Finding the buckling load of non-uniform columns using the iteration perturbation method

The aim of this study is to calculate the critical load of variable inertia columns. The example studied in this paper can be used as a paradigm for other non-uniform columns. The wavelength of equivalent vibratory system is used to calculate the critical load of the trigonometrically varied inertia column. In doing so, the equilibrium equation of the column is theoretically studied using the perturbation method. Accuracy of the calculated results is evaluated by comparing the solution with numerical results. Effect of improving the initial guess on the solution accuracy is investigated. Effects of varying parameters of the trigonometrically varied inertia and the uniformly tapered columns on their stability behavior are studied. Finally, using the so-called “perfectibility” parameter, two design goals, i.e., being lightweight and being strong, are studied for the discussed columns.     

W/Cu梯度功能材料的高热负荷性能研究

用等离子体喷涂和热压方法制作了W/Cu梯度功能材料(FGM)样品,用大功率ND∶YAG激光对其进行了高热负载模拟实验.结果表明,在100～400MW@m-2的瞬时(脉冲宽度为4ms)热负载下,经过200～700次热循环,未发现有W-Cu复合体开裂.在123MW@m-2的功率密度下作用700次,发现钨表面有再结晶现象及严重的晶界腐蚀和裂纹,再结晶的平均晶粒尺寸约为5～10μm,垂直于表面呈柱状结构,再结晶层厚度约20～30μm.由于激光的淬冷效应,晶粒生长的趋势并不明显.在398MW@m-2功率密度下出现了明显的腐蚀坑,坑内呈疏松的蜂窝结构,坑的边缘出现了明显沉积区,能谱分析表明沉积区集聚了大量的金属杂质.等离子体喷涂试样比热压试样更易产生晶界的断裂的裂纹.在相同的热负荷条件下,W/Cu FGM的重量损失低于石墨材料的重量损失.     

地区电力需求预测研究

本文在分析影响预测准确原因的基础上．指出预测工作必须定量与定性相结合．针对地区电力消费需求的特点，提出了适宜于定性分析的四个预测模型．运用上述模型，实际预测结果令人满意。     

Optimum load scheduling of nuclear-hydro-thermal power systems

This paper considers the problem of short-term optimal operation of nuclear-hydro-thermal electric power systems. The solution is obtained by use of a functional analytic optimization technique that employs the minimum norm formulation.A power system with an arbitrary number of generating stations is considered. The limited flexibility exhibited by the thermal nuclear reactors, when operating in a load-following mode, is accounted for by means of a model of the xenon concentration in their cores. The nonlinear effects induced by trapezoidal water reservoirs and the time delay of the water flow between upstream and downstream hydroplants is taken into consideration as well.A two-level iterative scheme of the feasible type is proposed for implementing the optimal solution.This work was supported in part by the National Research Council of Canada, Grant No. A-4146.     

Dynamic stability of a curved rail under a moving load

The stability of a curved rail under a constant moving load has been investigated using a linear theory; critical speeds of the moving load, and the dynamic rail deflections and rotation were calculated. The effect of the foundation was included through distributed linear springs. It was assumed that the moving load remains in constant contact with the rail and travels along a fixed path on the rail head.     

Dynamic Behavior of Rigid–Plastic Sector Plates

The paper studies the dynamic behavior of perfect rigid-plastic plates in the form of a sector with hinged or clamped sides under short-term intensive loads. Two dynamic deformation mechanisms are demonstrated. The dynamic equation is derived for each of the mechanisms. The realization conditions for the mechanisms are analyzed. Analytical expressions are derived for the ultimate (high) loads and the maximum residual deflection. Numerical examples are given     

Approximate importance sampling Monte Carlo for data assimilation

Importance sampling Monte Carlo offers powerful approaches to approximating Bayesian updating in sequential problems. Specific classes of such approaches are known as particle filters. These procedures rely on the simulation of samples or ensembles of the unknown quantities and the calculation of associated weights for the ensemble members. As time evolves and/or when applied in high-dimensional settings, such as those of interest in many data assimilation problems, these weights typically display undesirable features. The key difficulty involves a collapse toward approximate distributions concentrating virtually all of their probability on an implausibly few ensemble members.

After reviewing ensembling, Monte Carlo, importance sampling and particle filters, we present some approximations intended to moderate the problem of collapsing weights. The motivations for these suggestions are combinations of (i) the idea that key dynamical behavior in many systems actually takes place on a low dimensional manifold, and (ii) notions of statistical dimension reduction. We illustrate our suggestions in a problem of inference for ocean surface winds and atmospheric pressure. Real observational data are used.     

Adaptive modeling, adaptive data assimilation and adaptive sampling

For efficient progress, model properties and measurement needs can adapt to oceanic events and interactions as they occur. The combination of models and data via data assimilation can also be adaptive. These adaptive concepts are discussed and exemplified within the context of comprehensive real-time ocean observing and prediction systems. Novel adaptive modeling approaches based on simplified maximum likelihood principles are developed and applied to physical and physical–biogeochemical dynamics. In the regional examples shown, they allow the joint calibration of parameter values and model structures. Adaptable components of the Error Subspace Statistical Estimation (ESSE) system are reviewed and illustrated. Results indicate that error estimates, ensemble sizes, error subspace ranks, covariance tapering parameters and stochastic error models can be calibrated by such quantitative adaptation. New adaptive sampling approaches and schemes are outlined. Illustrations suggest that these adaptive schemes can be used in real time with the potential for most efficient sampling.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.     

Response time and vendor–assembler relationship in a supply chain

Relationships between an assembler and a vendor in a supply chain are investigated in two-period models when the assembler wants to reduce response time by incentive systems. The assembler may offer myopic or farsighted incentive contracts to the vendor, under short-term or long-term relationships. Incentive schemes, effort levels, and expected payoffs under different perspectives and relationships are examined. We find that a farsighted assembler provides the vendor with a higher incentive than a myopic assembler in the first period. A long (short)-term relationship is preferred if the value of farsightedness under a long-term relationship is greater (less) than the switching option value under a short-term relationship. We propose several sufficient conditions regarding which perspectives and relationships are preferred.