Numerical modeling of three-dimensional flow and pollutant dispersion around structures

This paper describes a numerical modeling approach that can be used to provide estimates of emissions at industrial sites. In particular the models presented are capable of simulating the wind flow and dispersion of airborne pollutants around surface-mounted structures such as buildings or building complexes. The calculational procedure in this approach consists of two sequential steps, namely: (i) prediction of the mean flow via a turbulent flow model; and (ii) employment of the calculated flow field to drive a particle-in-cell transport and diffusion model. A benchmark simulation is performed in which numerical results from the flow model are compared with other numerical models and with experimental data for flow over a backward-facing step. Results from three-dimensional simulations of flow and dispersion over a two-building complex are also presented.     

Particle modeling simulation of atmospheric dispersion using the MC-LAGPAR package

A Monte Carlo model and computer code (MC-LAGPAR) for simulating atmospheric transport and diffusion of plumes are described. The turbulent diffusion is simulated by the semi-random motion of Lagrangian particles. The particles are emitted by a point source and dispersed in a computational domain by pseudo-velocities derived from vertical profiles of meteorological variables.The MC-LAGPAR code includes the implementation of special algorithms for the simulation of a dynamic plume rise, chemical decay, deposition and resuspension effects. Furthermore, computer-graphics displays have been developed. The model, here used in its two dimensional version, is validated in the well-known case of homogeneous and stationary turbulence. In this case, we compared the concentration fields obtained by our model with those calculated by the known analytical solution. In both computations, the standard deviations of wind velocities are calculated according to the Taylor formulas.In the nonhomogeneous case, the vertical structure of turbulence is parameterized according to the scheme suggested by Hanna. As an example of the non-homogeneous case, we present numerical simulations in convective (unstable) conditions in which the influence of updraughts and downdraughts is empirically taken into account.     

Modelling of atmospheric pollutant transport and deposition in lakeshore environments

In order to study the effects of lakes on weather modification, pollutant transport and deposition, the hydrodynamic equations were solved numerically on a variable horizontal and vertical grid. Time splitting was applied to couple an explicit pseudospectral and an implicit finite difference scheme. The resulted model difference equations were used to predict the local wind, temperature, and pressure fields as well as the evolution of the atmospheric planetary boundary layer parameters. The modeling domain was centered around a lake to investigate the potential impact of nearby industrial sources. The situation studied was characteristic of summertime conditions, driven by a synoptic weather system with air moving from land, over a cooler lake surface. This flow is shown to become highly modified by a mesoscale meteorological phenomenon known as lake-breeze. It is also shown that the presence of a lake limits pollutant deposition over its length, due to stabilization and plume entrainment in the lake-breeze. On the hand, deposition was enhanced over the upwind land due to the lake-breeze effect, and the downwind shore due to intense mxing.     

基于GIS的大气污染扩散模拟的设计与实现

对于城市空气环境问题的管理和决策,大气污染扩散模拟系统提供了科学的依据.论述了一个基于GIS,采用Visual Basic.NET、组件MapObjects和组件Surfer8.0相结合的大气污染扩散模拟系统的设计与实现,同时介绍了大气污染扩散的模拟过程、系统的主要功能、MapObjects与Surfer8.0两个组件的部分属性和方法,并给出了实现系统的关键代码.     

傅立叶变换的收敛性与解析形式探讨

傅立叶变换自提出之日,便受到以严格著称的数学家们诟病。傅立叶变换之所以对近代工程和自然科学产生了深刻的影响,在于傅立叶变换符合实际情况,给工程应用带来了极大的便利。通过分析阶跃信号的傅立叶变换的存在性及数学推导过程,从数学角度和工程角度探讨信号傅立叶变换存在的条件。     

Robust joint modeling of mean and dispersion through trimming

The Maximum Likelihood Estimator (MLE) and Extended Quasi-Likelihood (EQL) estimator have commonly been used to estimate the unknown parameters within the joint modeling of mean and dispersion framework. However, these estimators can be very sensitive to outliers in the data. In order to overcome this disadvantage, the usage of the maximum Trimmed Likelihood Estimator (TLE) and the maximum Extended Trimmed Quasi-Likelihood (ETQL) estimator is recommended to estimate the unknown parameters in a robust way. The superiority of these approaches in comparison with the MLE and EQL estimator is illustrated by an example and a simulation study. As a prominent measure of robustness, the finite sample Breakdown Point (BDP) of these estimators is characterized in this setting.     

二维L-系统的推广及在植物模拟中的应用

弯曲枝条的模拟是植物模拟的重要组成部分,L-系统是植物模拟的重要方法之一。如果用L-系统模拟弯曲的枝条,则需要大量的产生式。通过在产生式中增加弯曲符号以及描述弯曲程度的曲率函数得到了函数L-系统。利用函数L-系统模拟植物,在不改变产生式个数的情况下,通过调整曲率函数能够绘制出相同拓扑结构,但整体形态差异较大的植物;利用函数L-系统模拟具有弯曲枝条的植物,产生式简洁,同时在编程时,容易控制枝条的长度。     

Decision process modeling across internet and real world by double helical model of chance discovery

