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On moving fast and breaking things
Craig Piercycpiercy@ans.org
So much of what is happening in federal nuclear policy these days seems driven by a common approach popularized in the technology sector. Silicon Valley calls it “move fast and break things,” a phrase originally associated with Facebook’s early culture under Mark Zuckerberg. The idea emerged in the early 2000s as software companies discovered that rapid iteration, frequent experimentation, and a willingness to tolerate failure could dramatically accelerate innovation. This philosophy helped drive the growth of the social media, smartphones, cloud computing, and digital platforms that now underpin modern economic and social life.
Today, that mindset is also influencing federal nuclear policy. The Trump administration views accelerated nuclear deployment as part of a broader competition with China for technological and AI leadership. In that context, it seems willing to accept greater operational risk in pursuit of strategic advantage and long-term economic and security objectives.
Martin Frank, Jonas Kusch, Thomas Camminady, Cory D. Hauck
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 971-988
Technical Paper | doi.org/10.1080/00295639.2020.1730665
Articles are hosted by Taylor and Francis Online.
Solving the radiative transfer equation with the discrete ordinates (S) method leads to a nonphysical imprint of the chosen quadrature set on the solution. To mitigate these so-called ray effects, we propose a modification of the S method that we call artificial scattering S (as-S). The method adds an artificial forward-peaked scattering operator that generates angular diffusion to the solution and thereby mitigates ray effects. Similar to artificial viscosity for spatial discretizations, the additional term vanishes as the number of ordinates approaches infinity. Our method allows an efficient implementation of explicit and implicit time integration according to standard S solver technology. For two test cases, we demonstrate a significant reduction of error for the as-S method when compared to the standard S method, both for explicit and implicit computations. Furthermore, we show that a prescribed numerical precision can be reached with less memory due to the reduction in the number of ordinates.