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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. [ABSTRACT FROM AUTHOR]

This paper presents the development of a benchmark for predicting thermal striping through simulation. This work utilized large eddy simulation and will be used to benchmark future models. The testing domain was created using both the STRUCT and the Reynolds-averaged Navier-Stokes turbulence models and is based on an earlier design of the General Atomics Fast Modular Reactor upper plenum. The plenum features two adjacent, identical hexagonal bundles each with a center-placed axial rod drive, with a hot left coolant stream and a cold right coolant stream. The simulation solves the nondimensional Navier-Stokes equations, with temperature accounted as a passive scalar. First- and second-order flow statistics were obtained after 600 convective time units of averaging. The first-order statistics reveal that the hot jet is damped by a recirculatory flow from the near wall. At the same location, the second-order statistics show strong oscillations both in velocity and temperature. The power spectral density was utilized to determine that a low-frequency oscillation occurs here that is within the range of interest for thermal striping. Furthermore, proper orthogonal decomposition was used to identify coherent structures that confirm the oscillatory behavior, indicative of thermal striping. Overall, this benchmark can aid in the development of future models for predicting thermal striping in nuclear reactors, potentially leading to improved reactor safety and performance. [ABSTRACT FROM AUTHOR]

Turbulent mixing of coolant streams can result in an oscillatory mixing phenomenon called thermal striping. These fluctuations have the potential to lead to anticipated thermal fatigue failures in advanced nuclear reactors. To predict thermal striping, robust and computationally affordable modeling tools that are capable of accurately representing complex turbulence are needed. Hybrid turbulence approaches, such as detached-eddy simulation and scale-adaptive simulation, have shown some success in resolving complex unsteady turbulence for massively separated flows, however the applicability of these models to internal flows is limited. A STRUCTure-based (STRUCT) second-generation Unsteady Reynolds-Averaged Navier–Stokes turbulence model was recently proposed at the Massachusetts Institute of Technology to robustly extend the applicability of hybrid closures. In this work, the STRUCT model is evaluated using experimental data taken at the Reactor Cavity Cooling System separate-effects test facility at the University of Michigan. The experiments observed the interaction of parallel symmetric rectangular jets, and include measurements for mean profiles of velocity and Reynolds stresses. In the present work, the simulation results are assessed against mean profiles of velocity and Reynolds stresses, demonstrating the ability to reproduce the unsteadiness of the jets in close agreement with the measurements at considerably reduced computational cost. [ABSTRACT FROM AUTHOR]

Thermal striping is a phenomenon characterized by oscillatory mixing of non-isothermal streams, which is commonly seen in industrial processes such as nuclear coolant piping, petrochemical plants and liquefied natural gas transportation. The oscillatory mixing of hot and cold fluid can produce thermal field fluctuations and pose a potential risk of high-cycle thermal fatigue failures. Predicting and evaluating spatiotemporal fluctuations in thermal striping often requires high resolution and massive computational power. Although there have been extensive studies using machine learning algorithms on surrogate modeling, research focused on spatiotemporal fluctuation predictions is very limited. Due to the high dimensionality, it often requires complex algorithms with a large amount of high-fidelity training data, which limits the adoption of such methods for industrial applications. In this research, a two-level machine learning framework based on turbulence coherent structures is proposed and its application to a practical problem is demonstrated. The two-level design leverages vortex identification and local bias correction techniques, efficiently reducing the number of full-order simulations required for training. In the first level, well-organized coherent structures are extracted by performing Proper Orthogonal Decomposition on local parameters and then a tree-based machine-learning model is used to down-select the reference structures for the field reconstruction. In the second level, a parameterized convolution neural network is trained to predict the bias introduced by reference structures approximation. The demonstration of the methodology shows that the method can accurately capture the fluctuation frequencies and amplitudes of the spatiotemporal fields in a highly variational setting. Based on the vortex identification method, the methodology is expected to be applicable to general phenomenon driven by large coherent structures. [ABSTRACT FROM AUTHOR]

To optimize the efficiency and safety of molten salt-based energy applications, accurate molten salt thermophysical property data are required. For molten fluorides, existing thermal conductivity results have large uncertainties and contradict the current theory by eliciting a positive temperature coefficient. Transient grating spectroscopy (TGS), a technique previously deemed reliable by the theoretical community, has been used to measure the thermal conductivity of fluorides (FLiNaK) for the first time. Results show a fairly flat but slightly increasing thermal conductivity as a function of temperature. The technique has been shown to not suffer from contributions from convection and radiation, an explanation used to discount the results of alternative experimental techniques. In addition to thermal conductivity, sound speed data as a function of temperature have also been obtained for the first time in FLiNaK. The use of accurate sound speed data in theoretical models of thermal conductivity provides better but not complete agreement with the results from TGS. The continued existence of a positive temperature coefficient highlights the need for new mechanistic proposals for why TGS, or current theoretical models, are unable to capture the correct temperature dependence for fluoride molten salt thermal conductivity. [ABSTRACT FROM AUTHOR]