Are Formula 1 Cars Racing like Dolphins in Water?

Complex engineering and leading-edge technology that goes into designing Formula 1 racing cars set them apart from the others in the game. Recently, there have been talks about this new term "porpoising" around F1 cars, bouncing up and down the racing track like dolphins in the water, during the Barcelona 2022 pre-season testing. Porpoising is a physical phenomenon that has been encountered by automotive for more than four decades and yet, there is no solid design rectification in place. According to Charles Leclerc, the F1 racing driver for Scuderia Ferrari, porpoising for him “feels like turbulence on an airplane, going up and down the whole straight” and made him feel a little bit ill. These racing cars have undergone several simulation tests before they ran on the track. Despite being refined to a great extent, simulation technologies have limitations in predicting real-world behaviors. Even wind tunnel testing cannot offer satisfying results because the dynamics inside the tunnel are slow and the underfloor aerodynamics at low ride heights cannot be accurately modeled. This leaves F1 automotive designers with no choice but to rely on real-world testing, as it affects both the driver and the vehicle. With countless possibilities in the real world, the numerous factors that need to be accounted for in a close-to-reality simulation make it a tedious task. In the last few years, artificial intelligence as a technology has been helping humans predict climate change, boost innovation, and enhance breakthroughs in healthcare, which is also expected to do wonders in simulation technology. Employing artificial intelligence to compile human inputs and real-world data from sensors installed on existing models, can bridge the gap between labor hours and accuracy towards an advanced simulation technology.

The transition from the vintage silo physics approach to Multiphysics studies, pushing the limits to design cars, planes, and ships, was expected to reduce failures in locomotives. Today, along with wind tunnel testing we have high-performance simulation software, that has increased the level and degree of checks before vehicles step into the production phase. Despite stringent quality control measures, we have come across several cases of vehicle failures. The failure of an aircraft maneuvering system causing stalling and subsequent fatal accidents is one such example. Even after CFD studies of the underhood thermal environment, a plug-in hybrid vehicle was caught on fire because of poor engine compartment packing and exhaust routing. These pieces of evidence substantiate that virtual design simulations cannot entirely replace real-world testing, instead, a tandem of computer simulation along with testing can produce more efficient results or we should rely on AI to bring together the human input and other relevant data for an accurate and reliable design solution.

The 7 biggest AI technologies that are bringing about transformational changes in 2022 are:-

1. Workforce augmentation: In our current data-driven world, AI-infused cultures at the workplace are helping humans boost their abilities and skill-sets for increased productivity and reduced errors.

2. Advanced language modeling: Computers understand our language and they communicate with us using language models. The recent release of GPT-3 by OpenAI, uses 175 billion parameters to process language in the form of variables and data points making it one of the most powerful language models available today.

3. Enhanced cybersecurity: As more and more human activities are replaced by machines connected to a network, the potential risk to your data is at its peak. With AI, network traffic and pattern recognition can alert suspicious accounts or intentions using smart algorithms.  

4. Metaverse: It is a digital environment created by combining virtual reality technology with the Facebook platform. AI technology has helped in creating this metaverse environment where users can play and work together with immersive experiences.

5. No-code or low-code solutions: The lack of skilled AI engineers was one of the barriers to the adoption of AI technology at the workplace. But with simple interfaces in no-code or low-code solutions, any person with the basic know-how of using computers and browsing through web pages can build complex AI systems.

6. Autonomous Vehicles: In 2022, we are ought to witness cars and ships with full self-driving capabilities enabled by AI to race on the roads and to sail in the waters. Mayflower, an autonomous ship designed and powered through a collaboration between IBM and ProMare is set for its second attempt to cross the Atlantic.

7. For creativity: So far, creativity and uniqueness were seen as human skills, but with new emerging models in 2022, such as GPT-4 and Google’s brain that uses AI, new capabilities in machines will emerge to cross the boundaries of artificial intelligence to come closer to the concept of “real intelligence."

With electrification around the corner, achieving the greenhouse gas reduction targets through sustainable practices in the automotive sector is crucial and is possible only through quick and fast design solutions. Using a combination of real-world testing, wind tunnel testing, and simulation results would endanger our available resources and the allotted time limitations. Hence, fine-tuning our simulation technologies using AI can enable accurate and early prediction of faults in the design cycle, saving both human hours and resources towards creative solutions or systems.