Classic application of big data F1 racing energy-saving reform

Energy saving and environmental protection have always been the response to various fields. Nowadays, Formula One cars also set off energy-saving and environment-friendly reforms that will reduce the engine of the racing engine from V8 to V6 while ensuring the speed. At the same time, the Chinese saying goes, letting the horses run and the horses not grazing. For this classic game on the court, I am afraid that only big data, can become the best choice to solve it.

F1 Circuit may be one of the most classic scenarios for big data. The design, simulation, testing and construction of a fast-paced $ 2 million Formula One racing car is done entirely in the computer. Every step of the process, Generate large amounts of data. Although the F1 simulation test requires expensive computer hardware and software environment, but it is still lower than the measured cost on the track, lotus team owner Patrick Louis has revealed that an F1 car actually measured on the track costs up to 40-60 Ten thousand U.S. dollars.

In addition, the FIA ​​has promulgated very specific rules on the design of F1 racing cars. For example, the chassis height of a car should not be less than 10 cm and must not use removable aerodynamic components. Formula One car makers such as Lotus, Ferrari and McLaren must rack their brains to design better-performing cars.

As a result, all major F1 teams have been heavily engaged in the design, testing and manufacturing of racing cars with advanced CFD and CAD / CAM. Especially in the test session, every F1 car is a big data generator, placing a high demand on the computing and storage environment. The F1 car, which is typically used for testing, will have 240 sensors installed and generate 25MB of data per turn, which is sent back to the factory via satellite link - where engine data and chassis data will be processed separately to analyze component performance And wear condition. In addition, big data forecasting analysis is also an important application of F1 racing test. For example, McLaren team can collect data in real time through pre-competition field tests by using car sensors, combine historical data with predictive analysis to find racing problems and take preemptive Race car tuning measures to reduce the chances of accidents and improve race winning percentage.

This year, with the FIA ​​promulgating new "green" engine rules. The major Formula One teams face an enormous challenge of how to swap a 2.4-liter V8 engine with a fuel-efficient 1.6-liter V6 engine without sacrificing racing speed. This means a major redesign of the car is required, and the key to completing this task is big data.

Lotus team's big data equipment

The new FIA rules mean that major Formula One teams need to make major changes to their car designs in order to reach new energy-efficiency targets, and the new V6 engines also require new, traditional system presets.

Lotus's strategy is to deploy two parallel IT projects to support the new car design and testing. One of them is on the factory side, running Microsoft Dynamics Business Suite and the other is on the team side, mainly for data acquisition and analysis related to track testing.

It is learned that Lotus will use EMC's storage, virtualization software and Cisco servers to build its big data environment, in addition, according to EMC marketing director Jeremy Burton revealed to GigaOM, Lotus may also purchase EMC's Atoms for storage and management of content, Syncplicity Synchronize and share files offsite, and Data Domain for backup and recovery.