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Fusion Science and Technology
Cs-137 sealed source lost in Western Australia
A rendering of the sealed source capsule’s appearance. (Image: DFES)
Authorities are searching 1,400 kilometers (870 miles) of Australia’s Great Northern Highway, between Perth and the remote town of Newman, for a lost sealed-source capsule containing cesium-137. The source was part of a density gauge used by mining company Rio Tinto at its mining operations in Western Australia.
The Department of Fire and Emergency Services (DFES) of Western Australia reported that the density gauge containing a 6-mm-diameter (0.24-inch-diameter) by 8-mm-height (0.31-inch-height) source capsule was sent by flatbed truck to Perth for repair, leaving Rio Tinto’s Gudai-Darri mine site in Western Australia on January 12 and arriving in Perth on January 16. The package containing the gauge, however, was not inspected until January 25.
Upon opening the package, it was found that the gauge was broken apart with one of four mounting bolts missing. The source itself and all screws on the gauge were also missing. It is assumed that vibrations from the truck broke the gauge apart and allowed the screws and capsule to fall through the bolt hole and away from the truck. DFES said they were notified of the loss on the evening of January 25.
Cristina Rea, Robert S. Granetz
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 89-100
Technical Paper | doi.org/10.1080/15361055.2017.1407206
Articles are hosted by Taylor and Francis Online.
Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges from the DIII-D tokamak with the objective of developing a disruption classification algorithm. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters such as the plasma internal inductance and the Greenwald density fraction , coming from both plasma diagnostics and equilibrium reconstructions. The utilization of dimensionless indicators will facilitate a more direct comparison between different tokamak devices.
In order to eventually develop a robust disruption warning algorithm, we leverage Machine Learning techniques, and in particular, we choose the Random Forests algorithm to explore the DIII-D database. We show the results coming from both binary (disrupted/non-disrupted) and multiclass classification problems. In the latter, the time dependency is introduced through the definition of class labels on the basis of the elapsed time before the disruption (i.e., ‘far from a disruption’, ‘within 350 ms of disruption’, etc.). Depending on the formulation of the problem, overall disruption prediction accuracy up to 90% is demonstrated, approaching 97% when identifying a stable and a disruptive phase for disrupted discharges. The performances of the different Random Forest classifiers are discussed in terms of accuracy, by showing the percentages of successfully detected samples, together with the false positive and false negative rates.