From 15 hours to one minute: How AI/ML is speeding up GM's development

But Anderson’s world recently moved into its third epoch, “which is where GM has really been pushing, which is a collapse of those functions into a single broadly informed, largely probabilistic method for design, development and manufacturing of these assets,” he explained. And yes, by probabilistic, he means AI/machine learning. Using simulation for engineering work like CFD—versus using physical models in a physical wind tunnel—sped up that work, but the complexities of simulation mean it’s very computationally demanding in terms of resources and time. But you can teach a computer how to virtualize that analysis and then run multiple virtualizations in parallel using AI/ML; last month, we reported on just such an example, when IBM and the race car manufacturer Dallara published research showing how the approach produces data that’s well-correlated enough to use. When you realize just how much faster these new tools are, it becomes extremely clear why GM is embracing them. “Our FEA runs that historically were 15 hours per run? They’re now one minute,” Anderson told me. “We’re not using virtual tools just to check our work after we’ve done vehicle design, but we’re actually giving our engineers a virtual environment where they can simultaneously optimize the hardware and the software and inform hardware design or software design or vehicle performance in a way that nobody in the industry is doing, especially at the scale and the speed of what we’re doing,” said Jason Fischer, executive director of virtual integration engineering at GM. “The beauty of these virtual tools is our collaboration with our motorsports team with NASCAR and Formula One,” Fischer continued. “We co-develop a lot of these tools together and then we independently develop tools depending on who’s got the strength and the bandwidth between the organizations to do that. And as one outpaces the other, we actually sit down and we have a monthly technology transfer between motorsports, and I’ll say the production side of things to ensure that we’re all seeing the latest and greatest technology and using the latest techniques.” One example Anderson and Fischer walked me through was using virtualization to perform a handling test for a vehicle in development, specifically Consumer Reports’ avoidance test, where a car has to swerve at speed to avoid an obstacle. Instead of connecting all the various subcomponents of a car’s electronics on a test bench to see if they talk to each other without errors, GM now models all the sensors, electronic control units, domain controllers, and so on. “We actually have IP protection on how we’ve set this system up at General Motors where we can put together the vehicle behavior from a physics perspective,” Fischer said. “So [we can now] run vehicle performance, electronic control units, and software simultaneously in this virtual environment, and we can really open up our design space exploration. This allows us to actually change physical parameters and run thousands of designs of experiments to see how the control logic handles that,” Fischer said.