一种风力发电机的尾迹识别方法

本发明提供一种风力发电机的尾迹识别方法,先采集试验风力发电机的所有流场数据,然后对流场数据通过差分方法进行求导计算,得到速度梯度张量,然后再计算出流场每个点上的流动不变量数据；对已知的数据进行分析,并按预定标准将收集的数据分为强湍流和弱湍流；以流动不变量数据作为输入量,以强湍流和弱湍流数据作为学习对象,通过机器学习算法软件生成一个识别器；将需要识别流场的不变量数据输入识别器,然后根据预定标准将符合强湍流的数据区域绘出,即得到产生当前需要识别流场的风力发电机的尾迹区域。本发明使用了现代计算机科学中的大数据分析方法,只需要提供充分的数据,而不需要加入其他人为干涉,可以保证计算结果的客观性

Self-propelled swimming of a flexible wing and flow pattern recognition

自然界中的大多数生物都浸没在流体之中，如飞行的鸟类和游动的鱼类等。这些生物在生存进化的过程中逐渐发展和形成了不同的运动技巧和对环境的感知能力。例如飞行和游动生物的各种高效自推进运动，水生生物利用周围的流场信息进行导航和运动规划等。生物的高效推进方式和对环境的感知能力是各种人造仿生推进器和信号探测器设计的重要灵感来源。生物的自推进运动通常涉及到柔性翼或鳍的变形，对这类问题的研究有助于我们了解生物高效推进形成的机理，设计出性能更出色的仿生推进器。水生生物可以借助于自身的感知器官识别出局部流场环境中水动力学信号之间的差异，对于这类局部流场信息识别问题的研究可以为相应的仿生流场信号探测器提供一定的理论指导。此外，还有一类流场识别问题也是人们所关注的焦点，即流场中的湍流和非湍流之间界面的识别。因此，针对生物的自推进运动问题，本文研究了仿生柔性翼简化模型的自推进运动和一些关键控制参数对其推进性能的影响；对于局部流场信息识别问题，本文建立了基于人工神经网络的自推进柔性扑翼尾迹识别模型。最后，对于流场中的湍流和非湍流界面识别问题，本文采用多种机器学习方法，建立了数据驱动的湍流和非湍流界面识别模型。 本文的主要创新性工作包括以下三个部分： (一). 升沉和俯仰联合驱动下仿生柔性翼的自主推进 针对升沉和俯仰联合驱动下仿生柔性翼的自主推进问题，本文采用数值模拟的方法研究了柔性翼的刚度、升沉和俯仰驱动之间的相位差、升沉与俯仰驱动的幅值等驱动参数对推进性能的影响。结果表明，随着刚度的增加，柔性翼的运动形态逐渐由波动式转变为摆动式。研究发现，升沉-俯仰联合驱动的柔性翼的推进性能优于单纯的升沉驱动，其在刚度比较大的情况下避免了推进速度的下降，且在很大的刚度范围内有更高的推进效率。此外，研究发现升沉和俯仰联合驱动之间的相位差是影响柔性翼推进性能和运动形态的关键因素。最后将自推进柔性翼的运动学形态和尾迹结构与游动动物进行了比较，并对其尾迹结构进行了分类。这里的研究结果可以为水下仿生机器人的设计提供一些新的思路。 (二). 人工神经网络识别自推进柔性翼的尾迹类型 针对局部流场中的水动力信号的识别问题，本文考察了基于测量的局部流场变量的自推进柔性翼尾迹结构的识别。这个问题的灵感来自于游动动物在黑暗条件下感知水动力环境的非凡能力。通过训练不同的人工神经网络模型，分别利用局部流向速度分量、横向速度分量、涡量以及三个流场变量的组合对自推进柔性翼的尾迹结构进行了识别。结果表明，利用两种局部速度分量训练的人工神经网络模型在识别尾迹类型方面表现良好，而利用局部涡量值训练的人工神经网络模型存在较高的识别错误率。最后利用这三种局部流场变量的组合对人工神经网络进行训练，可以获得很高的尾迹识别准确率。这里的研究结果可为水下机器人环境感知系统的设计提供一定的理论指导。 (三). 机器学习识别圆柱绕流尾迹中的湍流区域 湍流和非湍流之间界面的识别是湍流研究中一个具有挑战性的课题。本研究提出了采用机器学习的方法训练检测器来识别流经圆柱的湍流区域。为了保证湍流和非湍流之间的界面与坐标系的选取无关，本文提出采用流场中的张量不变量作为输入特征来训练检测器。通过数值模拟的方式生成雷诺数为 Re=100 和 Re=3900 的圆柱绕流流场数据，用于训练和测试探测器的识别性能。为了测试不同机器学习方法的性能，分别训练了四个检测器，即基于全连接人工神经网络 (FCN) 的方法，基于极端梯度提升(XGBoost) 的方法，以及基于两组不同训练数据集的自组织映射网络 (SOM) 的方法。研究发现，有监督的学习方法(FCN 和 XGBoost 方法)在识别流动状态时的性能优于无监督学习的 SOM 方法。其中，XGBoost 检测器将 Re=100 时整个流场区域的流动状态识别为非湍流状态。FCN 检测器也正确识别了 Re=100 时流场中的绝大部分非湍流区域，除了一些远离圆柱尾迹的位置。对于 Re=3900 时的流场, FCN 和 XGBoost 方法都成功地捕获了蜿蜒的尾迹特征。通过比较 XGBoost 探测器和基于涡量模量和交叉速度脉动的检测方法，发现 XGBoost 检测器优于这些传统的检测方法，同时 XGBoost 检测器在较高雷诺数 Re=5000 时的流场中表现出了很好的鲁棒性。</p

