Analysis of the filling capability to the microstructures in micro-injection molding

Micro-injection molding is an important fabrication process for polymer plastics with micro-features. In micro-injection molding of products with microstructures, the ability for the polymer melt to flow into the microstructures is a crucial factor for successful molding. An analytical model in micro-injection molding is constructed in this research. It has been reported that most of the filling in microstructure is done during the packing pressure. In this analytical model, the temperature of the polymer melt near the entrance of the microstructure at the end of mold filling is estimated first. With the temperature, we can calculate the injection distance into the microstructures of the mold insert during the packing stage. The model is compared with experimental results, and shows the feasibility. The experiment uses the LIGA-like lithography process to define the micro-feature and a micro-electroforming method to form the mold insert with the replicated micro-feature. The injection distance into the microstructures predicted by this analytical model shows reasonable result as compared to the experimental measurement.     

Control-oriented modeling of servo-pump driven injection molding machines in the filling and packing phase

Servo-valves or variable displacement pumps are typically used to control conventional hydraulic injection molding machines (IMMs). Recent developments in electrical drive technology allow to utilize servo-motor driven pumps instead, which is beneficial due to their higher energy efficiency. Their dynamic behavior, however, is significantly different compared to the conventional setup. Thus, currently used mathematical models and control concepts cannot be directly applied. This paper presents a computationally efficient and scalable mathematical model of the injection process for these servo-pump driven IMMs. A first-principles model of the injection machine is combined with a phenomenological model describing the injection process, i.e. the compression of the melt and the polymer flow into the mold. The proposed model is tailored to real-time applications and serves as an ideal basis for the design of model-based control strategies. The feasibility of the proposed model is demonstrated by a number of different experiments. They confirm a high model accuracy over the whole operating range for different mold geometries.     

CFD-based predictive control of melt temperature in plastic injection molding

A unique method of coupling computational fluid dynamics (CFD) to model predictive control (MPC) for controlling melt temperature in plastic injection molding is presented. The methodology is based on using CFD to generate, via open-loop testing, a temperature and input dependent system model for multi-variable control of a three-heater barrel on an injection molding machine. Results clearly show the benefit of temperature and input dependent system models for MPC control, and that CFD can be used to dramatically reduce the time associated with open-loop testing through physical experiments.     

Numerical study of transition in a cylindrical pipe flow

We present numerical results of transition in a smooth cylindrical pipe by small periodical suction and blowing (PSB) at the inlet of a laminar pipe flow at Reynolds number 3000 based on the maximum velocity of the laminar flow and radius of the pipe. The spatial development of the PSB disturbance is simulated by means of pseudo-spectrum element method. The transitional process is described in the paper that the disturbances are growing rapidly in a short part of the pipe after they develop gradually in sufficient long distance. When the rapid growth of disturbances occurs the time step of integration should be decreased and then the flow transits to turbulent.     

Exponential convergence in ‐Wasserstein distance for diffusion processes without uniformly dissipative drift

By adopting the coupling by reflection and choosing an auxiliary function which is convex near infinity, we establish the exponential convergence of diffusion semigroups with respect to the standard ‐Wasserstein distance for all . In particular, we show that for the Itô stochastic differential equation if the drift term b is such that for any , holds with some positive constants K₁, K₂ and , then there is a constant such that for all , and , where is a positive constant. This improves the main result in 14 where the exponential convergence is only proved for the L¹‐Wasserstein distance.     

Approximation and Simulation of the Distributions of Scan Statistics for Poisson Processes in Higher Dimensions

Given a Poisson process in two or three dimensions, we are interested in the scan statistic, i.e. the largest number of points contained in a translate of a fixed scanning set restricted to lie inside a rectangular area. The distribution of the scan statistic is accurately approximated for rectangular scanning sets, using a technique that is also extended to higher dimensions. The accuracy of the approximation is checked through simulation. This revised version was published online in July 2006 with corrections to the Cover Date.     

Simulation Study for the Clan of Ancestors in a Perfect Simulation Scheme of a Continuous One-Dimensional Loss Network

Perfect simulation of a one-dimensional loss network on ℝ with length distribution π and cable capacity C is performed using the clan of ancestors method. Previous works estimated the region of convergence of this scheme using a domination by a branching process. In this work, we show that the domination by the branching process is far from sharp and that there is room for improvement. Moreover, we derive an empirical relation concerning the critical value using simulation studies on the number of rectangles present in the clan of ancestors.      

