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This division promotes the development and timely introduction of fusion energy as a sustainable energy source with favorable economic, environmental, and safety attributes. The division cooperates with other organizations on common issues of multidisciplinary fusion science and technology, conducts professional meetings, and disseminates technical information in support of these goals. Members focus on the assessment and resolution of critical developmental issues for practical fusion energy applications.
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The blossoming of cooperation between the U.S. and Canada
The United States and Canadian nuclear industries used to be an example of how two independent teams of engineers facing an identical problem—making electricity from uranium—could come up with completely different answers. In the 1950s, Canada began designing a reactor with tubes, heavy water, and natural uranium, while in the U.S. it was big pots of light water and enriched uranium.
But 80 years later, there is a remarkable convergence. The North American push for a new generation of nuclear reactors, mostly small modular reactors (SMRs), is becoming binational, with U.S. and Canadian companies seeking markets and regulatory certification on both sides of the border and in many cases sourcing key components in the other country.
François Bachoc, Guillaume Bois, Josselin Garnier, Jean-Marc Martinez
Nuclear Science and Engineering | Volume 176 | Number 1 | January 2014 | Pages 81-97
Technical Paper | doi.org/10.13182/NSE12-55
Articles are hosted by Taylor and Francis Online.
This paper addresses the use of experimental data for calibrating a computer model and improving its predictions of the underlying physical system. A global statistical approach is proposed in which the bias between the computer model and the physical system is modeled as a realization of a Gaussian process. The application of classical statistical inference to this statistical model yields a rigorous method for calibrating the computer model and for adding to its predictions a statistical correction based on experimental data. This statistical correction can substantially improve the calibrated computer model for predicting the physical system on new experimental conditions. Furthermore, a quantification of the uncertainty of this prediction is provided. Physical expertise on the calibration parameters can also be taken into account in a Bayesian framework. Finally, the method is applied to the thermal-hydraulic code FLICA 4, in a single-phase friction model framework. It allows significant improvement of the predictions of FLICA 4.