solves nonlinear impact problems that involve large deformations with complex
contact and motion. Typical applications include crash, metal forming
processes, drop tests and ballistic scenarios. NEi Explicit can also be used to
solve static problems with millions of degrees of freedom. Because they are
extremely large and highly contact dominated, these problems are difficult to
solve using implicit FEA codes. It is completely
integrated with the NEi Nastran environment, making the learning process much
easier for Nastran users who are transitioning to an explicit solver. Existing
Nastran users can directly analyze a Nastran implicit model in the NEi Explicit
solver without any changes to the analysis file. The explicit architecture
lends itself to highly scalable parallel performance, and large deformation
contact solutions with highly nonlinear material behavior. NEi Explicit
provides analysts with automatic contact generation, rigid materials
definition, material deletion criteria with element deletion, and automatic
reconstruction of contact surface due to surface erosion.
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.