Transient numerical simulation of a thermoelectrical problem in cylindrical induction heating furnaces

This paper concerns the mathematical modelling and numerical solution of thermoelectrical phenomena taking place in an axisymmetric induction heating furnace. We formulate the problem in a two-dimensional domain and propose a finite element method and an iterative algorithm for its numerical solution. We also provide a family of one-dimensional analytical solutions which are used to test the two-dimensional code and to predict the behaviour of the furnace under special conditions. Some numerical results for an industrial furnace used in silicon purification are shown. Dedicated to Mariano Gasca on his 60th birthday     

Analysis of dual-phase-lag heat conduction in cylindrical system with a hybrid method

The dual-phase-lag heat transfer model is applied to investigate the transient heat conduction in an infinitely long solid cylinder for an exponentially decaying pulse boundary heat flux and for a short-pulse boundary heat flux. A hybrid application of the Laplace transform method and the control volume scheme is used to obtain the numerical solutions. Comparison between the numerical results and the analytic solution for an exponentially decaying heat flux pulse evidences the accuracy of the present numerical results. Results further show that the present numerical scheme can overcome the mathematical difficulties to analyze such problems. Effects of the thermal lag ratio τ_q/τ_T, the shift time τ_q–τ_T, the function form of heating pulse, and geometry of medium on the behavior of heat transfer are investigated.     

A Mathematical Analysis of Technical Analysis

In this paper, we investigate trading strategies based on exponential moving averages (ExpMAs) of an underlying risky asset. We study both logarithmic utility maximization and long-term growth rate maximization problems and find closed-form solutions when the drift of the underlying is modelled by either an Ornstein-Uhlenbeck process or a two-state continuous-time Markov chain. For the case of an Ornstein-Uhlenbeck drift, we carry out several Monte Carlo experiments in order to investigate how the performance of optimal ExpMA strategies is affected by variations in model parameters and by transaction costs.     

Virtual lab in Modelica of a cement clinker cooler for operator training

Plant operator training plays a fundamental role in improving the energy efficiency of the cement manufacturing process and in reducing the CO₂ emission. A virtual lab of a clinker grate cooler, intended for the training of cement plant operators, has been developed. The grate cooler model has been derived from first principles, and has been validated by consulting cement industry experts, and comparing the simulated results with published data and available information from the cement industry. The model has been described in the Modelica language. The Interactive Modelica library has been used to develop the interactive user-to-model interface, and the communication between this interface and the model. The virtual lab, which is completely described in Modelica, has been simulated using the Dymola 6.1 modelling environment. The Interactive Modelica library can be freely downloaded from the website http://www.euclides.dia.uned.es/     

基于随机Petri网的邻避冲突演化过程模型及情景仿真分析——以垃圾焚烧发电厂为例

在中国城镇化发展过程中,因某些基础设施的选址与建设而引发的"邻避冲突"时有发生,针对这一问题,对该类冲突的影响因素及其演化过程进行深入而系统的分析。通过借鉴扎根理论思想及多案例研究,对影响冲突演化过程的主要因素进行提炼与归纳,并构建冲突演化过程系统图。然后基于随机Petri网理论,对冲突演化过程进行建模,并进行情景仿真分析。结果表明,政府单边决策、意见领袖的组织策划及与设施有关的负面信息传播扩散容易引发周边居民不良的情绪反应及抵制抗议行为;政府妥协有利于事件的平息,但会使周边居民对政府形成负面经验认知、对政府产生不信任以及对风险产生过高的感知;政府无效的风险沟通行为可能会导致冲突升级,但能减少周边居民对于政府的不信任及风险感知。     

成形过程数值模拟的非增量时一空算法*

本文针对计及几何、材料、接触摩擦等耦合作用的高度非线性的加工成形过程数值模拟和计算分析工作,建议了非增量时-空求解算法。本文的非增量算法,系在整个时间域和空间域上迭代求解,与常见的Newton-Raphson算法明显不同。本文所附算例,进一步说明了本算法的正确性和可行性。     

稀疏过程在保险公司破产问题中的应用

本文讨论适用于一类人寿保险和财产保险的风险过程 ,其中保单到达服从Poisson过程 ,而描述索赔发生的计数过程为保单到达过程的 p -稀疏过程。对此模型给出了破产概率的上界并对该上界进行了随机模拟 ,同时把所得结果与经典情形进行比较     