使用机器学习识别圆柱尾迹中的湍流区域

探测湍流/非湍流界面是湍流研究中一个具有挑战性的课题。本文使用了机器学习方法训练探测器,用于识别圆柱绕流尾迹中的湍流区域。为保证所得到的湍流区域不依赖于坐标系的选取,我们提出了使用流动不变量作为探测器的输入变量,包括出现在脉动速度、脉动应变率张量、脉动涡张量的输运方程中的张量的不变量。训练所使用的数据为使用直接数值模拟/大涡模拟生成的Re=100和3900的圆柱绕流流场,使用的模型是Extreme Gradient Boosting(XGBoost),该方法属于监督学习方法,训练时需要给出流场对应的标签,即流动状态为湍流或非湍流,因此训练数据需选自流动状态已知的区域,例如湍流样本选自Re=3900的流动中远离湍流/非湍流界面的尾迹核心区域,非湍流样本则选自Re=3900的流动中的圆柱上游区域以及Re=100的流动中的圆柱尾迹区域。在训练完成之后,将流动中任意一点的不变量输入到探测器中,可以得到该点的流动状态,遍历全场即可得到流场中的湍流/非湍流界面。为了保证探测器结果的客观性,我们检验了训练过程中人为因素的影响,包括湍流样本的选取区域以及作为输入的不变量的个数。测试结果表明,扩大或缩小湍流样本的选取区域不影响湍流/非湍流界面的探测结果,增加作为输入的不变量的个数也不影响探测结果,从而保证了结果的客观性。和传统的湍流/非湍流界面的识别方法相比,客观性是机器学习方法的主要优势,具体体现在两个方面:1.传统方法需要人为指定输入变量的阈值作为区分湍流和非湍流的判据,而阈值的选取具有主观性,需要根据流动参数人为调整才能取得理想的识别效果,而机器学习方法不需要指定阈值,排除了这一人为因素的影响;2.传统方法通常只能选择一个或者两个变量作为输入量,而机器学习方法可以同时处理多个输入变量,从而反应湍流的不同特点,以本文的研究为例,输入的变量中对探测结果起决定性作用的不变量分别表征了湍流运动的非定常特性、涡拉伸现象、以及三维性等重要性质,并且不同的不变量具有不同的特征尺度,因此也反应了湍流的多尺度特性

Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry

Near-wall velocity prediction for wall-bounded turbulence is useful for constructing a wall model and estimating dissipation and wall shear stress. A convolutional neural network is developed to improve the near-wall velocity prediction and spatial resolution for wall-bounded turbulent velocity fields obtained using particle image velocimetry (PIV). To establish the relationship between the low-resolution and high-resolution fields, this machine learning model is trained on a synthetic PIV dataset generated based on velocity fields obtained from the direct numerical simulation of turbulent channel flows at Re-tau = 1000. Using a test dataset with a higher Reynolds number of Re-tau = 5200, the performance of this model is assessed in terms of instantaneous fields, error analysis, velocity statistics, and energy spectra. The influences of the interrogation window, image resolution, and particle concentration on the performance of this network are also considered. We further apply this network to practical PIV data from a turbulent boundary layer at Re-tau = 2200 to assess the network performance under real experimental conditions. The results indicate that the proposed machine-learning-based model can predict missing near-wall velocity fields and enhance the spatial resolution of PIV fields, but the accuracy for Reynolds shear stress prediction needs to be further improved. The presented approach shows the potential ability to predict the near-wall instantaneous velocity of high-Reynolds-number turbulence from low-Reynolds-number flow fields

A robust super-resolution reconstruction model of turbulent flow data based on deep learning

A new super-resolution model, namely the turbulence volumetric super-resolution (TVSR) model, is developed based on convolutional neural network (CNN) to reconstruct three-dimensional high-resolution turbulent flow field data from low-resolution data. Direct numerical simulation (DNS) and corresponding filtered DNS (FDNS) data of homogeneous isotropic turbulence at various Reynolds numbers are used to train the TVSR model. The proposed model is a modification of Liu et al. (2020), aiming to provide an improved generalization capability of the super-resolution model. For this purpose, we propose a patchwise training strategy in consideration of the property of turbulence that the velocity correlation between two points diminishes as the separation becomes sufficiently large. Furthermore, data at various Reynolds numbers are combined together to train the model. In comparison with existing models, the present TVSR model shows a better generalization capability in two aspects. First, the TVSR model trained using data at low Reynolds numbers is found robust and accurate in the super-resolution reconstructions of flow fields at higher Reynolds numbers. Second, although only DNS data are used for training, the TVSR model is also robust in reconstructing high-resolution flow fields from low-resolution data obtained from large-eddy simulation (LES). This feature of the TVSR model provides a new access to obtain turbulent motions at unresolved scales in LES studies of turbulent flows

