Analysis of intermittency and probe data in a supersonic flow with injection

 Probe measurements were performed in the flow field produced by injection of helium or air into a supersonic airstream. The injectant was seeded with water and Rayleigh scattering was used to image the injectant plume. The region of the flow containing injectant–air mixture is seen to be highly unsteady, leading to the intermittent presence of injectant in certain regions. The intermittency is inferred. It is shown that bias errors can occur when the probe data is analyzed by techniques which assume steady flow. A technique for relatively bias-free analysis utilizing the intermittency measurements is presented and the bias errors are estimated. The gas-sampling probe is shown to measure the mass-weighted-mean mass fraction of helium, which is significantly less than the simple mean. A new measure of mixing efficiency obtained from the combined probe and intermittency measurements is discussed. Received: 2 February 1996/Accepted: 2 December 1996     

Improved methods for measuring laminar-turbulent intermittency in boundary layers

The last stage of laminar-turbulent transition in boundary layers is commonly described using the spatial distribution of intermittency. Existing methods for computing this laminar-turbulent intermittency from experimental data involve the use of thresholds that are set by the experimentalist using subjective comparisons to the raw signal. These threshold settings cannot be reproduced by other experimentalists using different equipment, so a precise determination of intermittency is not currently possible. This note reports on a new method of determining boundary-layer intermittency that appears to be objective and reproducible. The wall shear was measured in a flat-plate boundary layer using hot-film sensors. Probability density functions (PDF's) of the calibrated wall shear are remarkably consistent. A correlation to the turbulent portion of these PDF's is given. The consistency observed in these PDF's suggests an objective and reproducible setting for the laminar-turbulent cutoff threshold. Intermittencies determined using this method can be compared quantitatively, with any differences being caused only by experimental error or differences in the flows. The universality of the method can be determined through comparisons to measurements in other flows.     

圆Couette流混沌现象的实验研究

本文用流动显示和激光散射功率谱观泌了圆Couette流中的混沌现象,发现有两条导致弱湍流激发的道路:一条是从准周期运动到混沌,另一条是阵发混沌,对于这两条道路在诸转变阈值上的巨大差别,作者用弱非线性作用局域有效的新观点给出物理解释。     

Looking for “laminars”: Measuring intermittency on the America’s Cup race course

The determination of intermittency from experimental results has been achieved in the past by a number of approximate methods, the most prolific of which involves the use of a detector function based on the square of first and second derivatives of the flow velocity with respect to time. The disadvantages of such methods are that they rely on appropriate time domain binning of the data, they are calibration dependent, they involve error propagating numerical differentiation and 50% intermittency is incorrectly diagnosed as an extremely high level of turbulence. Where experimental records are of limited time spans, calibration is difficult, measurement errors are significant and a 50% intermittency measurement is required for design purposes, the detector method loses its utility. However, recent experimental and theoretical work by Ferchichi and Tavoularis [1] has revealed a remarkably Gaussian probability distribution for the thermal passive scalar. The degree of self-similarity (analysed through the flatness of the signal) can then be used as a measure of intermittency. Flatness analysis has been used in this study on full scale data obtained experimentally on an International America’s Cup Class (IACC) yacht to overcome the problems of intermittency measurement. A generalised signal conditioning technique has been proposed.     

Le modèle des peaux entropiques en turbulence développée

A geometry called entropic skins has been recently proposed to describe and interpret the phenomenon of intermittency in fully developed turbulence. It is shown that the entropic skins model represents the geometrical counterpart of the She–Lévêque's model but with a different interpretation for the main parameters. The comparison with the multifractal approach shows that this is a new geometrical framework of intermittency.     

