436591 Oxy-Coal Power Boiler Simulation and Validation through Extreme Computing

Tuesday, November 10, 2015: 1:40 PM
250E (Salt Palace Convention Center)
Philip Smith, Chemical Engineering, University of Utah, Institute for Clean and Secure Energy, Salt Lake City, UT

Coal remains the low-cost but high carbon power generation option around the globe. We have studied the design of a 500 MW pulverized-coal boiler under advanced ultra-supercritical (AUSC) oxy-coal firing with pure oxygen (no flue gas recirculation) for carbon capture and high efficiency. This study was performed using high performance computing(HPC) at the extreme scale (two - twenty thousand processors per simulation) and dynamic large eddy simulation (LES). The HPC and LES in this study allowed for 1-2 cm resolution of the turbulence within the boiler and temporal resolution at the microsecond time scale. All of the scales of the particle transport and reaction are fully resolved (DNS) for the entire particle size distribution. Particle size segregation and clustering are spatially and temporal resolved.

This study targeted a formal validation and uncertainty quantification by using oxy-coal data from the 15MW testing program operated by Alstom Power under 5 years of DOE funding. For this validation study, 160 different temperature measurements, 20 different measurements of the heat flux, and 40 different O2 concentration were used as the specific quantities of interest as a function of wall and deposit thermal conductivity, thickness and emissivity, fuel feed rate, and coal reactivity. Our validation approach identified the region of simultaneous consistency between all experimental and simulation data, according to the consistency constraint, ue ≥ [ym(x) − ye] ≥ le, where the defect between the quantity of interest as predicted by the model and as measured by the experiment is bounded by the measurement uncertainty.

Our validation approach identified the region of consistency between experimental and simulation data; thus, providing insight into the experimental measurements that could not be achieved without concurrent simulation and validation. This simulation and validation approach to predictive design provides a path to accelerating deployment of new low-cost, low-carbon technologies.

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