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Two new partnerships forged in AI and nuclear sectors
The nuclear space is full of companies eager to power new AI development. At the same time, many AI companies want to provide services to the nuclear industry. It should come as no surprise, then, that two new partnerships have recently been announced that further bridge the AI and nuclear sectors.
AtkinsRéalis has announced a partnership with Nvidia that aims to leverage Nvidia’s technologies to deploy “nuclear-powered, large-scale AI factories.” Centrus Energy has announced a partnership with Palantir Technologies to use Palantir’s software in support of Centrus’s plans to expand enrichment capacity.
Nicolas Crouzet, Paul J. Turinsky
Nuclear Science and Engineering | Volume 123 | Number 2 | June 1996 | Pages 206-214
Technical Paper | doi.org/10.13182/NSE96-A24183
Articles are hosted by Taylor and Francis Online.
In solving few-group neutron kinetic equations in multidimensions, one must select time step sizes as a function of time such that the temporal truncation error introduced by the discrete time derivative approximation is limited to ensure the desired fidelity. When using the Euler backward finite difference to approximate the first derivative of the flux—a popular approximation because it ensures numerical stability—the truncation error is know to be O(Δt2) and proportional to the second derivative. By employment of the double-time-step-size technique, modified to reduce the frequency that double-time-step-size solutions are required, an estimate of the second derivative can be obtained, leading to an efficient computational algorithm for determining the near-optimum time-step-size sequence to ensure the desired fidelity.