DNS-based predictive control of turbulence: an optimal benchmark for feedback algorithms

摘要: Direct numerical simulations (DNS) and optimal control theory are used in a predictive setting to determine controls that effectively reduce the turbulent kinetic energy drag of flow plane channel at Re τ = 100 180. Wall transpiration (unsteady blowing/suction) with zero net mass flux is as control. The algorithm for optimization based solely on objective nonlinear partial differential equation governing flow, no ad hoc assumptions other than finite prediction horizon, T , over which optimized. Flow relaminarization, accompanied by reduction 50%, obtained some cases approach direct subcritical flows. Such performance far exceeds what has been date similar flows (using this type actuation) via adaptive strategies such neural networks, intuition-based opposition control, so-called ‘suboptimal’ strategies, involve optimizations vanishingly small horizon + → 0. To achieve relaminarization approach, it shown necessary optimize sufficiently long [gsim ] 25. Implications result discussed. requires full field information computationally expensive, involving iterative simulations. It is, therefore, impossible implement directly practical setting. However, these calculations allow us quantify best possible system given certain class actuation qualify how optimized correlate near-wall coherent structures believed dominate process turbulence production wall-bounded Further, various approaches have proposed distil feedback schemes from without suboptimal approximation, present work restrict severely effectiveness resulting algorithm. thus represents further step towards determination optimally effective yet implementable mitigation or enhancement consequential effects turbulence.