In this article we continue with the subject of aerodynamic performance metrics, discussing how characterising the performance of “many” leads to substantially different challenges compared to the characterisation of “one”.
We most often tend to think of aerodynamic performance from the perspective of the engineering team designing or running a particular car or craft when the objectives tends to revolve around maximising its overall performance.
While working with motorsport governing bodies, however, we see a different perspective to the same subject. These bodies’ goals are to ensure fairness of competition and, in some championships, ensure a reasonable level of equilibrium in the performance of the cars.
While the ultimate performance remains important to create a spectacle and attract fans, controlling the competitive differences across teams and ensuring economic viability for those involved is just as important for the creation of a healthy business case.
A few categories around the world use Balance of Performance (BOP) methods to mitigate differences across different cars. These rely on a performance metric of some sort, and result in restrictions applied to different teams regarding weight, power usage or aerodynamic changes.
The performance metrics used can be as simple as looking into the championship points differences between cars at certain points in the season or as complex as involving controlled wind tunnel testing, aeromaps and in-depth knowledge of the power unit systems.
Building a BOP using aerodynamic data poses several challenges to the engineers working with the governing body. Here, the testing procedures must be designed to produce data covering a wide range of operating conditions, representative of all the possible combinations of car architectures and setups to be seen on track. It becomes a balancing act between ensuring sufficient coverage without too much dilution in the data gathering.
The derived aerodynamic performance metrics must be sufficiently generic to accommodate the differences across the field, avoiding overfitting around the operating conditions of one or two cars only while retaining a proportionate level of accuracy to serve its purpose for the BOP.
The challenge, ultimately, remains the same: defining performance metrics which describe well what success means. For the governing bodies applying a BOP, success means fairness, a reasonable level of performance parity and economic viability.
In our next article we will temporarily depart from motorsport, we will discuss aerodynamic performance and metrics applied to aerospace propellers, using one of Sabe’s case studies for illustration.