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Aerodynamic testing is vital in racing and aviation, especially in Formula 1. But strict wind tunnel testing limits make it hard for teams to run enough experiments. Aerofy solves this by using Generative AI, offering real time insights to engineers. This allows faster design optimizations with fewer alterations needed on physical tests.

The Role of GenAI in Aerodynamic Testing

Traditional aerodynamic testing relies on trial and error, with engineers manually adjusting designs based on wind tunnel data. Aerofy’s GenAI platform streamlines this by predicting airflow and recommending optimal configurations in real-time, cutting down the need for physical tests. This innovation allows F1 teams and aviation manufacturers to work more efficiently within regulatory limits, accelerating design improvements and cutting down on the number of alterations.

Market Research Insights

Our research into the market showed a competitor like AirShaper, which has successfully used virtual wind tunnels, demonstrating the potential for digital solutions in aerodynamic optimization. Building on this, Aerofy’s GenAI-driven approach is designed to provide even more precise, real-time insights, addressing the growing demand for faster, more cost-effective testing. Industry feedback emphasizes the need for more advanced predictive tools, reinforcing Aerofy’s value proposition in this rapidly evolving market.

Impact on Racing and Aviation Industries

Aerodynamics is vital in motorsports like Formula 1, where small improvements can greatly impact performance. Aerofy’s GenAI platform allows racing teams to quickly analyse data and make design adjustments, maximizing efficiency within testing limits. This leads to faster innovation and reduced costs. From the research done into Airshaper multiple Racing companies have made testimonials, stating the effectiveness of the service and product.

In aviation, Aerofy’s technology helps manufacturers reduce drag, which cuts fuel consumption and operational costs. By optimizing aerodynamic designs and providing real-time insights, the platform enhances performance, safety, and efficiency for both industries. 

Objectives, Challenges, and the Path Forward

Aerofy’s primary objective is to revolutionize aerodynamic testing by enabling racing and aviation teams to make smarter, data-driven decisions in real-time. This will help optimize performance, reduce costs, and promote sustainability.

However, the platform depends on large, high-quality datasets from wind tunnel tests and simulations to train its GenAI models effectively. Limited data could impede on the accuracy of predictions. Moreover, ensuring real-time performance requires robust cloud infrastructure. Strong partnerships with AI providers, cloud services, and industry experts will be crucial in overcoming these challenges. All of this is also accompanied by high initial investment costs which could be become an entry barrier not only for us but also for our customers.

Conclusion

Aerofy’s innovative GenAI platform stands to transform aerodynamic testing across the racing and aviation industries. By providing real-time insights and reducing the reliance on physical tests, Aerofy enables engineers to make faster, more efficient decisions, ultimately driving performance, efficiency, and cost savings. While challenges remain in terms of data acquisition and scaling, the potential impact on these industries makes Aerofy a key player in the future of aerodynamic optimization.

Group: 29

Sebastiaan Damen- 576096

Kenneth Tang- 583744

Luis Eggert- 560311

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(Gen)AI is a term which is being mentioned endlessly in improving real world workflows. So I thought to myself let’s see if I can use this to my advantage to photoshop a nice picture I have; but which isn’t quite there yet. Mind you, I didn’t have any Photoshop skills prior to this endeavor.

I had a nice picture which I wanted the background to be cleared up. I have seen many social media posts where they made this work flawlessly. So I went into this with the idea that it would be easy, and to a certain extent it was. Take a brush and highlight what you need replaced.

However the AI seems to take the liberty of making changes you never asked for. The picture at hand can be found below. The request was simple; clear the background and replace it with the building behind. The blurry image almost suggest that the AI isn’t able to recognize a simple building pattern with the windows.

This picture was by far the best rendition of me trying out this tool. A major hurdle to get over was that the AI wanted to head into a different direction than the prompt given and area selected. You cannot see it that well in the above picture but its already ‘eating away’ at the front tire which isn’t circular anymore. With this final Product I might be able to upscale the picture myself, but that wasn’t the expectation when I started this little project. After having wasted a few hours I laid the work done and more or less came to the conclusion that a professional would have been able to complete the project Quicker and deliver a higher quality product.

To at least give the AI some credit, the color grading and mixing is pretty good, especially since the area I tried to edit is quite large at a high resolution. And the picture could have come out quite realistic, if it wasn’t for the bad pattern recognition that the AI is trained on.
Moreover, with ‘realistic’ AI generated images I find the pictures to be quite soulless, as the lighting seldom gets captured in any good way.

Finally, I do think that it is inevitable that with more time, resources and more complex learning models AI will be able to capture more photorealism. But even once this is achieved, I don’t believe Photographers need to worry about their job if they are able to implement tools like these themselves.