Neural-network models have been employed to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow. The numerical simulation data at the wall is utilized to predict the instantaneous velocity fluctuations and polymeric-stress fluctuations at three different wall-normal positions. Apart from predicting the velocity fluctuations well in a hibernating flow, the neural-network models are also shown to predict the polymeric shear stress and the trace of the polymeric stresses at a given wall-normal location with reasonably good accuracy. These non-intrusive sensing models can be integrated in an experimental setting to construct the polymeric-stress field in turbulent flows, which otherwise may not be directly quantifiable in experimental measurements.

翻译：神经网络模型已被用于预测粘弹性湍流槽道流动中近壁面的瞬时流动。利用壁面处的数值模拟数据，预测了三个不同壁面法向位置处的瞬时速度脉动和聚合物应力脉动。除了在休眠流动中能准确预测速度脉动外，神经网络模型还能以较好的精度预测给定壁面法向位置处的聚合物剪切应力和聚合物应力迹线。这些非侵入式传感模型可集成至实验装置中，用于构建湍流中的聚合物应力场——该物理量在实验测量中可能无法直接量化。