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DOE OSTI2015

Integrated sampling and data flow for Hanford vitrification - 15337

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is a one-of-a-kind nuclear facility being designed, built, and commissioned for the U.S. Department of Energy (DOE) by Bechtel National, Inc. and its subcontractor URS Corporation. The WTP facilities are under construction to complete the design and construction, as well as planning to support the startup, commissioning, and operation for vitrifying the radioactive waste stored in aging underground tanks at the Hanford Site. The work progress includes development of waste feed acceptance and qualification program, including methodologies for processing feed batches. Qualification data from staged feed samples will support the verification that waste acceptance criteria (WAC) and processability requirements are met. This verification is required to ensure safe operation of WTP processes for vitrifying the waste, while establishing specific campaign and batch sheets to comply with glass form acceptance requirements. Sampling and data integration are critical for process control and monitoring of the feed through unit operations. Data collection begins with process knowledge of staged feed for determining the required WAC analyses and testing for unit operations. The waste feed qualification data form the baseline information for WTP operation. The qualification data will be tracked and compared with sample analyses at different steps in vitrification operation. All aspects of data life cycle will be managed by the WTP Plant Engineering organization. The data management approach is established in this manner to allow maintaining campaign traceability through glass production records, while safely processing the corresponding feed campaigns and complying with permitting requirements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

Event-Based Parareal: A data-flow based implementation of parareal

Parareal is an iterative algorithm that, in effect, achieves temporal decomposition for a time-dependent system of differential or partial differential equations. A solution is obtained in a shorter wall-clock time, but at the expense of increased compute cycles. The algorithm combines a fine solver that solves the system to acceptable accuracy with an approximate coarse solver. The critical task for the successful implementation of parareal on any system is the development of a coarse solver that leads to convergence in a small number of iterations compared to the number of time slices in the full time interval, and is, at the same time, much faster than the fine solver. Fast coarse solvers may not lead to sufficiently rapid convergence, and slow coarse solvers may not lead to significant gains even if the number of iterations to convergence is satisfactory. We find that the difficulty of meeting these conflicting demands can be substantially eased by using a data-driven, event-based implementation of parareal instead of the conventional algorithm where solver tasks are executed sequentially. For given convergence properties, the event-based approach relaxes the speed requirements on the coarse solver by a factor of , where is the number of iterations required for a converged solution. This may, for many problems, lead to an efficient parareal implementation that would otherwise not be possible or would require substantial coarse solver development.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016