Prediction and understanding of the wall-shear stress modulation by non-linear interactions based on novel machine learning techniques

基于新颖机器学习技术的非线性相互作用对壁剪应力调制的预测和理解

• 批准号: 525782963
• 负责人: Dr.-Ing. Esther Lagemann
• 金额: --
• 依托单位: Lehrstuhl für Strömungslehre und Aerodynamisches Institut (AIA)
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/525782963?language=en
• 关键词: Prediction; understanding; wall; shear; stress

Turbulent wall-bounded fluid flows are of significant importance for numerous engineering and biomedical applications. However, they are typically characterized by high-dimensional, non-linear, and unsteady dynamics that exhibit rich multi-scale phenomena in space and time. Due to such challenging characteristics, we still lack a comprehensive understanding of these complex fluids. One key ingredient necessary for a majority of applications is the spatially and temporally resolved wall-shear stress. Besides providing a measure for the friction drag - a quantity of utmost importance in the transportation sector - it also gives insight into the dynamic loads imposed onto rigid/flexible walls, which can be crucial in human medicine. Despite its significance, it is still exceptionally difficult to measure instantaneous and spatially well-resolved wall-shear stress distributions. Most existing measurement sensors are single-direction and/or single-dimension devices that can only detect one wall-shear stress component at a fixed location. Moreover, the spatial resolution and the maximum number of jointly deployed sensors is typically limited due to experimental constraints arising from secondary electronic devices. As a result, the overwhelming majority of recent studies targeting the investigation of multi-scale phenomena that modulate the wall-shear stress dynamics are limited to time-dependent data without spatial resolution and consequently, cannot provide a comprehensive picture of the complex physics. Therefore, the overarching objective of this proposal is the development of modern deep learning based algorithms for the prediction of the wall-shear stress based on easily accessible velocity measurements. The envisioned neural networks are specifically designed to capture - without limiting assumptions - the complex non-linear and unsteady interactions that are responsible for the wall-shear stress dynamics and to make them interpretable for the human kind. In particular, this proposal targets the development of a deep learning based architecture specifically designed to learn a mapping function from two-dimensional velocity fields located in the outer layer of a turbulent wall-bounded flow to the instantaneous spatially resolved wall-shear stress distribution. Complementary, an interpretable mathematical expression, which corresponds to the inherently learnt transfer function, is extracted from the derived latent representation via symbolic regression. This expression is further investigated to gain deeper insight into the non-linear interactions that result in the modulation of the wall-shear stress. Trained on direct numerical simulation data, the generalization of the envisioned learner is evidenced based on simultaneous particle-image velocimetry measurements in the outer layer and wall-shear stress measurements using the Micro-Pillar Shear-Stress Sensor.

壁面湍流流体流动对于许多工程和生物医学应用具有重要意义。然而，它们的典型特征是高维、非线性和不稳定的动力学，在空间和时间上表现出丰富的多尺度现象。由于这些具有挑战性的特性，我们仍然缺乏对这些复杂流体的全面了解。大多数应用所需的一个关键因素是空间和时间解析的壁剪应力。除了提供摩擦阻力的测量（摩擦阻力在运输领域中至关重要的量）之外，它还可以深入了解施加在刚性/柔性墙壁上的动态载荷，这对于人类医学至关重要。尽管其意义重大，但测量瞬时且空间分辨率良好的壁剪应力分布仍然异常困难。大多数现有的测量传感器是单向和/或单维设备，只能检测固定位置处的一个壁剪应力分量。此外，由于二次电子设备产生的实验限制，联合部署的传感器的空间分辨率和最大数量通常受到限制。因此，最近绝大多数针对调节壁剪应力动力学的多尺度现象的研究仅限于没有空间分辨率的时间相关数据，因此无法提供复杂物理的全面图像。因此，该提案的首要目标是开发基于现代深度学习的算法，用于基于易于访问的速度测量来预测壁剪应力。设想的神经网络经过专门设计，可以在不限制假设的情况下捕获复杂的非线性和不稳定的相互作用，这些相互作用是造成壁剪应力动力学的原因，并使它们可以被人类解释。特别是，该提案的目标是开发一种基于深度学习的架构，专门设计用于学习从位于湍流壁边界流外层的二维速度场到瞬时空间解析壁剪切应力分布的映射函数。互补的是，通过符号回归从派生的潜在表示中提取与固有学习的传递函数相对应的可解释的数学表达式。进一步研究该表达式，以更深入地了解导致壁剪切应力调制的非线性相互作用。通过直接数值模拟数据的训练，基于外层同步粒子图像测速测量和使用微柱剪切应力传感器的壁剪切应力测量，证明了预期学习器的泛化能力。