NEURAL NETWORK MODEL OF MAGNETORHEOLOGICAL DAMPER FOR VIBRATION ISOLATION PLATFORM OF WHOLE-SPACECRAFT

为改善星箭界面振动环境,设计六杆隔振平台,采用磁流变阻尼器作为半主动控制元件,替代原有锥壳过渡支架.对整星隔振平台用磁流变阻尼器进行性能测试,得到反映磁流变阻尼器阻尼特性的实验数据.建立具有两个隐含层的反向传播神经网络对阻尼器进行建模,用于预测磁流变阻尼器阻尼特性以及控制系统设计.提出一种串行算法优化网络结构、权值和阈值,保证网络具有较好的泛化能力和稳定性.仿真结果表明,与参数化模型相比,提出的神经网络模型具有较小的训练误差和较强的泛化能力,能够很好地预测阻尼器的阻尼特性.     

基于遗传神经网络的锚杆极限承载力预测的研究

针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的锚杆极限承载力预测的遗传神经网络模型。该模型以低应变动测的5个变量作为输入变量来对锚杆极限承载力进行预测,并与BP神经网络预测结果进行比较。数值算例表明,遗传神经网络在锚杆极限承载力预测中具有较高的计算效率和识别精度。     

Nonlinear vibrations of a rotating shaft with broadband random variations of internal damping

A simple Jeffcott rotor is considered with broadband temporal random variations of internal damping which are described using the theory of Markov processes. Transverse response of the rotor with stiffening nonlinearity either in external damping or in restoring force is studied by stochastic averaging method. This method reduces the problems to stochastic differential equations (SDEs) for which analytical solutions are obtained for the Fokker–Planck–Kolmogorov (FPK) equations for stationary probability density functions (PDFs) of the squared whirl radius of the shaft. These PDFs do exist beyond the dynamic instability threshold and they correspond to forward whirl of the rotor. At rotation speeds just slightly above the instability threshold, the response PDF has integrable singularity at zero which corresponds to intermittency in the response.     

岩石可爆性神经网络研究

应用人工神经网络系统理论，采用机器学习的方法，建立了岩石的可爆性指数与岩体的爆炸漏斗体积Ｖ、大块率Ｋ＿１、平均合格率Ｋ＿２、小块率Ｋ＿３和波阻抗Ｚ之间的非线性映射关系，并将其用神经网络、网络连接权值矩阵和节点阈值向量分布式表达出来。对于新的岩石，网络采用并行推理的方法预报出其可爆性。实践表明，神经网络方法科学、具有较强的非线性动态处理的能力。     

Nonlinear reduced-order model for vertical sloshing by employing neural networks

The aim of this work is to provide a reduced-order model to describe the dissipative behavior of nonlinear vertical sloshing involving Rayleigh–Taylor instability by means of a feed forward neural network. A 1-degree-of-freedom system is taken into account as representative of fluid–structure interaction problem. Sloshing has been replaced by an equivalent mechanical model, namely a boxed-in bouncing ball with parameters suitably tuned with performed experiments. A large data set, consisting of a long simulation of the bouncing ball model with pseudo-periodic motion of the boundary condition spanning different values of oscillation amplitude and frequency, is used to train the neural network. The obtained neural network model has been included in a Simulink®  environment for closed-loop fluid–structure interaction simulations showing promising performances for perspective integration in complex structural system.

     

Investigation of intermittency measurement methods for transitional boundary layer flows

Three turbulent intermittency methods, namely the , TERA (turbulent energy recognition algorithm), and M-TERA (modified turbulent energy recognition algorithm) methods, for identifying the intermittent flow characteristics associated with boundary layer transition from laminar to turbulent were considered and compared. The data used were obtained from hot-wire measurements in transitional boundary layer flows on a concave surface with a 2-m radius of curvature and on a flat plate. Comparisons show that the and TERA methods are more sensitive to the choice of threshold constants than the M-TERA method. In terms of the intermittency distribution across the boundary layer, the values obtained by the and TERA methods are unrealistically high in the near-wall region, while those obtained by the M-TERA method are more realistic. In the outer boundary layer region and outside the boundary layer, the and M-TERA methods give reasonable intermittency values, whereas the TERA method produces unrealistically high values in the region outside the boundary layer. In addition, the M-TERA method provides a sharper definition of theend of transition.     

