed miniature models, the model demonstrated high accuracy, establishing its effectiveness for wind velocity prediction and power optimization in INVELOX type wind turbines.

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