Formula One race cars get faster thanks to particle-image velocimetry and CFD

June 23, 2011
Consider the Formula One (F1) race car. Rather than just running on simple oval tracks and always turning left, F1s race on closed tracks — sometimes even on blocked-off city streets — and drivers must quickly brake, corner right or left

Toyota Motorsport

Consider the Formula One (F1) race car. Rather than just running on simple oval tracks and always turning left, F1s race on closed tracks — sometimes even on blocked-off city streets — and drivers must quickly brake, corner right or left, and accelerate along straightaways. Motorsports engineers thus try to optimize F1 aerodynamics for these tricky conditions. The vehicle’s complex design, along with constantly changing regulations, makes aerodynamics an evolving challenge, says Frank Michaux, aerodynamic engineer at Toyota Motorsport in Germany.

The open-wheel design of F1s disrupts airflow, creating wakes behind the front wheels, which, while local, can affect the entire vehicle’s performance, explains Michaux. The cars also sport airfoils that generate downforce while also reducing drag.

To get a handle on normally hidden aerodynamic effects, Toyota Motorsport engineers rely on a wind tunnel equipped for particle-image velocimetry (PIV). Technicians feed PIV data into Tecplot 360, a stand-alone computational fluid-dynamics (CFD) postprocessor. This lets researchers quickly correlate PIV results with CFD simulations — both measured and rendered by Tecplot software.

Toyota’s full-scale wind tunnel includes a “seeding” generator (a pressurized vessel), CCD digital camera, high-power laser, an optical device that converts a laser beam into a sheet of light, and dedicated software.

For PIV tests, engineers position the camera at 90º to the plane of the flow field they want to examine. The generator floods the tunnel with tiny particles of di-ethyl-hexyl-sebacat (DEHS), which have nearly the same density as air. The particles don’t affect airflow because they simply float.

Engineers then turn off all tunnel lights. A moving belt under the vehicle spins the wheels for more realistic load conditions. Air in the tunnel is accelerated to travel 50 m/sec. Next, researchers illuminate the portion of interest with a laser, converted via optics to a 2D plane of light. The camera takes two black and white snapshots of the plane every 10 to 20 msec — the white DEHS particles contrast well with the black background — making up a dataset. A complete data collection comprises 300 datasets, each containing the X, Y position of the measured point and its velocity.

“Understanding the wake behind the front wheel is important because it must be calibrated to get the best performance out of the F1,” says Michaux. “Because the front wheel is completely exposed to the air, wind hitting the wheel produces a weak velocity component in the wind behind it. Ideally, this airflow should not hit other parts of the car, so we try to move it as far outboard as possible. For example, we might use PIV to measure a wheel’s wake to see if changing the shape of the front wing endplate moves the wake in a positive or negative direction.” The front wing endplate helps generate downforce.

With low pressure under the wing and high pressure on top of it, air is forced from the top to the bottom. The endplate partially blocks this flow, thus boosting downforce.

PIV builds data files — basically just text and numbers — but they aren’t useful until engineers can see them, says Mike Peery, CEO of Tecplot Inc., Bellevue, Wash. “Engineers must understand the phenomena qualitatively — that is, what is in the flow field: Is it a vortex, a wake, a combination, and are they interacting or not? They also must understand things quantitatively. Tecplot 360 can, for instance, generate a profile across the PIV field to extract vorticity.”

Vorticity is local rotation of the fluid and is generally visible. For example, it’s sometimes possible to see vortices at the wing tips of airplanes when they fly through a cloud.

F1 aerodynamic studies typically use experimental data from PIV, computational results from a CFD package, and a CFD postprocessor such as Tecplot 360. Preprocessing involves CAD software (either as a stand-alone package or as geometry-building capabilities in a CFD package) to generate a geometry and mesh generator (in the CFD software) to get a computational grid of millions of finite volumes. The postprocessor’s job is to accurately approximate the governing differential equations — Navier-Stokes equations for the F1, with variations for compressible and incompressible flow — and generate an approximate solution.

Most commercial CFD packages contain their own postprocessors, says Peery. “But one advantages of being stand-alone is that the postprocessor can read in all kinds of data, whether it’s from Ansys, Fluent, CFX, or CD-adapco, or even experimental data.”

Toyota Motorsport engineers can compare, say, a contour of a vorticity magnitude in the cross plane from the PIV data to what is coming out of the CFD code. “Because they are both surfaces, the software can also interpolate the field data from one set, say the computational slice, into a PIV slice,” says Peery. “Thus, everything is on the same grid. The software computes the differences and then displays them, giving quantitative values for where the PIV and the CFD data differ and by how much. This, in turn, lets engineers build increasingly exact CFD models of the entire car.”

© 2011 Penton Media, Inc.

About the Author

Leslie Gordon

Leslie serves as Senior Editor - 5 years of service. M.S. Information Architecture and Knowledge Management, Kent State University. BA English, Cleveland State University.

Work Experience: Automation Operator, TRW Inc.; Associate Editor, American Machinist. Primary editor for CAD/CAM technology.

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