Written by Rut Linneswala & Aditya Singh;
The 2023 Formula Racing sports season is going to be one of the most exciting in recent years. Formula racing for the uninitiated, not only includes the ever-popular Formula One, but also Formula E, Formula Two, Formula Three, Regional Formula Three, and Formula Four categories just to name a few.
This season, budget caps on the back of rising commodity and fuel prices will be a challenge for most teams. Formula 1 for example, will see a marginal budget drop in 2023 to $135Mn per team from $140Mn in 2021. The trickle effect is expected to spill over across other Formula racing categories too.
Given the advanced tracking technologies, technical changes have been narrowly defined by the motorsport bodies, in order to make the cars safer, and faster, and make it a level playing field for all teams. Teams need to comply with metrics across various parameters ranging from Aerodynamic Oscillations (AOM), floor height, minimum weight, fuel temperature, tire use, and many others.
With global warming being a more serious issue there is a moral obligation for motorsports to adapt. The surge in popularity of Formula E as an eco-friendly version gives some insight into the future, made possible with cutting-edge technology.
The challenge for engineering teams in Formula sports
The combination of high speeds, precision driving, and bleeding edge engineering makes Formula racing exciting, giving us those timeless edge-of-the-seat moments.
Add to this the competitive pressure that is intense, teams have no option but to keep innovating to stay ahead. Whether it's through aerodynamic tweaks, new materials, or more efficient powertrains, the tech teams have to optimize every engineering metric to develop the race car, make it go faster, and last the season.
Expectations from the engineering team to solve complex design problems given the conflicting constraints are immense. The margins are incredibly small with the smallest variable having the potential to massively change the outcome of the result. Even something as seemingly insignificant as the tire pressure, the weight of a bolt, or the shape of a wing can have a significant impact on the speed and handling of the car
This is where faster, accurate simulations can revolutionize Formula racing.
Unlike traditional design processes, simulations combined with Quantum computing algorithms can be applied in a number of ways. Teams can perform simulations much faster, with higher accuracy and faster turnaround than is possible with classical computers.
Designs can be tested, validated, and delivered much faster, reducing the overall cost of production, and reducing the engineering lead time, which is critical for adapting to conditions that change race to race
Currently, some design engineering simulations are too complex or too energy intensive for High Powered Classical (HPC) computing. Quantum-enabled algorithms, combined with the power of HPC can solve next-generation engineering problems, that could make or break a coveted podium finish.
Simulations powered by Quantum for engineering breakthroughs
Performing computational fluid dynamics simulating the flow of air over the car's surface and analyzing the resulting drag and lift forces, engineers can identify ways to improve the car's aerodynamics by redirecting the airflow to cool the components, or the braking system to reduce overall drag. This can help drivers navigate tight bends, overtake and achieve higher fuel efficiency.
In addition to optimizing the shape of the car's body, simulations with quantum computing can also be used to optimize other aspects of the car's design, such as the layout of the internal components, simulation design for suspension and braking systems for better car handling to comply with various norms defined.
Simultaneous simulations with a high-accuracy simulation solver can help with highly data-intensive and complex engineering processes to improve Aerodynamics, Safety, vibrations and shock absorption, Fuel Efficiency, and energy transfer among other features.
Structural Design simulations
Cars racing at speeds of 350 km/hour, pushed to the limits by champion drivers are subject to high structural stress. Results from structural simulations, modeling the behavior of various materials and forces can lead to better-designed cars with lower maintenance costs for the entire season
Simulations with quantum-enabled algorithms can compute deformations, stresses, and strains in materials with higher accuracy, much faster, compared to classical engineering simulation systems
Heat transfer properties and behavior of the system under different conditions and its response to changes in temperature can be better predicted better with simulation using quantum mechanical principles.
Quantum machine learning (QML) algorithms, for example, can be trained on data from classical thermal analysis simulations for predicting the thermodynamic properties of materials and systems based on their mechanical structure.
These simulations are vital to determine ambient fuel temperature, energy transfer efficiency, and Tire wear based on driving conditions. It can help the engineering team to suggest drivers precise Pit Stops and acceleration points on straights giving the teams that vital split second advantage.
The hybrid approach – A game changer
Overall, the ability to adapt faster and better is often the difference between in a hyper-competitive sport like Formula Racing. Quantum computing, though still at a nascent stage may be the game changer by providing higher accuracy of simulation, resulting in lower costs and reduced turn-around time for prototyping to production.
Investing in Quantum computing for simulations in terms of budgets and manpower is a challenge given the budget cap constraints for teams.
A hybrid quantum approach for simulations involving the use of both classical computers and quantum computers to perform simulations with a subscription-based pricing model can allow Formula sports teams to circumvent the budget caps. Plug-and-play integration with existing systems using QPUs, GPUs, and CPUs is a cost-effective way forward, in the immediate term.
The hybrid approach can be designed in a number of ways, using classical computers to pre-process and post-process data, and using quantum computers to perform the most computationally intensive parts of the simulation.
This approach can be beneficial as it utilizes the strengths of both types of computers in a single simulation, and lead to more efficient and accurate simulations.