A Bayesian optimisation framework is developed to optimise low-amplitude wall-normal blowing control of a turbulent boundary-layer flow. The Bayesian optimisation framework determines the optimum blowing amplitude and blowing coverage to achieve up to a 5% net-power saving solution within 20 optimisation iterations, requiring 20 Direct Numerical Simulations (DNS). The power input required to generate the low-amplitude wall-normal blowing is measured experimentally for two different types of blowing device, and is used in the simulations to assess control performance. Wall-normal blowing with amplitudes of less than 1% of the free-stream velocity generate a skinfriction drag reduction of up to 76% over the control region, with a drag reduction which persists for up to 650δ 0 downstream of actuation (where δ 0 is the boundary-layer thickness at the start of the simulation domain). It is shown that it is the slow spatial recovery of the turbulent boundary-layer flow downstream of control which generates the net-power savings in this study. The downstream recovery of the skin-friction drag force is decomposed using the Fukagata-Iwamoto-Kasagi (FIK) identity, which shows that the generation of the net-power savings is due to changes in contributions to both the convection and streamwise development terms of the turbulent boundary-layer flow.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints -eprint.ncl.ac.uk Lai J, Moody A, Chakraborty N. Turbulent kinetic energy transport in head-on quenching of turbulent premixed fames in the context of Reynolds Averaged Navier Stokes simulations.

The over-all duty of a turbomachine with respect to fluid-friction effects is measured by the machine Reynolds number, UD/ν. Experimental data are presented for several types of turbomachines which show the variation in over-all efficiency with UD/ν when all other dimensionless parameters are held constant. The results are conclusive for the ranges of data reported, and should be useful to design, application, and operating engineers confronted with unfamiliar UD/ν values.

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