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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.
12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)
Technical Session
Wednesday, June 16, 2021|12:00–1:45PM EDT
Session Chair:
Graeme M. West
Alternate Chair:
Jamie B. Coble
Session Organizer:
Staff Producer:
Ashley Jiminian (ANS)
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Automated Image Stitching of Down Channel Nuclear Reactor Fuel Channel Inspection Footage
Michael Devereux (Univ. of Strathclyde), Paul Murray (Univ. of Strathclyde), Graeme West (Univ. of Strathclyde)
Paper
Attachment — Bio
An Innovative Crack Detection Algorithm to Support Automated Inspection of Nuclear Reactor Cores
Efstathios Branikas (Univ. of Strathclyde), Paul Murray (Univ. of Strathclyde), Graeme West (Univ. of Strathclyde)
Machine Learning of Flash Thermography Images for Detection of Early Stage Fatigue in Martensitic Steel
Xin Zhang (Illinois Institute of Technology), Jafar Saniie (Illinois Institute of Technology), Thiago Seuaciuc-Osorio (Electric Power Research Institute), Sasan Bakhtiari (ANL), Alexander Heifetz (ANL)
Gaussian Process Ensemble for Corrosion Modeling and Prediction in Molten Salt Reactors
Elizabeth Sooby (Univ. of Texas San Antonio), Miltiadis Alamaniotis (Univ. of Texas at San Antonio), Alexander Heifetz (ANL)
Automated Generation of Training Dataset for Crack Detection in Nuclear Power Plant Components
Zhouxiang Fei (Univ. of Strathclyde), Graeme M. West (Univ. of Strathclyde), Paul Murray (Univ. of Strathclyde), Gordon Dobie (Univ. of Strathclyde)
Attachment — Updated version of the paper
Attachment — Biography
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