Probability distribution and conditional averaging in turbulent flows

The results of an experimental investigation of the turbulence characteristics in the plane mixing layer and in the wake behind a cylinder are given. Measurements are made of the distribution of the velocity and temperature probabilities, the intermittency coefficient, and the conditionally averaged values of the square of the velocity and temperature derivatives.Translated from Izvestiya Akademii Nauk SSSR, Mekhanika Zhidkosti i Gaza, No. 6, pp. 31–37, November–December, 1977.     

A new artificial neural network tracking technique for particle image velocimetry

 The analysis of Particle Image Velocimetry (PIV) data requires effective algorithms to track efficiently the particles suspended in the fluid flow. The artificial neural network algorithm method described here presents a new approach to solve this problem. Contrary to the classic cross correlation method, this new method does not require a large number of particles per frame, it can handle flows with large velocity gradients, and is suited for tracking images with multiple exposures as well as tracking through consecutive images. The algorithm was tested on synthetic and available experimental data to provide a thorough performance analysis. Received: 28 May 1996/Accepted: 25 December 1996     

Using a Lumped Parameter Dynamic Model of a Rock Bolt to Produce Training Data for a Neural Network for Diagnosis of Real Data

Ground anchorage systems are used extensively throughout the world as supporting devices for civil engineering structures such as bridges and tunnels. The condition monitoring of ground anchorages is a new area of research, with the long term objective being a wholly automated or semi-automated condition monitoring system capable of repeatable and accurate diagnosis of faults and anchorage post-tension levels. The ground anchorage integrity testing (GRANIT) system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration signals that arise from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. Novel artificial intelligence techniques are used in order to learn the complicated relationship that exists between an anchorage and its response to an impulse. The system has a worldwide patent and is currently licensed commercially.A lumped parameter dynamic model has been developed which is capable of describing the general frequency relationship with increasing post-tension level as exhibited by the signals captured from real anchorages. The normal procedure with the system is to train a neural network on data that has been taken from an anchorage over a range of post-tension levels. Further data is needed in order to test the neural network. This process can be time consuming, and the lumped parameter dynamic model has the potential of producing data that could be used for training purposes, thereby reducing the amount of time needed on site, and reducing the overall cost of the system's operation.This paper presents data that has been produced by the lumped parameter dynamic model and compares it with data from a real anchorage. Noise is added to the results produced by the lumped parameter dynamic model in order to match more closely the experimental data. A neural network is trained on the data produced by the model, and the results of diagnosis of real data are presented. Problems are encountered with the diagnosis of the neural network with experimental data, and a new method for the training of the neural network is explored. The improved results of the neural network trained on data produced by the lumped parameter dynamic model to experimental data are shown. It is shown how the results from the lumped parameter dynamic model correspond well to the experimental results.     

基于神经网络的差分方程快速求解方法

近年来, 人工神经网络(artificial?neural?networks, ANN), 尤其是深度神经网络(deep?neural?networks, DNN)由于其在异构平台上的高计算效率与对高维复杂系统的拟合能力而成为一种在数值计算领域具有广阔前景的新方法. 在偏微分方程数值求解中, 大规模线性方程组的求解通常是耗时最长的步骤之一, 因此, 采用神经网络方法求解线性方程组成为了一种值得期待的新思路. 但是, 深度神经网络的直接预测仍在数值精度方面仍有明显的不足, 成为其在数值计算领域广泛应用的瓶颈之一. 为打破这一限制, 本文提出了一种结合残差网络结构与校正迭代方法的求解算法. 其中, 残差网络结构解决了深度网络模型的网络退化与梯度消失等问题, 将网络的损失降低至经典网络模型的1/5000; 修正迭代的方法采用同一网络模型对预测解的反复校正, 将预测解的残差下降至迭代前的10?5倍. 为验证该方法的有效性与通用性, 本文将该方法与有限差分法结合, 对热传导方程与伯格方程进行了求解. 数值结果表明, 本文所提出的算法对于规模大于1000的方程组具有10倍以上的加速效果, 且数值误差低于二阶差分格式的离散误差.      

