Replication data for : High-Fidelity Simulation of Turbulent Mixed Convection in Pipe Flow (Re=5300, Pr=0.71, Ri = 0.964)

The present dataset contains time and space averaged profiles for various turbulent quantities in case of forced and mixed convection in a turbulent pipe flow. The dimensionless numbers for the cases are Re=5300, Pr=0.71 and various Ri (0, 0.252, 0.348, 0.964). For a full description of the configuration, please check the corresponding paper : High-Fidelity Simulation of Turbulent Mixed Convection in Pipe Flow by Rodrigo Vicente Cruz, Cédric Flageul, Eric Lamballais, Vladimir Duffal, Erwan Le Coupanec and Sofiane Benhamadouche (submitted to International Journal of Heat and Fluid Flow). Direct Numerical Simulations are performed to provide a fine description of the turbulent mixed convection flow in an upward heated pipe. The introduction of buoyancy forces in this type of flow gives rise to different non-trivial phenomena such as laminarisation and a bi-stable behaviour, capable of modifying the flow characteristics in a deep and non-monotonic way. The present work aims therefore to establish a high-fidelity and publicly accessible database, containing first and second-order accurate turbulence statistics, as well as budgets of various one-point correlations of turbulent quantities, namely the turbulent kinetic energy and its associated dissipation rate, the temperature variance and its associated dissipation rate, the Reynolds stress tensor and the turbulent heat flux vector. Besides, the coexistence of two and very distinct statistically stationary solutions for a same set of flow parameters is also investigated. These two states are referred to as weak- and strong-turbulence regimes. The budget analyses performed here, such as the original ones presented for the budgets of the Reynolds stresses, contribute to gaining further understanding of the physics of mixed convection flows. The results issued from the present study shall also support the validation of lower-fidelity numerical models, such as Large Eddy Simulation, RANS and hybrid RANS/LES approaches.