The complex system encompassing a high-performance race car, its driver, and the track involves thousands of parameters and variables that affect a car’s performance, drivability, balance, and tire life. To complicate matters, conditions are constantly changing, and data acquisition is limited during races. Choosing the right metrics at the right time is critical because vehicle dynamics models and analyses rarely output holistically accurate values for speed, balance, and drivability. Success on the track depends on weighing these variables and, for example, making split-second rate recommendations for springs, damping front and rear roll, as well as suspension alignment, kinematics, and tire pressure.
To determine when to use which metrics and which models, so that variables can be weighed effectively and appropriate choices made.
Ahlman’s team used Design of Experiments, many-parameter and variable optimization, and basic systems engineering tools like chunking, aggregation, and hierarchy. The team also used standard goals definition and requirements cascades that show functional requirements cascade down a system hierarchy from system through subsystems to components.
|Figure 1: This concept map of high-performance passenger car handling|
shows some of the many parameters and subsystems
chassis engineers work with.
A systems perspective is important for recognizing non-technical and non-strategic influences like human behavior, assigning these influences more credence, and coping with or even shaping, the influences.
Clear goals + specific system, subsystem, and component requirements = successful design journey and clear destination.
|Figure 2: This chart shows part of an optimization sheet. It helps engineers to focus|
on the right things in the right order and to complete difficult optimization
of many parameters and variables at once, as a system.
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