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Fusion energy: Progress, partnerships, and the path to deployment
Over the past decade, fusion energy has moved decisively from scientific aspiration toward a credible pathway to a new energy technology. Thanks to long-term federal support, we have significantly advanced our fundamental understanding of plasma physics—the behavior of the superheated gases at the heart of fusion devices. This knowledge will enable the creation and control of fusion fuel under conditions required for future power plants. Our progress is exemplified by breakthroughs at the National Ignition Facility and the Joint European Torus.
J. T. Fisher, J. W. Leachman
Fusion Science and Technology | Volume 68 | Number 2 | September 2015 | Pages 388-391
Technical Paper | Proceedings of TOFE-2014 | doi.org/10.13182/FST14-970
Articles are hosted by Taylor and Francis Online.
Flow and heat transfer measurements of solid hydrogenic materials inside twin screw extruders are not available. Fusion tokamaks like ITER require fuel pellet injection at 99.9% reliability which requires validated twin screw extruder throughput models for operation. The throughput of an extruder is limited by the amount of leakage flow through clearance gaps which depends on flow properties that vary strongly with temperature for hydrogenic materials. A Diagnostic Twin Screw Extruder (DTSE) has been built to measure azimuthal and axial temperature distributions as well as torque, cooling power, and screw speed for H2, D2, and Ne extrusions. In this paper the experimental procedure for the DTSE is described and azimuthal temperature measurements at three locations along the screws are discussed. The results show variations in temperature as large as 0.5 K azimuthally and >0.5 K axially. The overall temperatures stay close to the solidification temperature and therefore support high backflow and explain extrudate stall scenarios experienced in other hydrogenic twin screw extruders. This temperature data is therefore useful to size tolerance gaps in future extruder designs and enables refinement of predictive models for continuous operation.