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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Nasir, Sheharyar
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Artificial-Intelligence Aerodynamics for Efficient Energy Systems: The Focus on Wind Turbines Nasir, Sheharyar; Zainab, Hira; Hussain, Hafiz Khawar
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 5 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The incorporation of AI in wind energy systems has transformed the design, operation and management of wind turbines, wind farms increasing their effectiveness, resilience and viability. This paper explores the transformative impact of AI-driven technologies across various aspects of wind energy, focusing on five key areas: Lear two main areas: in turbine engineering, advanced concepts such as fluid dynamics and blade design, while in computer sciences, major components consist of machine learning for performance assessment of turbines, monitoring of turbines on real-time basis as well as for the purpose of maintenance, and optimization of wind farms. In the specific application of improving the efficiency of turbine blade design and function, AI continues to be useful as machine learning is used in creating new and more efficient and long lasting blades while dynamic real time monitoring systems are used in making adjustments based on external conditions. AI-based predictive maintenance enables for mechanical problems identification before they evolve, thus decreasing the time a machine spends out of service and operational expenses. Also, AI enhances the design of wind farm, control of wake and load balance to enhance efficiency of wind electricity generation. It allows for a more effective intro of energy into the larger grid and hydrates therefore increasing the availability of renewable energy with stability. Based on this paper, the future of AI remains evident in future enhancement of wind energy systems, hence guaranteeing sustainable energy, efficiency, and cost-effectiveness in energy solutions for the overall energy transformation.
Active Learning Enhanced Neural Networks for Aerodynamics Design in Military and Civil Aviation Nasir, Sheharyar; Hussain, Hafiz Khawar; Ibrar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.5036

Abstract

The use of adaptive neural networks in aerodynamics design has become one of the most promising recent invention in both military and civil aircraft design, providing new approaches to the solution of a number of problematic issues connected with optimization of aircraft performance. Herein, this review provides a synthesis of neural networks and aerodynamics by emphasizing their ability to facilitate advanced design engineering, expedite the design process, as well as promote the usability and effectiveness of higher performing systems. Neural networks are involved in shape optimization, drag cutting, real time aircraft modifications and other key issue areas attesting to their capability in handling aerodynamics. Employing methods like supervised learning, reinforcement learning, and physics aware neural networks these networks can simulate non-linear multidimensional systems and arrive at solutions that are impossible through ordinary methods. The usage of these tools has been pushed even more over time, due to new advancements such as High-Performance Computing and specialized hardware. The review also considers effective application of systematic adaptive neural networks in the military and civil aviation hypersonic vehicle design, stealth aircraft design and optimization, the new fuel-efficient wings, and flight efficiency systems for real time control. The results put into evidence benefits of neural networks for cutting down design cycles, boosting MPG, increasing safety, and encouraging environmentally friendly solutions. The future for aerospace engineering will be in the hands of adaptive neural networks as part of the development of the aviation industry, dictating new advancements in both military and commercial aviation.
Active Learning Enhanced Neural Networks for Aerodynamics Design in Military and Civil Aviation Nasir, Sheharyar; Hussain, Hafiz Khawar; Ibrar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.5036

Abstract

The use of adaptive neural networks in aerodynamics design has become one of the most promising recent invention in both military and civil aircraft design, providing new approaches to the solution of a number of problematic issues connected with optimization of aircraft performance. Herein, this review provides a synthesis of neural networks and aerodynamics by emphasizing their ability to facilitate advanced design engineering, expedite the design process, as well as promote the usability and effectiveness of higher performing systems. Neural networks are involved in shape optimization, drag cutting, real time aircraft modifications and other key issue areas attesting to their capability in handling aerodynamics. Employing methods like supervised learning, reinforcement learning, and physics aware neural networks these networks can simulate non-linear multidimensional systems and arrive at solutions that are impossible through ordinary methods. The usage of these tools has been pushed even more over time, due to new advancements such as High-Performance Computing and specialized hardware. The review also considers effective application of systematic adaptive neural networks in the military and civil aviation hypersonic vehicle design, stealth aircraft design and optimization, the new fuel-efficient wings, and flight efficiency systems for real time control. The results put into evidence benefits of neural networks for cutting down design cycles, boosting MPG, increasing safety, and encouraging environmentally friendly solutions. The future for aerospace engineering will be in the hands of adaptive neural networks as part of the development of the aviation industry, dictating new advancements in both military and commercial aviation.
Co-Authors Hussain, Hafiz Khawar Ibrar Hussain Zainab, Hira