Building Bloodhound: The Fastest Car in the World

Jets, rockets, and computer models will help a British team break 1,600 km/h

10 min read
Building Bloodhound: The Fastest Car in the World
Photo: Stefan Marjoram

In a suburb of Bristol, in western England, not far from the Welsh border, a band of engineers are building a machine that they hope will make the biggest jump in the century-long history of the official world land-speed record, taking it from a smidgen above the speed of sound to 1,600 kilometers (1,000 miles) per hour. That's roughly the cruising speed of a fighter aircraft, but it's considerably harder to achieve at ground level, where the atmosphere is far thicker. And there's the not-insignificant danger that the vehicle will end up plowing into the ground.

Even in the complicated business of breaking such speed records, Bloodhound represents a remarkable array of firsts in terms of technology, engineering techniques, and propulsion systems, all set to send the missile-shaped car down a nearly 20-km-long racetrack in the South African desert toward the end of this year. Perhaps the most striking of those firsts is the project's method of verifying the safety of the design. To a degree that would be unthinkable today, earlier record attempts relied on overengineering, best-guess estimates, intuition, and sheer luck. Earlier generations of engineers would often discover a car's limits with destructive testing—running it until it broke. Now modeling and data acquisition, the preferred tools for designing both aircraft and cars, are making headway in this most extreme of sports. Bloodhound is the first project of its kind to apply them. By the time the car makes its great bid for the record in South Africa, it will have done the run 1,000 times in silico.

Keep reading... Show less

Stay ahead of the latest trends in technology. Become an IEEE member.

This article is for IEEE members only. Join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum’s articles, podcasts, and special reports. Learn more →

Membership includes:

  • Get unlimited access to IEEE Spectrum content
  • Follow your favorite topics to create a personalized feed of IEEE Spectrum content
  • Save Spectrum articles to read later
  • Network with other technology professionals
  • Establish a professional profile
  • Create a group to share and collaborate on projects
  • Discover IEEE events and activities
  • Join and participate in discussions

Inventor of the First AI System That Could Read Handwriting Dies at 72

IEEE also mourns the loss of other members

3 min read

A photo of a man in a dark jacket in glasses.  University at Buffalo

Sargur “Hari” Srihari

Keep Reading ↓ Show less

Fundamental Energy Transitions Can Take a Century

Electricity’s benefits were obvious, but it still took a lifetime to dominate

3 min read
A photo of Thomas Alva Edison standing in front of a dynamo.
Thomas Alva Edison poses with his dynamo, which he used to generate electricity for lighting.
Oxford Science Archive/Print Collector/Getty Images

One hundred and forty years ago, Thomas Edison began generating electricity at two small coal-fired stations, one in London (Holborn Viaduct), the other in New York City (Pearl Street Station). Yet although electricity was clearly the next big thing, it took more than a lifetime to reach most people. Even now, not all parts of the world have easy access to it. Count this slow rollout as one more reminder that fundamental systemic transitions are protracted affairs.

Such transitions tend to follow an S-shaped curve: Growth rates shift from slow to fast, then back to slow again. I will demonstrate this by looking at a few key developments in electricity generation and residential consumption in the United States, which has reliable statistics for all but the earliest two decades of the electric period.

Keep Reading ↓ Show less

Bridge the Gaps in Your ADAS Test Strategy

Full-scene emulation in the lab is key to developing robust radar sensors and algorithms needed to realize ADAS capabilities

1 min read
Keysight
Keysight

Achieving the next level in vehicle autonomy demands robust algorithms trained to interpret radar reflections from automotive radar sensors. Overcome the gaps between software simulation and roadway testing to train the ADAS / AV algorithms with real-world conditions. Sharpen your ADAS' radar vision with full-scene emulation that allows you to lab test complex real-world scenario, while emulating up to 512 objects at distances as close as 1.5 meters.

Get this free whitepaper now!