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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.
P. Rodriguez-Fernandez, A. E. White, A. J. Creely, M. J. Greenwald, N. T. Howard, F. Sciortino, J. C. Wright
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 65-76
Technical Paper | doi.org/10.1080/15361055.2017.1396166
Articles are hosted by Taylor and Francis Online.
Understanding transport in magnetically confined plasmas is critical for developing predictive models for future devices such as ITER. Thanks to recent progress in simulation and theory, along with enhanced computational power and better diagnostic systems, direct and quantitative comparisons between experimental results and models is possible. However, validating transport models using additional constraints and accounting for experimental uncertainties still remains a formidable task. In this work, a new optimization framework is developed to address the issue of constrained validation of transport models. The Validation via Iterative Training of Active Learning Surrogates (VITALS) framework exploits surrogate-based strategies using Gaussian processes and sequential parameter updates to achieve the combination of plasma parameters that matches experimental transport measurements within diagnostic error bars. VITALS is successfully implemented to study L-mode plasmas in the Alcator C-Mod tokamak, and for the first time, additional measurable quantities, such as incremental diffusivity and fluctuation levels, are used during the validation process of the quasi-linear transport models TGLF-SAT1 and TGLF-SAT0. First results indicate that these machine-learning algorithms are very suitable and adaptable as a self-consistent, fast, and comprehensive validation methodology for plasma transport codes.