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INL’s Teton supercomputer open for business
Idaho National Laboratory has brought its newest high‑performance supercomputer, named Teton, online and made it available to users through the Department of Energy’s Nuclear Science User Facilities program. The system, now the flagship machine in the lab’s Collaborative Computing Center, quadruples INL’s total computing capacity and enters service as the 85th fastest supercomputer in the world.
Jieun Lee, Paolo Balestra, Yassin A. Hassan, Robert Muyshondt, Duy Thien Nguyen, Richard Skifton
Nuclear Technology | Volume 208 | Number 12 | December 2022 | Pages 1769-1805
Technical Paper | doi.org/10.1080/00295450.2022.2081482
Articles are hosted by Taylor and Francis Online.
The verification and validation of Pronghorn is imperative for predicting the fluid velocity, pressure, and temperature in advanced reactors, specifically high-temperature gas-cooled reactors. Pronghorn is a coarse-mesh, intermediate-fidelity, multidimensional thermal-hydraulic code developed by Idaho National Laboratory. The Pronghorn incompressible Navier-Stokes equations are validated by using the pressure drop measurements and axial velocity averaged from the particle image velocimetry data obtained at the engineering-scale pebble bed facility at Texas A&M University.
Pronghorn and STAR-CCM+ porous media models using the Handley, Kerntechnischer Ausschuss, and Carman correlations comparably estimate the pressure drop better than other functions with a maximum 3.34% average relative difference compared to the experimental measurements. The precise average pebble bed porosity estimation has a large impact on the pressure drop. The implementation of the volume-averaged porosity in several sectors, with each sector’s thickness larger than the representative elementary length, has the potential to improve pressure drop modeling or provide more detailed velocity profiles in nuclear reactors with high aspect ratios. The wall effects can be considered using this approach, applying the relatively higher volume-averaged porosity near walls. In addition, the pressure gradients and volume- or surface-averaged axial velocities from the realizable two-layer and shear stress transport models are in good agreement with the porous media simulations and particle image velocimetry data.