Autonomous vehicles – or driverless cars – are expected to become a viable form of transportation within the first half of this century.
Proponents believe these vehicles can help create a safer driving environment. The National Highway Traffic Safety Administration (NHTSA) estimates that 94 percent of serious car accidents are caused by human error1. Considering that more than 35,000 people in the United States were killed in automobile accidents in 2015, the potential for saving lives is tremendous. Less important, but still significant, the NHTSA puts the economic cost of these accidents in the hundreds of billions of dollars each year.
There are many issues to address and hurdles to clear; however, before safe driverless cars can be safely and widely used.
“Self-driving cars are the natural extension of active safety and obviously something we should do.”
One of the first challenges will be to provide the Artificial Intelligence (AI) needed to teach autonomous cars how to respond safely to aerodynamic conditions encountered on real-world roads, which is something human drivers have been responsible for up until now. To do this, engineers must thoroughly understand how road conditions and variables such as the number, type, and distance of other vehicles on the road affect the aerodynamics surrounding a driverless car.
“The concept is to teach these vehicles to position themselves on the road in areas with less drag for optimal fuel efficiency, travel time, stability and ultimately for maximum safety,” says Khalid Khalil2. Khalil recently completed the automotive industry’s first numerical study of aerodynamic conditions that surround a vehicle on the road. The study formed the basis of Khalil’s MSc thesis in Computational Fluid Dynamics (CFD) at Cranfield University in the United Kingdom (UK) and established a baseline for future numerical studies.
“The idea is to inject real-life conditions into the CFD simulation software in order to simulate the conditions on an actual motorway,” says Khalil. “Which makes it a complex simulation with many moving parts.”
Working with his professors and advisors, Khalil began by gathering data about road conditions on the UK’s M1 roadway such as traffic patterns, accidents, winds, and speed. He then used Pointwise to create individual high-fidelity meshes for the subject car and a variety of vehicles. After inputting the road conditions and running simulations on the individual aerodynamics of the main vehicle, he joined all the meshes together using Pointwise’s multiple block feature for the final study of how the moving parts interacted together.
“We knew it would require a great deal of time and there was no room for error, so we ran an initial study using a coarse grid level,” says Khalil. “The test took about 19 hours to run on 64 high-performance cores, and everything worked quite well. The final study was run on a medium grid, took about 48 hours, and yielded some very accurate results. I don’t think we would have been able to do it that quickly without Pointwise.”
Pointwise uses a multiple block approach simplifying complex CFD studies into modular grids that can easily be linked together. This approach gives users full control of the process, from the smallest element to the largest, and enables them to troubleshoot and improve grid quality early in the process. It also saves time and increases accuracy by allowing the reuse of grids in different configurations for additional CFD studies.
“For the motorway study, I did not have to re-mesh the car. I did not have to re-extrude viscous layers or re-mesh the volume,“ says Khalil. “It saves a remarkable amount of time and ensures that the results are exactly the same because we’re using the exact same data and mesh. Pointwise’s T-Rex feature was also critical to accurately capturing the boundary layer flow, which is especially important for turbulence studies.”
This is just the beginning for researchers. The data and results from Khalil’s numerical study will be used by future researchers to refine the automotive industry’s understanding of how changing aerodynamic conditions affect vehicles on the road.
“I am really excited about a safer future with driverless cars,” says Khalil. “There is still so much to do, but it is important that we have taken the first step.”
For more information about how engineers and designers in the automotive industry use Pointwise to make faster, more efficient vehicles, visit Pointwise Delivers Fast, Accurate Results for Garry Rogers Motorsport.
Cranfield University has been a Pointwise customer for over twenty years and was Pointwise’s first customer in the United Kingdom
As the UK’s only exclusively postgraduate university, with world-class expertise, large-scale facilities, and unrivalled industry partnerships Cranfield is helping to create leaders in technology and management globally. The university is located about 80 km (50 miles) northwest of London near the town of Milton Keynes.
There are many hurdles to clear before driverless cars can be used safely by the general public. One challenge is to provide the artificial intelligence needed to teach driverless cars how to respond safely to changing conditions on the road.
To do this, engineers must thoroughly understand how road conditions and variables such as the number, type, and distance of other vehicles on the road affect the aerodynamics surrounding a driverless car.
It is estimated that 94 percent of all serious car crashes in the United States are caused by driver error. Teaching these driverless cars to position themselves on the road in areas with less drag will help increase fuel efficiency, travel time, and stability. Ultimately, it should lead to fewer serious accidents.
Watch and download the presentation given by Steve Karman, Pointwise, Inc. at the Pointwise User Group Meeting about how Pointwise used custom Glyph scripts to automatically generate high-quality unstructured meshes for Engineering Sketch Pad (ESP) geometries, saving time and freeing users from repetitive and tedious tasks.
In this video, we demonstrate a couple of different strategies users can leverage in Pointwise to limit changes to only those localized regions in their computational domain that have been modified from one configuration to the next helping them spend more time focusing on the results of their parametric studies.
The Stanford Solar Car Project team developed a repeatable, simulation driven design framework consisting of Pointwise for rapid hybrid grid generation, SU2 to run the CFD simulations, and Tecplot 360 EX to post-process and interpret the results.