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Fusion Science and Technology
Latest News
Glass strategy: Hanford’s enhanced waste glass program
The mission of the Department of Energy’s Office of River Protection (ORP) is to complete the safe cleanup of waste resulting from decades of nuclear weapons development. One of the most technologically challenging responsibilities is the safe disposition of approximately 56 million gallons of radioactive waste historically stored in 177 tanks at the Hanford Site in Washington state.
ORP has a clear incentive to reduce the overall mission duration and cost. One pathway is to develop and deploy innovative technical solutions that can advance baseline flow sheets toward higher efficiency operations while reducing identified risks without compromising safety. Vitrification is the baseline process that will convert both high-level and low-level radioactive waste at Hanford into a stable glass waste form for long-term storage and disposal.
Although vitrification is a mature technology, there are key areas where technology can further reduce operational risks, advance baseline processes to maximize waste throughput, and provide the underpinning to enhance operational flexibility; all steps in reducing mission duration and cost.
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.