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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.
H. Park
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 952-970
Technical Paper | doi.org/10.1080/00295639.2020.1769390
Articles are hosted by Taylor and Francis Online.
Recent development of the high-order, low-order (HOLO) method has shown promising results for solving thermal radiative transfer problems. The HOLO algorithm is a moment-based acceleration, similar to the well-known nonlinear diffusion acceleration and coarse-mesh finite difference methods. In this work, we introduce a new spatial-differencing scheme for the low-order (LO) system based on the corner-balance method and analyze an asymptotic diffusion property for a one-dimensional gray equation. An asymptotic analysis indicates that the new spatial-differencing scheme possesses the equilibrium diffusion limit. Numerical examples demonstrate significant improvements in the solution accuracy compared to the LO finite-volume discretization with a discontinuous source reconstruction.