Circulant Embedding of Approximate Covariances for Inference From Gaussian Data on Large Lattices

Recently proposed computationally efficient Markov chain Monte Carlo (MCMC) and Monte Carlo expectation–maximization (EM) methods for estimating covariance parameters from lattice data rely on successive imputations of values on an embedding lattice that is at least two times larger in each dimension. These methods can be considered exact in some sense, but we demonstrate that using such a large number of imputed values leads to slowly converging Markov chains and EM algorithms. We propose instead the use of a discrete spectral approximation to allow for the implementation of these methods on smaller embedding lattices. While our methods are approximate, our examples indicate that the error introduced by this approximation is small compared to the Monte Carlo errors present in long Markov chains or many iterations of Monte Carlo EM algorithms. Our results are demonstrated in simulation studies, as well as in numerical studies that explore both increasing domain and fixed domain asymptotics. We compare the exact methods to our approximate methods on a large satellite dataset, and show that the approximate methods are also faster to compute, especially when the aliased spectral density is modeled directly. Supplementary materials for this article are available online.     

一类马氏过程在作战分析中的应用

本文应用随机过程的理论和方法定义了一类特殊的作战过程——纯灭战斗过程,研究并导出了该战斗过程的作战实力转移特征等．     

Analysis of particle methods for structured population models with nonlocal boundary term in the framework of bounded Lipschitz distance

Recently developed theoretical framework for analysis of structured population dynamics in the spaces of nonnegative Radon measures with a suitable metric provides a rigorous tool to study numerical schemes based on particle methods. The approach is based on the idea of tracing growth and transport of measures which approximate the solution of original partial differential equation. In this article, we present analytical and numerical study of two versions of Escalator Boxcar Train algorithm which has been widely applied in theoretical biology, and compare it to the recently developed split‐up algorithm. The novelty of this article is in showing well‐posedness and convergence rates of the schemes using the concept of semiflows on metric spaces. Theoretical results are validated by numerical simulations of test cases, in which distances between simulated and exact solutions are computed using flat metric. © 2014 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 30: 1797–1820, 2014     

A new model for classifying inputs and outputs and evaluating the performance of DMUs based on translog output distance function

In conventional data envelopment analysis it is assumed that the input versus output status of each chosen performance measures is known. In some conditions finding a statue of some variables from the point view of input or output is very difficult; these variables treat as both an input and output and are called flexible measures. This paper proposes a new model based on translog output distance function for classifying inputs and outputs and evaluating the performance of decision-making units by considering flexible measures. Monte Carlo simulation is applied to evaluate the presented model comparing with that of the recent model found in the literature. The result shows that the measure efficiencies of our model are statistically closer to true efficiencies and have higher rank correlation with true efficiencies. Also results obtained from simulated data show that there are high correlation between our model and that of the recent model.     

Simulation of First-Passage Times for Alternating Brownian Motions

The first-passage-time problem for a Brownian motion with alternating infinitesimal moments through a constant boundary is considered under the assumption that the time intervals between consecutive changes of these moments are described by an alternating renewal process. Bounds to the first-passage-time density and distribution function are obtained, and a simulation procedure to estimate first-passage-time densities is constructed. Examples of applications to problems in environmental sciences and mathematical finance are also provided.AMS 2000 Subject Classification: 60J65, 60G40, 93E30     

Generation of self-similar processes for simulation studies of telecommunication networks

It is generally accepted that self-similar processes may provide better models for teletraffic in modern telecommunication networks than Poisson processes. If stochastic self-similarity of teletraffic is not taken into account, it can lead to inaccurate conclusions about the performance of networks. Thus, an important requirement for conducting simulation studies of networks is the ability to generate long synthetic self-similar sequences of incremental processes, to transform them into interevent time intervals, and to do this accurately and quickly. A fast generator for count processes based on wavelets is described. Then a method for transformation of count processes into interevent processes proposed by Leroux and Hassan [1] and an alternative method, that is, inverting the empirical distribution directly, are studied. A case study is discussed to show how long sequences are needed in the steady-state simulation of queueing models with self-similar input processes. This is compared with simulation run lengths of the same queueing models fed by Poisson processes.