Classifying wakes produced by self-propelled fish-like swimmers using neural networks

We consider the classification of wake structures produced by self-propelled fish-like swimmers&nbsp;based on local measurements of flow variables. This problem is inspired by the extraordinary&nbsp;capability of animal swimmers in perceiving their hydrodynamic environments under dark&nbsp;condition. We train different neural networks to classify wake structures by using the streamwise&nbsp;velocity component, the crosswise velocity component, the vorticity and the combination of three&nbsp;flow variables, respectively. It is found that the neural networks trained using the two velocity&nbsp;components perform well in identifying the wake types, whereas the neural network trained using&nbsp;the vorticity suffers from a high rate of misclassification. When the neural network is trained using&nbsp;the combination of all three flow variables, a remarkably high accuracy in wake classification can&nbsp;be achieved. The results of this study can be helpful to the design of flow sensory systems in&nbsp;robotic underwater vehicles.</p

Using machine learning to detect the turbulent region in flow past a circular cylinder

Detecting the turbulent/non-turbulent interface is a challenging topic in turbulence research. In the present study, machine learning methods are used to train detectors for identifying turbulent regions in the flow past a circular cylinder. To ensure that the turbulent/non-turbulent interface is independent of the reference frame of coordinates and is physics-informed, we propose to use invariants of tensors appearing in the transport equations of velocity fluctuations, strain-rate tensor and vortical tensor as the input features to identify the flow state. The training samples are chosen from numerical simulation data at two Reynolds numbers, and 3900. Extreme gradient boosting (XGBoost) is utilized to train the detector, and after training, the detector is applied to identify the flow state at each point of the flow field. The trained detector is found robust in various tests, including the applications to the entire fields at successive snapshots and at a higher Reynolds number . The objectivity of the detector is verified by changing the input features and the flow region for choosing the turbulent training samples. Compared with the conventional methods, the proposed method based on machine learning shows its novelty in two aspects. First, no threshold value needs to be specified explicitly by the users. Second, machine learning can treat multiple input variables, which reflect different properties of turbulent flows, including the unsteadiness, vortex stretching and three-dimensionality. Owing to these advantages, XGBoost generates a detector that is more robust than those obtained from conventional methods

生态系统生产力供给服务合理消耗度量方法——以内蒙古草地样带为例1

高强度资源利用与持续增加的食物、纤维等需求正在导致对地球表面有限自然资源消耗强度的日益增长，如何构建生态系统服务供给和消耗相匹配的科学合理的模式是减缓乃至遏制生态系统退化面临的新挑战，需要发展生态系统服务合理消耗的评价体系与模拟方法，其中发现自然生态系统服务供给与人类对其消耗的结合点和适宜指标是关键。生态系统净初级生产力（NPP）消耗反映了人类对生态系统供给服务利用强度，本研究以 NPP 消耗为主要指标建立了生态服务合理消耗评价体系的概念框架与计算方法，并以生态消耗模式与强度具有明显梯度的内蒙古草地样带为案例区开展了实证分析。合理生态消耗被定义为人类为维持生计对生态系统供给服务的消耗既不对生态系统产生过度压力又能够满足人类维持生计的基本生活需求。本文详细阐述了生态系统生产力供给服务合理消耗评价过程中的数据选取原则与方法，生态消耗及其合理性阈值的计算步骤与依据。该方法基于生态系统服务消耗主体的属性特征设计，是开发生态系统生产力供给服务合理消耗多主体技术模拟平台的算法基础，也是为发展兼顾多种生态系统服务的生态服务合理消耗综合评价方法体系进行的尝试

Self-propelled swimming of a flexible filament driven by coupled plunging and pitching motions

This paper numerically investigates the self-propelled swimming of a flexible filament driven by coupled pitching and plunging motions at the leading edge. The influences of bending rigidity and some actuation parameters (including the phase offset between pitching and plunging, and the amplitudes of pitching and plunging motions) on the swimming performance are explored. It is found that with increasing rigidity, the swimming style gradually transits from the undulatory mode to the oscillatory mode. The plunging-pitching actuation is found to be superior to the plunging-only actuation, in the sense that it prevents the decrease of speed at high rigidity and achieves a higher efficiency across a wide range of rigidity. The comparison of the body kinematics with those of animal swimmers, and the classification of the wake structures are discussed. The results of this study provide some novel insights for the bio-inspired design of autonomous underwater vehicles.</p