方形对称传输线TE模的色散特征曲线的数值计算

本文提出了一种计算方形对称传输线TE模的各模式的截止波数,以及根据各模式的截止波数计算各模式的色散曲线的方法。     

Measurement and calculation of gas compressibility factor for condensate gas and natural gas under pressure up to 116 MPa

The volumetric properties of two reservoir fluid samples collected from one condensate gas well and one natural gas well were measured under four groups of temperatures, respectively, with pressure up to 116 MPa. For the two samples examined, the experimental results show that the gas compressibility factor increases with the increase of pressure. But the influence of the temperature is related to the range of the experimental pressure. It approximately decreases with the increase of temperature when the pressure is larger than (45 to 50) MPa, while there is the opposite trend when the pressure is lower than (45 to 50) MPa. The dew point pressure was also determined for the condensate gas sample, which decreases with the increase of temperature. The capabilities of four empirical correlations and a thermodynamic model based on equation of state for describing gas compressibility factor of reservoir fluids under high pressure were investigated. The comparison results show that the thermodynamic model recommended is the most suitable for fluids whatever produced from high-pressure reservoirs or conventional mild-pressure reservoirs.     

Nonlinear vertical accelerations of a floating torus in regular waves

The longitudinal motions and vertical accelerations of a floating torus as well as wave motion inside the torus are studied by model tests in regular deep-water waves. Comparisons are made with linear and partly with second-order potential-flow theory for the smallest examined experimental wave height-to-wave length ratio 1/120. Reasonable agreement is obtained, in particular for the linear problem. The importance of 3D flow, hydroelasticity and strong hydrodynamic frequency dependency is documented. Experimental precision errors and bias errors, for instance, due to tank-wall interference are discussed. Numerical errors due to viscous effects are found to be secondary. Experiments show that the third and fourth harmonic accelerations of the torus matter and cannot be explained by a perturbation method with the wave steepness as a small parameter.     

A reduced simulation applied to the viscoelastic fatigue of polymers

The paper extends the use of the PGD method to viscoelastic evolution problems described by a large number of internal variables and with a large spectrum of relaxation times. The internal variables evolution is described by a set of linear differential equations that involve many time scales. The feasibility and the robustness of the method are discussed in the case of a polymer in a non-equilibrium state under creep and cyclic loading. The relationships between different time scales (loading and internal variables) are also discussed.     

Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model

In this paper, a model predictive control (MPC) scheme for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities, arising in the context of transport-reaction processes, is proposed. A spatial operator of a parabolic PDE system is characterized by a spectrum that can be partitioned into a finite slow and an infinite fast complement. In this view, first, Galerkin method is used to derive a set of finite dimensional slow ordinary differential equation (ODE) system that captures the dominant dynamics of the initial PDE system. Then, a Multilayer Neural Network (MNN) is employed to parameterize the unknown nonlinearities in the resulting finite dimensional ODE model. Finally, a Galerkin/neural-network-based ODE model is used to predict future states in the MPC algorithm. The proposed controller is applied to stabilize an unstable steady-state of the temperature profile of a catalytic rod subject to input and state constraints.     

Nitrile Rubber Reactor Operation Troubleshooting with Principal Component Analysis

Principal Component Analysis (PCA) is employed as a tool in order to demonstrate yet another application of the technique, and, most importantly, to show that results from the statistical multivariate technique do make physico-chemical sense. The operation of a typical emulsion copolymerization of acrylonitrile and butadiene (nitrile butadiene rubber, NBR) is used as an example of process troubleshooting. In more general terms, a statistical tool is used to aid process data analysis and process operation (recipe, product property) troubleshooting.

The goal is to produce consistent Mooney Viscosity (MV) among different batches. The observation is that varying induction times lead to Mooney Viscosity inconsistencies. Firstly, we show results from the application of PCA to process data. Secondly, we deal with an even more important (and often ignored) question by examining whether the trends indicated by PCA make process sense.     

集团统一审计、审计风险与组成部分重要性水平

针对集团审计的特性,运用贝叶斯定理构建集团审计模型GUAM,并用伽玛概率分布函数表示先验函数、后验函数与似然函数,用于规范集团各组成部分的重要性水平核算,将模型从单一组织扩展到集团层面的审计,并建立了各组成部分重要性水平与相对可变成本的计算公式,通过各组成部分的资产等标准分配权重,进而确定合适的先验水平使得集团整体的可变成本降低,以达到提高集团审计效率,通过集团层面的控制,使得在完成集团整体目标置信水平的基础上达到最佳审计效果的目的.     

Analysis of risk bounds in partially specified additive factor models

The study of worst case scenarios for risk measures (e.g. the Value at Risk) when the underlying risk vector (or portfolio of risks) is not completely specified is a central topic in the literature on robust risk measurement. In this paper we discuss partially specified factor models as introduced in Bernard et al. (2017) in more detail for the class of additive factor models which admit more explicit results. These results allow to describe in more detail the reduction of risk bounds obtainable by this method in dependence on the degree of positive resp. negative dependence induced by the systematic risk factors. The insight may help in applications of this reduction method to get a better qualitative impression on the range of influence of the partially specified factor structure.     

Non parametric estimation of the diffusion coefficients of a diffusion with jumps

In this article, we consider a jump diffusion process ${\left(X_t\right)}_{t\ge 0}$, with drift function $b$, diffusion coefficient $\sigma$ and jump coefficient ${\xi }^2$. This process is observed at discrete times $t=0,\Delta ,\dots ,n\Delta$. The sampling interval $\Delta$ tends to 0 and the time interval $n\Delta$ tends to infinity. We assume that ${\left(X_t\right)}_{t\ge 0}$ is ergodic, strictly stationary and exponentially $\beta$-mixing. We use a penalized least-square approach to compute adaptive estimators of the functions ${\sigma }^2+{\xi }^2$ and ${\sigma }^2$. We provide bounds for the risks of the two estimators.