Well, if there isn't yet a database compiling all of that data, there needs to be, hence why it makes sense that predictive modeling and simulation eat up a sizeable chunk of the funding. Given how easy it is to slant performance metrics and position claims, there needs to be some trusted record of data to draw on so engineers in these fields, using these new materials, can make the best, most informed design choices.
The one thing I already worry about with small city cars is, what about all those huge trucks and buses they could run into? The safety standards would have to protect against low-speed crashes with huge vehicles, as well as with other small cars.
Beth, I also noticed the emphasis on predictive modeling. The announcement (link given in the article) is quite brief and doesn't give any more details. Having covered this subject before a little, I suspect it might be aimed at discovering which materials perform best, according to certain specs, for which specific applications, meaning, in different components of the car. I would guess that those specs would combine the required material performance characteristics (toughness, strength, impact resistance, chemical resistance, etc.) of that component with weight saved. To date, AFAIK there's no such automotive materials database, at least for composites or for composites vs metals, only many different manufacturers' claims and specs and tests. If anyone knows any different, please chime in.
This doesn't seem like much money for R&D, especially since it is spread out of a number of years. This may simply be the most the White House could put together without congressional approval. The load for the company in Dearborn woud have been massive. But in our current political climate, I can see why it didn't get through.
@TJ: That's an interesting idea. But, would it work in the US given that our car culture is centered on independence and mobility. Could another possibility be to beta test newer technologies in public transportation or partner with delivery companies, such as UPS or FedEx?
I haven't been following this issue closely. Why is it interesting that the DOE's announcement came the day after the American Iron and Steel Institute released its industry profile?
I think one way to help with this goal would be a new safety class, for light weight vehicles intended for city use, at lower speeds. The vehicle can be lighter, the safety equipment reduced. Lighter weight, lower cost, better fuel efficiency, all without the need for new materials (though such materials would also help with this class).
Maybe it's the lens in which I look at these things given my beat area for Design News, but it stuck me as interesting that predictive modeling and simulation endeavors are factoring so prominently into DOE funding. Now predictive modeling is different than the simulation (CAE) stuff we talk about here quite a bit. Any intel on what role exactly the predictive models are going to play in the work being done to advance lightweighting and new materials?
Truchard will be presented the award at the 2014 Golden Mousetrap Awards ceremony during the co-located events Pacific Design & Manufacturing, MD&M West, WestPack, PLASTEC West, Electronics West, ATX West, and AeroCon.
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.
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.