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. |
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Pointwise, Inc. is solving the top problem facing computational fluid dynamics (CFD) today – reliably generating high-fidelity meshes. The company's Pointwise software generates structured, unstructured, overset and hybrid meshes; interfaces with CFD solvers such as ANSYS FLUENT®, STAR-CCM+®, OpenFOAM®, and SU2 as well as many neutral formats, such as CGNS; runs on Windows, Linux, and Mac, and has a scripting language, Glyph, that can automate CFD meshing. Manufacturing firms and research organizations worldwide have relied on Pointwise as their complete CFD preprocessing solution since 1994.
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