Ai Powered Velocity Prediction Enhancement by Kumar Ramesh (8 results)

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Taschenbuch. Condition: Neu. AI-POWERED VELOCITY PREDICTION AND ENHANCEMENT IN INVELOX WIND TURBINE | K Ramesh Kumar (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786203029604 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 168 pp. Englisch.

Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 168 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.