Solving Huxley equation using an improved PINN method

Although many effective methods for solving partial differential equations (PDEs) have been proposed, there is no universal method that can solve all PDEs. Therefore, solving partial differential equations has always been a difficult problem in mathematics, such as deep neural network (DNN). In recent years, a method of embedding some basic physical laws into traditional neural networks has been proposed to reveal the dynamic behavior of equations directly from space-time data [i.e., physics-informed neural network (PINN)]. Based on the above, an improved deep learning method to recover the new soliton solution of Huxley equation has been proposed in this paper. As far as we know, this is the first time that we have used an improved method to study the numerical solution of the Huxley equation. In order to illustrate the advantages of the improved method, we use the same network depth, the same hidden layer and neurons contained in the hidden layer, and the same training sample points. We analyze the dynamic behavior and error of Huxley’s exact solution and the new soliton solution and give vivid graphs and detailed analysis. Numerical results show that the improved algorithm can use fewer sample points to reconstruct the exact solution of the Huxley equation with faster convergence speed and better simulation effect.

     

Vortices for computing: the engines of turbulence simulation

Vortices have been described as the “sinews of turbulence”. They are also, increasingly, the computational engines driving numerical simulations of turbulence. In this paper, I review some recent advances in vortex-based numerical methods for simulating high Reynolds number turbulent flows. I focus on coherent vortex simulation, where nonlinear wavelet filtering is used to identify and track the few high energy multiscale vortices that dominate the flow dynamics. This filtering drastically reduces the computational complexity for high Reynolds number simulations, e.g. by a factor of 1000 for fluid–structure interaction calculations (Kevlahan and Vasilyevvon in SIAM J Sci Comput 26(6):1894–1915, 2005). It also has the advantage of decomposing the flow into two physically important components: coherent vortices and background noise. In addition to its computational efficiency, this decomposition provides a way of directly estimating how space and space–time intermittency scales with Reynolds number, Re ^α . Comparing α to its non-intermittent values gives a realistic Reynolds number upper bound for adaptive direct numerical simulation of turbulent flows. This direct measure of intermittency also guides the development of new mathematical theories for the structure of high Reynolds number turbulence.     

结构分析和设计中神经网络计算研究评述

综述了工程结构分析和设计中神经网络计算研究的现状与趋势,指出了进一步发展神经计算的策略及方向。研究结果表明神经网络计算是工程结构分析中一种很有发展潜力的新方法。     

爆破地震峰值预报神经网络研究

在分析某核电站一期地面爆破振动监测结果的基础上,用神经网络理论建立了爆破振动加速度峰值的预报模型,提出了神经网络模型预报加速度峰值的方法。将神经网络模型预报的结果与传统方法（经验公式法）预报的结果相比,前者的预报结果有明显的改善。     

Sensitivity analysis of freestream turbulence parameters on stagnation region heat transfer using a neural network

A neural network has been used to predict stagnation region heat transfer in the presence of freestream turbulence. The neural network was trained using data from an experimental study to investigate the influence of freestream turbulence on stagnation region heat transfer. The integral length scale, Reynolds number, all three components of velocity fluctuations and the vorticity field were used to characterize the freestream turbulence. The neural network is able to predict 50% of the test data within ±1%, while the maximum error of any data point is under 3%. A sensitivity analysis of the freestream turbulence parameters on stagnation region heat transfer was performed using the trained neural network. The integral length scale is found to have the least influence on the stagnation line heat transfer, while the normal and spanwise turbulence intensities have the highest influence.