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Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior
Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior
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Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior
Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior

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Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior
Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior
Dissertation

Assessment of Hybrid CFD Turbulence Model, STRUCT- , for Thermal Striping Behavior

2024
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Overview
Many advanced nuclear reactor designs are susceptible to thermal fatigue damage caused by thermal striping, which presently accepted modeling and design tools are unable to accurately or reliably predict. Advanced reactors are vital in achieving netzero carbon electricity production and thus developing design tools that can predict thermal striping is essential. Any new design tool used in the nuclear industry must be validated against experimental data sets to ensure that results predicted by these methods are sufficiently accurate. The STRUCT- Computational Fluid Dynamics model was used to aid the development of a dedicated thermal striping experiment that will later be used to help validate the capabilities of various models.The STRUCT- model provided the ability to conduct turbulence resolving simulations at a speed conducive to rapid iteration of the design of the DESTROJER test facility. To further increase confidence in the model’s applicability to the test cases, two LES runs were completed and demonstrate the ability to capture flow unsteadiness with a Computation Fluid Dynamic model. However, in both test cases the STRUCT- model exaggerates the behavior seen in the LES runs; over predicting temperature oscillations in one case and the flow asymmetry in the other. The STRUCT- model’s potential to predict asymmetric configurations provides promising further applications of the model. Future studies of STRUCT- should seek to better understand the model’s performance in asymmetric flow cases to further support experimental design and the assessment of complex operating configurations.