The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (LGAC). The actuator is a set of six slot jets in crossflow aligned with the freestream. An open-loop optimal periodic forcing is defined by the carrier frequency, the duty cycle and the phase difference between actuators as control parameters. The control laws are optimised with respect to the unperturbed TBL and to the actuation with a steady jet. The cost function includes the wall convective heat transfer rate and the cost of the actuation. The performance of the controller is assessed by infrared thermography and characterised also with particle image velocimetry measurements. The optimal controller yields a slightly asymmetric flow field. The LGAC algorithm converges to the same frequency and duty cycle for all the actuators. It is noted that such frequency is strikingly equal to the inverse of the characteristic travel time of large-scale turbulent structures advected within the near-wall region. The phase difference between multiple jet actuation has shown to be very relevant and the main driver of flow asymmetry. The results pinpoint the potential of machine learning control in unravelling unexplored controllers within the actuation space. Our study furthermore demonstrates the viability of employing sophisticated measurement techniques together with advanced algorithms in an experimental investigation.

翻译：采用基于线性遗传算法控制的人工智能方法，对平板湍流边界层中的对流换热进行强化。执行器为一组与来流方向一致的横流射流槽缝。通过载波频率、占空比以及执行器间的相位差作为控制参数，定义了开环最优周期性激励。控制律以未受扰动的湍流边界层和稳态射流激励为基准进行优化，代价函数包含壁面对流换热速率与激励成本。控制器性能通过红外热成像评估，并辅以粒子图像测速测量进行表征。最优控制器产生轻微非对称流场。线性遗传算法控制对所有执行器收敛至相同频率与占空比。值得关注的是，该频率与近壁区大尺度湍流结构平均迁移时间的倒数惊人一致。多射流激励间的相位差被证明至关重要，是流场非对称性的主要驱动因素。研究结果揭示了机器学习控制在执行空间内探索未开发控制器的潜力。此外，本研究证实了在实验研究中结合先进测量技术与复杂算法的可行性。