The reason is our ability to predict turbulence. Some simulation software has gotten close. But to date we can only predict tested conditions. The facts behind turbulence are still largely guessed and even after a good bit of aviation history we are still working on the kinks. I have been to several meetings with mathematicians that are leaders in this field. It's difficult for them to predict with any great accuracy. Yes 10000% error is outrageous but it's possible in a field we are infants on.
That's a good question, Ann. The fact that it travelled successfully for three minutes might indicate that the shock wave was a sudden anomaly shortly before it failed (I can't imgine any design standing up to 100X loads for three minutes). Still, it's hard to imagine why no one foresaw a shockwave of this magnitude.
I guess what's not clear to me is, why was the aircraft designed to withstand shockwaves 100 times LESS strong than it actually experienced? I'm especially surprised since this was apparently the second flight, not the first. Why didn't engineers do a better job of prediction?
I recall several publications and reporters reveling in the "failure" of the HTV-2 test back in August. But the ability to withstand forces 100x greater than design specifications and still manage to deploy a controlled abort should be a success in everybody's metrics. Controlled flight at Mach 20 for 3 minutes should have provided a wealth of telemetry. And these are the unclassified tests.... exciting.
In a bid to boost the viability of lithium-based electric car batteries, a team at Lawrence Berkeley National Laboratory has developed a chemistry that could possibly double an EV’s driving range while cutting its battery cost in half.
Using Siemens NX software, a team of engineering students from the University of Michigan built an electric vehicle and raced in the 2013 Bridgestone World Solar Challenge. One of those students blogged for Design News throughout the race.
Robots that walk have come a long way from simple barebones walking machines or pairs of legs without an upper body and head. Much of the research these days focuses on making more humanoid robots. But they are not all created equal.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.