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DOE launches UPRISE to boost nuclear capacity
The Department of Energy’s Office of Nuclear Energy has launched a new initiative to meet the government’s goal of increasing U.S. nuclear energy capacity by boosting the power output of existing nuclear reactors through uprates and restarts and by completing stalled reactor projects.
UPRISE, the Utility Power Reactor Incremental Scaling Effort, managed by Idaho National Laboratory, is to “deliver immediate results that will accelerate nuclear power growth and foster innovation to address the nation’s urgent energy needs,” DOE-NE said in its announcement.
Aya Diab, Michael Corradini
Nuclear Science and Engineering | Volume 165 | Number 2 | June 2010 | Pages 180-199
Technical Paper | doi.org/10.13182/NSE08-18
Articles are hosted by Taylor and Francis Online.
Two-dimensional (2-D) experiments have been conducted to study the phenomenon of liquid entrainment associated with interfacial hydrodynamic instabilities, in particular, the Rayleigh-Taylor instability (RTI). The current work is part of an effort to understand the phenomenon of RTI associated with the rapid expansion of a superheated steam bubble that may occur in a CANDU reactor. The goal of the present work is to quantify the entrainment phenomenon associated with the RTI pertinent to the growth of a 2-D air bubble expanding adiabatically against a 2-D pool of water for a range of operating pressures. This experimental work is similar to that undertaken three decades ago at Massachusetts Institute of Technology, but the geometry has been modified to decrease the blowdown chute volume in order to reduce the experimental uncertainties. The entrainment phenomenon is characterized by means of two parameters that can be used to verify a semiempirical model developed in a parallel modeling effort. Specifically, the first parameter quantifies the width of the mixing zone, and the second parameter quantifies the volumetric ratio between the entrained liquid and the mixing zone. Comparing the experimental data with the model predictions is used to validate the developed model.