Neural-network models have been employed to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow. Numerical simulation data at the wall is utilized to predict the instantaneous velocity-fluctuations and polymeric-stress-fluctuations at three different wall-normal positions in the buffer region. The ability of non-intrusive predictions has not been previously investigated in non-Newtonian turbulence. Our analysis shows that velocity-fluctuations are predicted well from wall measurements in viscoelastic turbulence. The models exhibit enhanced accuracy in predicting quantities of interest during the hibernation intervals, facilitating a deeper understanding of the underlying physics during low-drag events. The neural-network models also demonstrate a reasonably good accuracy in predicting polymeric-shear stress and the trace of the polymer stress at a given wall-normal location. This method could be used in flow control or when only wall information is available from experiments (for example, in opaque fluids). More importantly, only velocity and pressure information can be measured experimentally, while polymeric elongation and orientation cannot be directly measured despite their importance for turbulent dynamics. We therefore study the possibility to reconstruct the polymeric-stress fields from velocity or pressure measurements in viscoelastic turbulent flows. The results are promising but also underline that a lack of small scales in the input velocity fields can alter the rate of energy transfer from flow to polymers, affecting the prediction of the polymer-stress fluctuations. The present approach not only aids in extracting polymeric-stress information but also gives information about the link between polymeric-stress and velocity fields in viscoelastic turbulence.

翻译：本研究采用神经网络模型预测黏弹性湍流槽道近壁区域的瞬态流动。通过壁面数值模拟数据，成功预测了缓冲区内三个不同壁法向位置上的瞬时速度脉动与聚合物应力脉动。在非牛顿湍流领域，此类非侵入式预测能力尚未得到系统研究。分析表明，基于壁面测量数据可有效预测黏弹性湍流中的速度脉动。模型在流动休眠期（低阻事件阶段）对关键物理量的预测精度显著提升，有助于深化对该阶段底层物理机制的理解。神经网络模型在预测特定壁法向位置的聚合物剪切应力及聚合物应力迹线方面亦表现出良好精度。该方法可应用于流动控制或仅能通过实验获取壁面信息的场景（如不透明流体）。尤为重要的是，实验中仅能直接测量速度与压力信息，而尽管聚合物链的拉伸与取向对湍流动力学至关重要，却无法直接观测。因此，本研究探索了从速度或压力测量数据重构黏弹性湍流中聚合物应力场的可行性。研究结果展现出良好前景，但同时也揭示：输入速度场中小尺度结构的缺失可能改变流动至聚合物的能量传递速率，从而影响聚合物应力脉动的预测精度。本方法不仅为提取聚合物应力信息提供了新途径，更有助于揭示黏弹性湍流中聚合物应力场与速度场之间的内在关联。