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NN Asks: What hurdles stand in the way of nuclear power’s global expansion?
Jake Jurewicz
Nuclear technology is mature. It provides firm power at scale with minimal externalities and has done so for decades. The core problem isn’t about the technology—it is how the plants are built. Nuclear construction has a well-documented history of cost and schedule overruns. Previous nuclear plants often spent more than twice what was first budgeted, making nuclear among the power technologies with the largest average cost overruns worldwide.
Recent projects illustrate how severe the problem can be. In South Carolina, the V.C. Summer nuclear expansion saw projected costs rise from roughly $10 billion to more than $25 billion before the project was abandoned in 2017, by which time more than $9 billion had already been spent and customers were stuck paying for a site they have yet to benefit from.
Ralph Wiser, Emilio Baglietto
Nuclear Technology | Volume 210 | Number 7 | July 2024 | Pages 1143-1166
Research Article | doi.org/10.1080/00295450.2023.2202802
Articles are hosted by Taylor and Francis Online.
Turbulent heat transfer in buoyancy-dominated flows is a challenging problem for computational fluid dynamics (CFD). Many authors attribute model error in these conditions to the Reynolds analogy. We leverage a brand-new direct numerical simulation database to evaluate the performance of several popular turbulence models in buoyant diabatic channel flow. We find that heat transfer results are relatively accurate, with a Nusselt number error less than 20%. However, the turbulent flow solution is very inaccurate, with wall shear overpredicted by up to 100%. This indicates significant turbulence model error in such flows. We determined that the dominant sources of model error are missing physics in the algebraic Reynolds stress framework and the simple buoyancy production term used in industrial CFD. We suggest that future modeling efforts focus on these two sources of model error. We demonstrate that the Reynolds analogy is not the dominant source of model error.