管带式散热器热力学分析及参数优选设计

本文利用Fluent软件分析了DST3型号管带式散热器型号参数对散热散热性能的影响,优选了散热器参数,经实验验证优选参数散热器的散热效率得到了提高。     

加氢精制工艺全流程仿真培训系统

以80万t/a加氢精制装置全流程仿真培训系统的开发为例,在概述与分析加氢精制装置生产工艺的基础上,通过机理加经验的方法给出其主要设备即反应器和汽液分离器的数学模型,并通过了Aspenplus流程模拟软件验证.介绍了仿真培训系统的硬件结构和软件系统模块功能,给出了仿真培训系统仿CENTIUM CS-3000集散控制系统(DCS)操作界面、教师指令台、现场操作站和ESD(逻辑连锁工作)站的实现.该仿真培训系统已通过省级鉴定并投入使用.     

Unobserved components in ARCH models: An application to seasonal adjustment

The paper deals with unobserved components in ARIMA models with GARCH errors, in the context of an actual application, namely seasonal adjustment of the monthly Spanish money supply series. The series shows clear evidence of (moderate) non-linearity, which does not disappear with simple outlier correction. The GARCH structure explains reasonably well the non-linearity, and this explanation is robust with respect to the GARCH specification. We look at the time variation of the standard error of the adjusted series estimator and show how it can be measured. Next, we look at the implications this variation has on short-term monetary control. The non-linearity seems to have a small effect in practice. It is further seen that the conditional variance of the GARCH process may, in turn, be decomposed into components. In fact, the conditional variance of the money supply series is the sum of a weak linear trend, a strong non-linear seasonal component, and a moderate non-linear irregular component. This information has policy implications: for example, there are periods in the year when policy can be more assertive because information is more precise. Finally, looking at the non-linear components of the money supply it is seen how linear combinations of non-linear series can produce series that behave linearly.     

Forecasting the Antwerp maritime traffic flows using transformations and intervention models

This article uses univariate time-series models with data transformations and intervention models to forecast the volumes of twenty-two maritime traffic flows in the port of Antwerp which are expressed in tonnes. The models obtained produce forecasts that are a substantial improvement over those obtained with unadjusted data. The models also provide useful insight into the behaviour of maritime traffic flows during the period 1971–82.     

Existence of macroscopic spatial superpositions in collapse theories

It is shown that the collapse dynamics in the CSL model will entangle two independent systems under certain conditions, and their state after collapse may be an entangled superposition of spatially separated states. However, since the conditions can hardly be satisfied in reality, the occurrence of such superpositions is very improbable, and thus collapse theories still provide a promising solution to the measurement problem.     

内燃机工作过程多维模型计算机仿真平台设计与实现

设计了内燃机工作过程仿真集成化PC平台.以缸内流体动力学为基础,耦合反映缸内工作过程的喷射、燃烧和碳烟生成等子模型构建了平台的多维模型;通过设置Compaq Visual Fortran软件的双精度运行环境,在PC微机上成功编译了KIVA-3V源代码;然后通过设计可视化模块,实现了平台计算与可视的集成化功能.试验分析表明:仿真平台能模拟内燃机缸内工作过程,准确反映客观物理因素对工作过程的影响,仿真误差小于5%,可以在内燃机行业推广.     

状态方程计算高分子共混物的比体积

基于一个简单的非均核链流体分子热力学模型,采用不同形式的微扰项,可构筑不同形式的高分子共混物系统的状态方程。方程已被应用于关联聚苯乙烯/聚2,6-二甲基苯醚(PS/PPO)、聚苯乙烯/聚乙烯甲醚(PS/PVME)、聚苯乙烯/聚丁二烯(PS/PBD)、聚醚砜/聚氧乙烯(PESP/PEO)等高分子共混物的比体积。结果显示,用一个与温度无关的二元相互作用可调参数可满意关联所选体系的比体积,并可预测所选体系的比体积。     

环境场特征与暴雨之间的关系浅析

利用2002年6-7月香港天文台业务区域谱模式(ORSM)的物理量预报资料,通过对暴雨发生时的平均高度场、温度场、湿度场、经向及纬向风速场特征的分析,探讨了我国南方形成持续强降水的大尺度环流形势及物理条件.通过对暴雨带附近的热力场和动力场的研究表明：温度场上,在大气中低层,暴雨带处于高温区南侧相对的冷区,且温度梯度较小,而在暴雨区北侧,温度梯度较大;湿度场上,暴雨区落在高湿度带中,并且在大气低层,暴雨带位于南北相对湿度对比带的南侧;而在风速场上,在大气低层,暴雨带附近为东西风及南北风的复合带.     

用微分方程方法研究红绿灯控制下的交通流模型

红绿灯模型是交通流动力学模型研究中一个重要而困难的问题,首先给出交通流模型,然后给出红绿灯交通流模型与特征线的求解,根据红绿灯模型与特征线意义,对交通流的红绿灯转换周期的设置具有重要意义．     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.