Load Extraction from Photoelastic Images Using Neural Networks

Photoelastic materials develop colored fringes under white light when subjected to mechanical stresses, which can be viewed through a polariscope. This technique has traditionally been used for stress analysis of loaded components, however, this can also be potentially used in sensing applications where the requirement may be measurement of the stimulating forces causing the generation of fringes. This leads to inverse photoelastic problem where the developed image can be analyzed for the input forces. However, there could be infinite number of possible solutions which cannot be determined by conventional techniques. This paper presents neural networks based approach to solve this problem. Experiments conducted to prove the principle have been verified with theoretical results and finite element analysis of loaded specimens. The developed technique, if generalized, can be implemented for whole-field analysis of the stress patterns involving complex fringes under different loading conditions. This can also provide direct visualization of the stress field, which may find application in a variety of specialized areas including biomedical engineering and robotics.

结构紧固连接件损伤的神经网络诊断研究

在结构紧固连接件损伤诊断中采用了函数连接型神经网络。与常用的ＢＰ网络不同，函数连接网络通过对输入模式的增强来实现复杂模式的分类。它借助某种函数变换，将原始的模式特征向量变换成另一个更高维的形式，使原始空间里不能区分的类别，在这个高维空间中得以区分，减少了神经网络的层数，提高了网络的学习速度。诊断时选用正弦激振下结构中多个位置上传感器稳态响应的幅值构成表征损伤的特征向量，简化了特征提取的过程。数值模拟和实验结果表明，这一方法能对多个位置的损伤进行准确的识别     

Existence and Uniqueness for Stochastic 2D Euler Flows with Bounded Vorticity

The strong existence and the pathwise uniqueness of solutions with \({L^{\infty}}\)-vorticity of the 2D stochastic Euler equations are proved. The noise is multiplicative and it involves the first derivatives. A Lagrangian approach is implemented, where a stochastic flow solving a nonlinear flow equation is constructed. The stability under regularizations is also proved.     

A Turbulent-Wake Estimation Using Radial Basis Function Neural Networks

An estimation of the fluctuations in the passive-tracer concentration for the turbulent wake behind an airfoil is presented. The estimation is based on experimental modelling using Radial Basis Function Neural Networks. For the experiment the fluctuations of the concentration in the turbulent wake were recorded with a visualization method. The records of the concentration in the selected regions of the turbulent wake were used as the input and output regions for the training and estimation with neural networks. The uncertainty of the estimation increased with increasing distance between the input and the output regions. The power spectra, the spatial correlation functions and the profiles of the concentration were calculated from the measured and estimated fluctuations of the concentration. The measured and estimated concentration power spectra were in reasonable agreement. The measured and estimated spatial correlation functions and the profiles of the concentration showed a similar agreement.     

一种基于神经网络的传感器故障诊断方法

提出了基于两级神经网络结构的多传感器斜置组件的故障诊断方法,以消除传感器安装误差、刻度系数误差以及常值偏差对故障检测与隔离准确性的影响。与基于参数估计的奇偶向量补偿方法相比,这种方法不需要各项误差的动态模型和噪声的统计特性。     

基于神经网络改进WENO-Z重构方法

本文通过使用深度神经网络对WENO-Z格式的非线性权重进行改进,提出了一种新的WENO-Z-NN格式．新格式首先用神经网络去随机扰动有限体积系数集,然后用代表偏微分方程解的波形生成的数据,采用L2正则化来学习扰动的最优函数,最后引入测试函数并结合最小总变差和最小总偏差作为评估依据进行后处理,从而得到新的权重．一维波动方程和一维Euler方程的数值结果表明,无论是在粗网格还是在细网格,本文所提出的WENO-Z-NN格式的激波捕捉能力明显优于传统的WENO-Z和WENO-JS-NN格式．     

Earthquake Forecasting Using Neural Networks: Results and Future Work

In 1994, a new earthquake forecasting method was developed, that integrated in a neural network several forecasting tools that had been originally developed for financial analysis. This method was tested with the seismicity of the Azores, predicting the July, 1998, and the January, 2004, earthquakes, albeit within very wide time and location windows. Work is beginning to integrate physical precursors in the neural network, in order to narrow the forecasting windows     

