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Latest News
Can hydrogen be the transportation fuel in an otherwise nuclear economy?
Let’s face it: The global economy should be powered primarily by nuclear power. And it probably will by the end of this century, with a still-significant assist from renewables and hydro. Once nuclear systems are dominant, the costs come down to where gas is now; and when carbon emissions are reduced to a small portion of their present state, it will become obvious that most other sources are only good in niche settings. I mean, why use small modular reactors to load-follow when they can just produce that power instead of buffering it?
Tuomas Viitanen, Jaakko Leppänen
Nuclear Science and Engineering | Volume 177 | Number 1 | May 2014 | Pages 77-89
Technical Paper | doi.org/10.13182/NSE13-37
Articles are hosted by Taylor and Francis Online.
The target motion sampling (TMS) temperature treatment technique, previously known as “explicit treatment of target motion,” is a stochastic method for taking the effect of thermal motion on reaction rates into account on-the-fly during Monte Carlo neutron tracking. The method is based on sampling target velocities at each collision site and dealing with the collisions in the target-at-rest frame using cross sections below the actual temperature of the nuclide or, originally, 0 K. Previous results have shown that transport with the original implementation of the TMS method requires about two to four times more CPU time than conventional transport methods, depending on the case. In the present paper, it is observed that the overhead factor may increase even above 10 in cases involving burned fuel. To make the method more practical for everyday use, some optimization is required. This paper discusses a TMS optimization technique in which the temperatures of the basis cross sections are elevated above 0 K. Comparisons show that the TMS method is able to reproduce the NJOY-based reference results within statistical accuracy, both with and without the newly implemented optimization technique. In the specific test cases, the optimization saved 35% to 83% of the calculation time, depending on the case.