C A S E
S T U D Y
Company
Swift Engineering, Inc.
San Clemente, CA
www.swiftengineering.com
Using HPC to Up the Formula
Racing Game
Swift engineers use CFD passing maneuver to improve aerodynamics
Overview
Swift was founded in 1983 as a designer
and manufacturer of world class open
wheel race cars. The company has
produced a wide range of cars from
amateur category racing cars, like its
Formula Ford, to professional, world
class racing cars like its Formula Nippon
chassis. In total, Swift has produced over
500 cars and won over 40 national and
international racing championships.
Their customers
Automotive companies, aerospace prime
contractors and anyone else seeking high
performance products and services
The challenge and the application
Conduct an unsteady state simulation with
moving mesh and rotating boundary conditions using Metacomp Technologies CFD++
code and a Cray XE6™ supercomputer
“This is the first time [an unsteady
state simulation] has been done
on a passing maneuver in a racing
situation. Cray’s part was huge in
support. ”
— John Winkler
Chief Aerodynamicist, Swift Engineering
Swift Engineering designs and builds Formula racecars capable of 200 miles per hour and 5
G cornering – cars that have won dozens of racing championships. But the way Swift figured
it, they could still do more.
“We wanted to promote the excitement in racing [for the spectator],” says John Winkler,
Swift’s chief aerodynamicist. “And to do that, we realized we needed to improve the driver’s
ability to pass.”
Overtaking another vehicle in Formula racing is difficult by nature. Drivers are not only trying
to pass on a twisting track at 100+ miles per hour, but today’s Formula car performance is so
fine tuned that, in terms of the technology, drivers are closely matched. Add the aerodynamic
effects of two high-performance race cars in close proximity to each other and passing gets
even tougher.
The bottom line? Passing is fun to watch, but it doesn’t happen very often. So Swift figured
if they could alter the aerodynamic design of the race cars to make it easier for the trailing
vehicle to pass they could revolutionize the spectator experience. “We felt this was a unique
way of attacking the problem,” says Winkler.
Swift had its problem, now they needed a solution.
Swift’s computational strategy
In a racing situation, the wake of the lead car has a profound effect on the performance of
the trailing car. For example, as a trailing car moves up to pass, the wake causes it to lose
some of its downforce – the aerodynamic force that helps push the car’s tires onto the track.
Consequently, the driver has less grip on the road. Less grip means less control and fewer
opportunities for thrill-inducing lead changes.
A Swift engineer had already come up with a device called a “mushroom buster” designed
to “wake shape” or modify the wake signature of the vehicles and make it easier to execute
lead changes. However, while the effects of a lead car’s wake are understood in principle,
the details of this dynamic interaction are not. In order to evaluate their solution, Swift
needed to understand the aerodynamic effects throughout a complete simulated pass
of one racecar by another racecar.
From a computational perspective, simulating a typical passing maneuver is extremely
difficult because the aerodynamics change dramatically as the cars change position
relative to each other. In the computational fluid dynamics (CFD) field it’s an unsteady
state problem – a computationally intensive one that involves simulating a complex
time-dependent aerodynamic flowfield. In other words, solving an unsteady state
problem means simulating a moving object in relation to another throughout an
entire maneuver.
Typically, a problem like this one would be run as a steady state one in CFD –
fixing one car relative to the other and getting a time-average solution of the
two cars at that instant of time. Swift had done plenty of those steady state
simulations. But that’s not how a real race works. “We wanted to look at
how the aerodynamics change in an actual maneuver – how fast the car
responds, the downforce,” says Winkler.
Cray XE6 System Overview
Two 12--core AMD Opteron™
processors per node
32GB RAM per node
1.33GB RAM per core
Gemini interconnect
Lustre file system
“Swift has simulated combat on
the racetrack beyond the confines of a wind tunnel. Using our
Cray XE6 system, we simulated
our ‘wake shaping concept’ in
an aggressive passing maneuver. We now have definitive
results that can be applied to the
next generation Indy Lights car.
This can increase passing opportunities for even more racing
excitement.”
First of all, Swift needed compute power. So they turned to Cray. “We have
a Cray CX1000™ and Cray CX1™, but because of the size of the problem, the
Cray XE6 system was going to be a better solution in terms of scalability and
performance,” says Winkler.
Using the Cray XE6 system and CFD, they were able to break a 13-second passing
maneuver down into a series of steps. As Winkler describes it, when you’re running
an unsteady state solution in CFD, you’re solving the equation at each time step. “At
every 10 milliseconds, you’re making a solution. For a 13-second maneuver, that’s
1,300 time steps.” What that looks like in CFD is essentially 1,300 different grids. Not
as easy as it sounds. Setting up that kind of CFD run means essentially creating grids
upon grids composed of millions of cells. Ultimately, Swift created a grid composed of
118 million cells. That’s a lot.
“You have a different grid for each time step,” says Winkler. “It’s one of the reasons
it makes it difficult to solve. It limits the scalability on a supercomputer as it’s a very
numerically intense solution.”
—Mark Page
Chief Scientist, Swift Engineering
WAKE SHAPING: CFD++ simulation of passing maneuver with unsteady flow, moving mesh and rotating tires.
(Images courtesy of Swift Engineering, Inc.)
With Cray support and using 384 cores on a Cray XE6 system, Swift simulated an unsteady
state passing maneuver with 1,350 unsteady iterations in 350 hours of compute time. In doing
so, they solved a first-of-its-kind unsteady problem in a racing situation. “Moving one object
in relation to another has been done before,” says Winkler. “But we believe this is the first
time this has been done on a passing maneuver in a racing situation. Cray’s part was huge in
support. With the size of the problem, we couldn’t have done it without shrinking [the model
size] down somewhat. We didn’t use a ton of cores but we found a nice performance point
of cores and memory.”
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Just to add to their list of firsts, Swift tossed in an extra complexity. When simulating an
open-wheel race car like the Formula ones, it’s crucial to model the fact that the tires
are spinning. “We’d never asked to move a car and also have this rotating boundary
condition,” says Winkler. Not only had they never asked the question, accounting for
spinning tires wasn’t an existing capability in the CFD code. So Swift partnered with
Metacomp to add new features to their CFD++ code.
While Swift may have started with passing, given the leading-edge nature of its
simulation work they – in a sense – ended with simply surpassing.