用人工神经网络预测摩擦学系统磨损趋势

人工神经网络具有高度的并行分布式、联想记忆、自组织及自学习能力和极强的非线性映射能力，在许多领域显示了广阔的应用前景．但是，将神经网络用于摩擦学行为预测的研究报道却还鲜见．在对基于神经网络的单变量时间序列预测方法与过程进行分析之后，提出了摩擦学系统磨损趋势神经网络预测模型．采用定量铁谱参数中的总磨损Ｑ作为预测磨损趋势的特征参数，讨论了磨损趋势神经网络预测的单步预测法和多步预测法，并用其对ＣＤ４０齿轮泵的磨损趋势进行了预测，预测值与实测值吻合较好     

基于神经网络的结构变形估计和形状控制

准确的变形估计是智能结构形状控制的前提。本文基于人工神经网络（ＡＮＮ）方法设计了智能桁架结构的变形估计器和形状控制器，根据结构系统有限数目的测量值可以估计结构变形并用于形状控制。该方法同时适于处理结构非线性问题。算例表明方法的可行性与有效性。     

Assessment of Delayed DES and Improved Delayed DES Combined with a Shear-Layer-Adapted Subgrid Length-Scale in Separated Flows

A comparative study is conducted between the original versions of Delayed Detached-Eddy Simulation (DDES) and Improved DDES (IDDES) and these approaches combined with a new (shear layer adapted) definition of the subgrid length-scale recently proposed in Shur et al. (Flow Turbul. Combust. 95(4), 709–737, 2015). This definition is aimed at accelerating the transition to resolved turbulence in separated shear-layers, which significant delay is typical of the non-zonal hybrid RANS-LES models, in general, and DES-like approaches, in particular. An objective of the study is widening the validation database of the new solutions-dependent definition of the length-scale compared to that employed in the original work of Shur et al. In order to reach this, three different complex separated flows with well-understood flow physics were considered, which all are widely used for the validation of different CFD approaches. These flows are: a flow with non-fixed pressure-induced separation and reattachment (wall-mounted hump), a massively separated flow (NACA 0021 airfoil beyond stall), and a supersonic separated flow (wake behind a cylindrical forebody). The results of simulations suggest that the DDES and IDDES models combined with the shear-layer adapted subgrid length-scale perform according to their design (no unforeseen interactions of the shear-layer adapted length-scale with the empirical functions involved in the DDES and IDDES formulations are observed) and considerably mitigate the delay of transition from fully modeled to partially resolved turbulence in the separated shear layers compared to the standard DES definition of the length-scale (maximum local grid-spacing).     

基于粗集—神经网络的磨粒模式识别

应用粗集理论中最小约简的近似算法 ,对磨粒的形状参数进行约简 ,找出判断磨粒模式的形状参数长短轴比 Rt和圆度 Rd,同时采用这些参数训练神经网络以进行磨粒模式识别 .结果表明 :应用此算法 ,对 63个已知样本和历时 2年多对柴油机 1 4 5个润滑油油样进行制谱分析判断 ,准确率在 90 %以上 ,比原来用模糊识别的准确率提高了约 1 0个百分点 ;使用 BP网络减化了网络结构 ,使网络的训练速度加快 ,整个系统变得简单、可靠、有效     

结构试验机的神经网络内模自适应跟踪控制系统

针对结构试验系统的非线性和不确定性特性，研究了一种基于神经网络的非线性内模自适应加载控制方法。引入的神经网络内模可跟踪学习对象的时变动力学，控制器的设计较少依赖于对象的先验知识，控制参数的调整是基于被控过程的测量信息，利用导出的神经网络算法来实现的。实验结果证明该系统具有良好的控制效果。     

Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks

Transport in Porous Media - We present a reduced-order modeling technique for subsurface multi-phase flow problems building on the recently introduced deep residual recurrent neural network...     

Subgrid Scale Modeling in Large-Eddy Simulation of Turbulent Combustion Using Premixed Flamelet Chemistry

