We develop an open-loop control system using machine learning to destabilize and stabilize the mixing layer. The open-loop control law comprising harmonic functions is explored using the linear genetic programming in a purely data-driven and model-free manner. The best destabilization control law exhibits a square wave with two alternating duty cycles. The forced flow presents a 2.5 times increase in the fluctuation energy undergoing early multiple vortex-pairing. The best stabilization control law tames the mixing layer into pure Kelvin–Helmholtz vortices without following vortex-pairing. The 23% reduction of fluctuation energy is achieved under the dual high-frequency actuations.

中文翻译：

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混合层的机器学习开环控制

我们开发了一个开环控制系统，使用机器学习来破坏和稳定混合层。使用线性遗传规划以纯数据驱动和无模型的方式探索包含谐波函数的开环控制律。最好的失稳控制律表现出具有两个交替占空比的方波。受迫流使经历早期多重涡流配对的波动能量增加了 2.5 倍。最好的稳定控制法则将混合层驯服成纯开尔文-亥姆霍兹涡旋，而无需遵循涡旋配对。双高频驱动下，波动能量降低23%。