Methods of decreasing the CO₂ emissions of the internal combustion engine have been suggested. For example, an engine can be designed with a high compression ratio and/or a downsizing turbocharger. However, these methods generate high combustion temperatures that increase the heat load. The piston cooling gallery has been proposed as a system for cooling the engine piston. The piston cooling gallery is an oil flow path that is set internal to the piston. An oil jet injected from a nozzle placed under the piston flows into the piston cooling gallery through an entrance hall. It may thus be desirable to control the shape of the oil jet such that it is stable and straight. However, the interface of the ambient air and oil jet may have unstable waviness because of Kelvin- Helmholtz instability and/or Rayleigh-Taylor instability. In addition, we investigated the flow and found that the propagation of the flow speed fluctuation of the nozzle internal flow results in the waviness of the oil jet in a previous study. To further clarify the relationship between oil jet interface instability immediately after nozzle exit and flow in nozzle, this paper reports on two types of particle image velocimetry (PIV), namely two-dimensional two-velocity-component PIV and two-dimensional three-velocity-component PIV, in addition to two-component and three-component snapshot proper orthogonal decompositions, and analyzes turbulence propagation adopting a cross-correlation method. We find a characteristic basis vector with large energy that propagates the fluctuation downstream under the condition that the interface between the oil jet and air has strong waviness.

We attempt to optimize the control parameters of traveling wave-like wall deformation for turbulent friction drag reduction using the Bayesian optimization. The Bayesian optimization is an optimization method based on stochastic processes, and it is good at finding the parameter values to minimize (or maximize) an expensive cost function or a blackbox function. The parameter value to be tested in the next iteration step is chosen based on the acquisition function that accounts for both the exploration term searching in high uncertainty regions and the exploitation term searching in the regions of high possibility over the current best observations. First, we investigate the effectiveness of the Bayesian optimization using a two-parameter test function with known optimum value. As a result, the Bayesian optimization is shown to successfully work. Next, we apply the Bayesian optimization to the control parameters of traveling wave-like wall deformation for friction drag reduction in a turbulent channel flow at the friction Reynolds number of Re_τ = 180. While the wavenumber (k⁺_x) is fixed, the velocity amplitude (v⁺_w) and the phasespeed (c⁺) are chosen as the variable to optimize. As a result of the Bayesian optimization, although the bulk-mean velocity in the optimized case varies periodically, we achieved the maximum drag reduction rate of 60.5% when (v⁺_w, c⁺) = (10.0, 42), which is higher than that in the previous study (Nabae et al., 2020), i.e., 36.1%. In the optimized case, by repeating laminarization of flow field and re-transition to turbulent flow due to the inflection instability, the bulk-mean velocity increases and decreases periodically.