Large-eddy simulation (LES) of turbulent combustion with premixed flamelets is investigated in this paper. The approach solves the filtered Navier–Stokes equations supplemented with two transport equations, one for the mixture fraction and another for a progress variable. The LES premixed flamelet approach is tested for two flows: a premixed preheated Bunsen flame and a partially premixed diffusion flame (Sandia Flame D). In the first case, we compare the LES with a direct numerical simulation (DNS). Four non-trivial models for the chemical source term are considered for the Bunsen flame: the standard presumed beta-pdf model, and three new propositions (simpler than the beta-pdf model): the filtered flamelet model, the shift-filter model and the shift-inversion model. A priori and a posteriori tests are performed for these subgrid reaction models. In the present preheated Bunsen flame, the filtered flamelet model gives the best results in a priori tests. The LES tests for the Bunsen flame are limited to a case in which the filter width is only slightly larger than the flame thickness. According to the a posteriori tests the three models (beta-pdf, filtered flamelet and shift-inversion) show more or less the same results as the trivial model, in which subgrid reaction effects are ignored, while the shift-filter model leads to worse results. Since LES needs to resolve the large turbulent eddies, the LES filter width is bounded by a maximum. For the present Bunsen flame this means that the filter width should be of the order of the flame thickness or smaller. In this regime, the effects of subgrid reaction and subgrid flame wrinkling turn out to be quite modest. The LES-results of the second case (Sandia Flame D) are compared to experimental data. Satisfactory agreement is obtained for the main species. Comparison is made between different eddy-viscosity models for the subgrid turbulence, and the Smagorinsky eddy-viscosity is found to give worse results than eddy-viscosities that are not dominated by the mean shear. Paper presented on the Eccomas Thematic Conference Computational Combustion 2007, submitted for a special issue of Flow, Turbulence and Combustion.     

基于RBF网络的有源噪声控制

提出一种神经自适应噪声有源控制（ANC）的方法。应用RBF（Radial Basis Function)网络对噪声进行有源控制。针对RBF的网络特点,使用递阶遗传算法确定网络参数（连接权、隐节点中心和宽度），同时解决了网络拓扑结构的优化训练。利用RBF网络的有源噪声控制系统用于三维空间传播的宽频带空调噪声的降噪获得了良好的效果。     

Subgrid length-scales for large-eddy simulation of stratified turbulence

The influence of buoyancy on the length-scales for the dissipation rate of kinetic energy, and for momentum, heat, and other scalar transport has to be known for subgrid-scale (SGS) models in a large-eddy simulation (LES). For the inertial subrange, Lilly (1967) has shown that grid spacing is the relevant length-scale for SGS effects. Deardorff (1980) proposed to reduce all the length-scales for stable stratification. Numerical and experimental data show, however, that the dissipation length-scale may strongly increase in stable layers with little shear. Lumley's (1964) theory for the energy spectrum in a stratified fluid also suggests such an increase. In this paper we apply the analysis of previous algebraic second-order closure SGS models, parameter studies with different length-scale models in LES, and the analysis of direct simulations of sheared and unsheared stably stratified homogeneous turbulence. These analyses show advantages of first-order closures for LES and suggest that the limiting effect of stratification should only be applied to the length-scales of vertical eddy-diffusivities of heat and scalars but not to those of momentum and dissipation.Dedicated to Professor J.L. Lumley on the occasion of his 60th birthday.This work was supported by the Deutsche Forschungsgemeinschaft.     

Generalized Wall Functions for High-speed Separated Flows Over Adiabatic and Isothermal Walls

We present the extension of our wall-laws developed for low-speed flows to super-and hyper-sonic configurations. In particular, we are interested in flows over isothermal walls and in the modeling of heat transfer. We recall the main steps of the development:

?Obtaining generalized wall functions for low-speed fluids, valid for all y ⁺.

?Taking into account transversal effects in wall-laws.

?Accounting for the compressible feature of the flow on adiabatic walls without using information on the local boundary layer structure, for instance its thickness, but only using information available at the fictitious wall.

?Taking into account thermal effects on isothermal walls. In particular, the heat flux at the real wall is obtained by an a posteriori evaluation using information at the fictitious one.

?Only using information available on unstructured meshes and avoiding the information coming from a Cartesian hypothesis for the mesh in near-wall regions.

These ingredients are validated on hyper-sonic configurations on adiabatic and isothermal walls for expansion and compression ramps.     

Compound Wall Treatment for RANS Computation of Complex Turbulent Flows and Heat Transfer

We present a generalised treatment of the wall boundary conditions for RANS computation of turbulent flows and heat transfer. The method blends the integration up to the wall (ItW) with the generalised wall functions (GWF) that include non-equilibrium effects. Wall boundary condition can thus be defined irrespective of whether the wall-nearest grid point lies within the viscous sublayer, in the buffer zone, or in the fully turbulent region. The computations with fine and coarse meshes of a steady and pulsating flow in a plane channel, in flow behind a backward-facing step and in a round impinging jet using the proposed compound wall treatment (CWT) are all in satisfactory agreement with the available experiments and DNS data. The method is recommended for computations of industrial flows in complex domains where it is difficult to generate a computational grid that will satisfy a priori either the ItW or WF prerequisites.