Shaving Seconds from an Olympic Lid

Reverse engineering and CFD analysis help the British Cycling Team sprint to Olympic medals. 

Tenths of a second can make the difference between a gold medal and ending up out of the running in Olympic track cycling.

In the months leading up to the 2004 Summer Olympic Games in Athens, Greece, the British Cycling Team had been shaving those precious fractions of a second using CFD (computational fluid dynamics) studies conducted by the Sports Engineering Research Group (SERG) at the University of Sheffield. SERG combined CAD, 3D scanning, reverse engineering, CFD analyses, and cutting-edge visualization to streamline the overall aerodynamics of the handlebars and front fork/wheel combinations of the team’s cycles. The team thought everything was dialed in. Then, just weeks before the games were to begin, cycling’s international governing body changed the rules for helmets, sending the team back to the drawing board.

The new rule required that only helmets passing a formal safety test in an accredited laboratory could be used in Olympic competition. Since some of the helmets the team planned on using did not fit those specifications because they were designed primarily for speed rather than protection, the team decided to test helmet designs for aerodynamic efficiency from among those that passed the safety regulations. Four helmets were selected for testing from manufacturers to which the team had access.

› ›  The Sports Engineering Research Group at the University of Sheffield combined 3D scanning, reverse engineering, CFD, and visualization to study helmet designs for the UK Olympic cycling team. The pathlines here show airflow, and the color maps depict contours of total pressure distribution.

The team turned again to SERG’s Dr. John Hart for a last-minute CFD analysis. Hart ruled out modeling from scratch with CAD as there was not enough time and CAD is not well-suited to creating the organic shapes required for accurate modeling and CFD analysis of the helmets and athletes.

Instead, Hart decided to capture athlete and helmet geometry with 3D Scanners’ ModelMaker X70 noncontact 3D laser. The scanner, fitted to a FaroArm, would capture the shapes, then Hart could merge the scans and create a NURBS model of the data in Geomagic Studio.

“CAD engineers work at different tolerances than those required for CFD analysis,” Hart says. “Even if we had the CAD files for the helmets, we would have had to spend a great deal of time cleaning up the model to make it watertight. Reverse engineering the helmets and surfacing them in Geomagic Studio guaranteed a highly detailed, watertight model in less time.”

Scanning the helmets was relatively straightforward. Each helmet took approximately 25 minutes to scan depending on the complexity of the design. The FaroArm was moved around the object, capturing point-cloud data and depth information.

SERG planned to capture data from the athletes by scanning them in different racing positions: one aerodynamic posture with the cyclist looking ahead and one where the head is down in a sprint to test more fully the effect of the various helmet shapes. Because of the time crunch, however, Hart did not have access to a cyclist; he had to scan a colleague for the human geometry. The subject was scanned over the course of two hours, allowing for rest breaks during scanning. To help eliminate issues arising from sudden movements during the process, completed scans were broken into sections that followed closely in succession—upper arm, lower arm, hand.

‹ ‹  Point-cloud data collected from scans was imported into Geomagic Studio to generate models for accurate analysis. Geomagic Studio automatically aligned the data.

  Refining Complex Scan Data

Point-cloud data collected from the scans of the four different helmets was imported into Geomagic Studio, a reverse-engineering solution for generating models for accurate CFD analysis and custom manufacturing. Geomagic Studio automatically aligned the scan data and a polygon mesh was applied. The model was cleaned to remove holes and defects, and patches were placed over the polygons, outlining the positions of the NURBS surfaces.  

Hart handled scan data from the human subject in much the same way, except additional work had to be done to reduce noise and align the data due to subtle movements from Hart’s colleague as he was being scanned. Hart used Geomagic Studio’s noise-reduction feature, as well as editing and filter tools, to refine the human model. He then used the software’s polygon geometric reconstruction functions to fill in missing data such as body hair and eyebrows that weren’t captured due to laser scatter.

› ›  Geomagic Studio applied a polygon mesh, and the model was cleaned to remove defects. Patches were then placed over the polygons, outlining the positions of the NURBS surfaces.

“Geomagic Studio’s editing tools and ability to handle large, complex data sets made it a great match for this project,” says Hart. “We used the tools to refine scan data around the ears and in tight gaps, which enabled us to maintain a high degree of geometric realism on such a challenging human scan with nearly six million raw data points.”
He applied polygons and NURBS patches to the human model and used Geomagic Studio to output a STEP file.

“The STEP file format provides a robust geometric file that’s not too large,” Hart says. “We can end up with a model with a large number of NURBS patches in order to capture the detail we need. The accuracy of the CFD study was highly dependent upon the geometrical accuracy of the assembled model.”

‹ ‹  The subject was scanned over the course of two hours. Completed scans were broken into sections to help eliminate issues arising from sudden movements during the process.  

 

The STEP file containing each helmet design and the human geometry was meshed for CFD analysis in Fluent Gambit, where Hart also generated a flow domain around each model. The meshes ranged from two to seven million cells, depending on the geometry. Wherever possible, prism cells were generated over the surface geometry to capture boundary-layer flow features in detail. CFD analysis, which incorporated data on boundary and physical conditions acquired from a previous British Cycling project, was conducted using Fluent.

‹ ‹ STEP files containing each helmet design and the human geometry were meshed for CFD analysis in Fluent Gambit. The meshes ranged from two to seven million cells, depending on the geometry.  

 

The SERG team imported CFD results into CEI’s EnSight software, which produced highly detailed flow visualizations showing the aerodynamic properties of the helmets. SERG chose to concentrate on the drag and lift forces in the simulations, using isosurfaces to show wake structures and particle streamlines to visualize swirling and recirculating flow paths.

Based on the wake structures and recirculating flows in the visualizations, SERG was able to quickly identify how different geometric components of the models (i.e., helmet and cyclist) interacted and influenced each other. They were also able to pinpoint large wakes that resulted in high drag forces.

Hart colored different aspects of the model within EnSight, and applied properties such as reflective surfaces for the bike and helmet and matte surfaces for fabric and skin. He also applied lighting effects to the model to complete the lifelike look, which, says Hart, makes the results more believable. “The flow visualizations and images were vital in presenting the physics-based simulation results in an understandable manner to the cycling team,” Hart says. “Being able to clearly show a client what is happening is essential to their understanding of the results.”

Once scans were combined and patched, Fluent and EnSight were used to show airflow speed in the form of pathlines around the helmet and the cyclist’s head.

The optimized bike design and later helmet recommendations from SERG have been credited with helping contribute to the team’s best-ever Olympic medal haul. The team won two gold medals, a silver, and a bronze in Athens, but they’re not done. SERG is once again working with the team in preparation for the 2008 Beijing Summer Olympics.

Erin Hatfield is a freelance writer who covers the computer graphics, IT, and electronics industries. Send e-mail about this article by clicking here. Please reference “Shaving Seconds, May 2006” in your message.

EnSight
Computational Engineering International—CEI

FaroArm
Faro Technologies

Fluent, Gambit
Fluent, Inc.

Geomagic Studio
Geomagic

ModelMaker X70 Laser Scanner
3D Scanners UK Ltd.

Sports Engineering Research Group
University of Sheffield