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Home»Technology»How AI Helps Design Better Airplanes
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How AI Helps Design Better Airplanes

October 26, 2024Updated:October 26, 2024No Comments5 Mins Read
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Designing modern aircraft is a complex challenge that requires advanced computational tools. Researchers have developed a new artificial intelligence (AI) technique called neural networks that can rapidly simulate the complex aerodynamics of aircraft. This breakthrough could revolutionize the aircraft design process, allowing engineers to test thousands of designs virtually before building physical prototypes. The research, led by a team from Airbus and ISAE-Supaero, demonstrates how this AI-powered approach can accurately predict the intricate airflow patterns around 2D airfoils and 3D wings, including the formation of shock waves at high speeds. With its potential to drastically reduce the time and cost of aircraft development, this innovative technology could pave the way for more efficient, sustainable, and affordable air travel in the future.

Designing the Future of Flight

Designing modern aircraft is a complex challenge that requires advanced computational tools. Computational intelligence’>artificial intelligence, have emerged as a promising solution to this challenge. Researchers have developed a new AI-powered approach called Implicit Neural Representations (INRs) that can rapidly simulate the complex aerodynamics of aircraft, including the formation of shock waves at high speeds.

figure 1
Fig. 1

Unlocking the Secrets of Airflow

The key innovation of the INR approach is its ability to learn a continuous representation of the aerodynamic flow field, rather than relying on a fixed grid or mesh. This allows the model to accurately capture the intricate details of the airflow, even at high resolutions, without being constrained by the limitations of traditional numerical simulations.

The researchers tested their INR-based surrogate models on two challenging datasets: a transonic airfoil and a 3D wing with various shape variations and control surface deflections. The results were impressive, with the INR models achieving significantly lower error rates compared to state-of-the-art Click Here

This article is made available under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. This license allows for any non-commercial use, sharing, and distribution of the content, as long as you properly credit the original author(s) and the source, and provide a link to the Creative Commons license. However, you are not permitted to modify or adapt the licensed material. The images or other third-party content in this article may have additional licensing requirements, which are indicated in the article. If you wish to use the material in a way that is not covered by this license or exceeds the permitted use, you will need to obtain direct permission from the copyright holder. To view a copy of the license, please visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
aerodynamics aircraft design Artificial intelligence artificial neural networks aviation impact computational fluid dynamics future of sustainable transportation infrastructure
jeffbinu
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Tech enthusiast by profession, passionate blogger by choice. When I'm not immersed in the world of technology, you'll find me crafting and sharing content on this blog. Here, I explore my diverse interests and insights, turning my free time into an opportunity to connect with like-minded readers.

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