Editor's note: Scott Ahlman will be delivering a webinar on a related topic on January 28, 2013.
Free and open to all. Details/registration.
Over the past five years I've been part of 20 wins, numerous pole positions, and two second place finishes in championships. My studies at the SDM program as a Ford-sponsored fellow contributed significantly to my success. The program taught me to view complex challenges through technical, business, and socio-political lenses and to integrate these perspectives using systems thinking.
The complex system encompassing a high-performance race car, driver, and track involves literally thousands of parameters and variables — primarily car design and setup parameters, vehicle dynamic behavior variables and weather related variables including temperature, barometric pressure, track temperature, and resultant air density. These must be prioritized, analyzed, understood, and adjusted. The system also involves a team of engineers and mechanics.
While in SDM, I developed a hierarchical approach to combining tools and methods for evaluating complex, dynamic, system architectures. My thesis research was conducted in the context of a very high-performance passenger car.
My most recent work focused on chassis and vehicle dynamics. I led the development and use of predictive tools to determine the best chassis setup for a fast, drivable, and consistent race car. I also contributed to chassis development, including target setting, analysis, design, manufacture, and refinement on and off the track.
|Figure 1: This concept map of high-performance|
passenger car handling shows some of the many parameters
and subsystems that chassis engineers work with.
Race weekend is really, really fast. We've done extensive analysis beforehand, and created a short book of recommendations on chassis setup and prioritized adjustments, depending on various handling issues. Planning is essential. When a driver came in from a run and gave us feedback on the car and how it needs to behave differently, we had 15 to 30 seconds to make a judgment call. It was far better to be in predictive or refinement mode than to conduct analyses while the driver was on the track.
Adding to the challenge, there was very limited practice time at a specific track on race weekend — typically 1.5 hours of practice before qualifying and then two one-hour practices on the day before the race. We therefore used system engineering to predict the best race car setup for the different tracks and the infinite number of weather possibilities in a racing season. NASCAR rules preclude testing or practice at any track or facility that sanctions a NASCAR race, so our ability to predict the race car's behavior and make chassis setup recommendations was critical.
The challenges in understanding the variables and race conditions went beyond just car, driver, track layout and track surface. Here are a few of the other variables:
- Annual tire changes by Goodyear. We were provided lab test data for new tires that we used for analysis and modeling to understand how the changes would affect the car's behavior. However, we were only allowed to participate in Goodyear field tests on a limited basis — about five tracks we raced on a year. We had to be purely predictive for the remaining tracks.
- Constantly changing conditions. Ambient temperature, sun load on the track, tire rubber buildup on the track, tire wear, and changing fuel load add numerous levels of unpredictability.
- Limited data acquisition during races. Only driver feedback on the car's behavior is allowed. Therefore, the quality of his feedback and our ability to prompt him for information is crucial.
|Figure 2: This chart shows part of an optimization sheet.|
It helps engineers focus on the right things in the right
order and to complete difficult optimization of many
parameters and variables at once, as a system.
- Which metrics to use and when to use them, since vehicle dynamics models and analyses rarely output holistically accurate values for speed, balance, balance consistency, drivability and consistency.
- The strengths and weaknesses of various models, when to use which, and which could be relied upon at face value, and which require human judgment.
With a "tight" or under-steering race car, the front tires lose grip first, resulting in the car tending to continue straight on a path tangent to the curve when pushed to the limit. A "loose" or over-steering car is one in which the rear tires lose grip first, resulting in the car tending to spin out when pushed to the limit.
In many cases, the three major parts of a corner — entry, middle and exit — can all exhibit very different balance characteristics due to the nature of the part of the corner, the driver's style, the car's inherent behavior, track geometry changes (banking, lateral and normal curvature), and bumps in the track. Therefore, balance consistency throughout the corner is one of our most important — and challenging — criteria for chassis design and setup.
Working with NASCAR chassis was a great example of the need to define, then design. The numerous variables and tight deadlines left the process vulnerable to classic errors that arose in the absence of a systems perspective. Lack of clear goals that can be used to generate system, subsystem and component requirements led to a system that failed to meet the customer need. And the lack of clear goals and requirements led to what I call "engineering on walkabout" where one is on a design journey with no clear destination.
And then there were the human factors. Although the stated objective might be winning races and championships, there are sometimes hidden agendas. Often there were also conflicting goals, including focusing on productivity at the expense of standard goals definition and requirements cascades, i.e. how functional requirements "cascade" down a system hierarchy from system through subsystems to components.
Taking a systems perspective helped me recognize these non-technical, and often non-strategic, influences faster and better, assign them more credence, and cope with or even influence and shape them. This has been instrumental in helping "incentivize" my teammates in the desired direction.
A key part of my process in helping the team tackle this complex system was the use of basic systems engineering tools like chunking, aggregation, and hierarchy. In addition, Design of Experiments and many-parameter and variable optimization were also key parts of this process. They helped achieve high-performance results much more efficiently than standard component-focused and typical one-factor at-a-time strategies.
Ultimately, though, understanding what parameters and variables to focus on and when was critical and required significant judgment. In addition, team members needed to know the strengths and weaknesses of the tools and models they used.
This article shows only a few of the ways that SDM knowledge can be applied in a multi-variable environment. For a more in-depth discussion on, for example, testing concepts vs. track-specific setup; the use of systems engineering tools such as chunking, aggregation, and hierarchy; use of solution neutral requirements; the functional requirements cascade; and mind-mapping, please request an electronic copy of my thesis, Complex Dynamic System Architecture Evaluation Through A Hierarchical Synthesis Of Tools And Methods, from Joan S. Rubin, SDM Industry Co-director, email@example.com.
About the Author
Under his firm Ahlman Engineering, Scott Ahlman, SDM '01, was a chassis/vehicle dynamics engineer for Ford Racing in the NASCAR Sprint Cup Series since 2006. He holds an MS in Engineering and Management from MIT.