A case is presented for the double helical processing of chance discovery — human and an automated data mining system co-work, each progressing spirally toward the creative reconstruction of ideas. Especially, the discovery of what we call chances, significant novel events, is realized in this process. The example shown here is an application to questionnaire analysis for understanding new behaviors of Internet users. Internet users are born and bred with face-to-face human relations in the real world, but their interactions with WWW are distilling new value-criteria, keeping personal real-world senses of rationality, empathy, ethics, etc. In our method for aiding the discovery based on the double-helix model, the in-depth interaction of the Internet, the fundamental (i.e., common both in the Internet and in the real world) characters and the behaviors of people are discussed with revealing unnoticed value-criteria. Yukio Ohsawa, Ph.D.: BS, U. Tokyo, 1990, MS, 1992, DS, 1995. Research associate Osaka U. (1995). Associate prof. Univ. of Tsukuba (1999-) and also researcher of Japan Science and Technology Corp (2000-). He has been working for the program com. of the Workshop on Multiagent and Cooperative Computation, Annual Conf. Japanese Soc. Artificial Intelligence, International Conf. MultiAgent Systems, Discovery Science, Pacific Asia Knowledge Discovery and Data Mining, International Conference on Web Intelligence, etc. He chaired the First International Workshop of Japanese Soc. on Artificial Intelligence, Chance Discovery International Workshop Series and the Fall Symposium on Chance Discovery from AAAI. Guest editor of Special Issues on Chance Discovery for the Journal of Contingencies and Crisis Management, Journal of Japan Society for Fuzzy Theory and intelligent informatics, regular member of editorial board for Japanese Society of Artificial Intelligence. Currently he is authoring book “Chance Discovery” from Springer Verlag, “Knowledge Managament” from Ohmsha etc. Yumiko Nara, Ph.D.: She graduated from Nara Women’s University in 1987 and obtained her Master and Ph.D. degrees from Nara Women’s University respectively in 1993 and 1996. From 1987 through 1990 she worked for Sumitomo Bank. She is at Osaka Kyoiku University as lecturer (1997–2001) and as associate professor (2002-). She serves as a member of The Japan Sociological Society, The Japan Association for Social and Economic Systems Studies, The Japan Society of Home Economics, and The Japan Risk Management Society. She is an editorial committee member of the journal of Social and Economic Systems Studies (2001-), and a council member of The Japan Risk Management Society (1997-). In 1997, she received research awards from The Japan Society of Home Economics and The Japan Risk Management Society for studies on risk management.     

Computational modeling and comparison of three co-laminar microfluidic mixing techniques

This study compares three common microfluidic mixing techniques: electroosmosis with patterned zeta potential, hydrodynamic focusing and physical constrictions. All three techniques provide a higher degree of mixing than a comparable channel without a mixer, but at the cost of higher power requirements. Of the three techniques, the electroosmotic mixer requires the greatest amount of power to produce a high degree of mixing, unless the channels are much smaller than those typical for microfluidic devices. The power requirement of the physical constriction mixer may be lowered by using multiple constrictions, with only a small loss in mixing effectiveness. The physical constriction mixer is recommended, since it has power requirements similar to the hydrodynamic focusing mixer but only requires the use of a single pump. However, if the mixing liquids contain particulates, a hydrodynamic focusing mixer may be preferred, because the physical constriction mixer may clog, depending on the particulate size.     

Theory and techniques of data mining in CGF behavior modeling

For the large quantity of data,rules and models generated in the course of computer generated forces (CGFs) behavior modeling,the common analytical methods are statistical methods based on the tactical rules,tactical doctrine and empirical knowledge.However,from the viewpoint of data mining,we can find many of these analytical methods are also each-and-every different data mining methods.In this paper,we survey the data mining theory and techniques that have appeared in the course of CGF behavior modeling f...     

Examination of thermo-physical and material property interactions in cereal foams by means of Boltzmann modeling techniques

Cereal foam is a high complex material undergoing several temperature-dependent processes under thermal treatment, such as phase transitions, biochemical reactions and structural changes. Simultaneous heat and mass transfer plays an important role to investigate optimization studies in cereal-based foams. In porous media such as cereal foams, thermal conduction is of minor impact on the overall heat transfer, since the major part of heat is transferred through the gas phase filled with water vapor. This becomes evident comparing the thermal diffusivities of solid and gaseous components of the foam, where the difference is in the order of five magnitudes. The objective of this study is to model the coupled heat and mass diffusion processes in cereal-based foam under thermal treatment by means of Lattice Boltzmann methods. The proposed model is then used to perform parameter variation studies, showing the impact of material property changes offering the possibility on optimizing heat transfer through the foam.     

Implementation of modeling and analysis techniques using both mainframe and microcomputers

This paper discusses an implementation context, for systems modeling and analysis, which takes advantage of the relative capabilities of both a microcomputer and a mainframe computer. A specific illustration is given to convey the nature and scope of interface requirements which must be accommodated.     

The Use of the Ritz Method and Laplace Transform for Solving 2D Fractional‐Order Optimal Control Problems Described by the Roesser Model

In this article a numerical solution is presented for a class of two‐dimensional fractional‐order optimal control problems (2D‐FOOCPs) with one input and two outputs. To implement the numerical method, the Legendre polynomial basis is used with the aid of the Ritz method and the Laplace transform. By taking the Ritz method as a basic scheme into account and applying a new constructed fractional operational matrix to estimate the fractional and integer order derivatives of the basis, the given 2D‐FOOCP is reduced to a system of algebraic equations. One of the advantages of the proposed method is that it provides greater flexibility in which the given initial and boundary conditions of the problem are imposed. Moreover, satisfactory results are obtained in just a small number of polynomials order. The convergence of the method is extensively investigated and finally two illustrative examples are included to show the validity and applicability of the novel proposed technique in the current work.     

Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors

Many previous studies have employed predictive models for a specific disease, but fail to note that humans often suffer from not only one disease, but associated diseases as well. Because these associated multiple diseases might have reciprocal effects, and abnormalities in physiological indicators can indicate multiple associated diseases, common risk factors can be used to predict the multiple associated diseases. This approach provides a more effective and comprehensive forecasting mechanism for preventive medicine. This paper proposes a two-phase analysis procedure to simultaneously predict hypertension and hyperlipidemia. Firstly, we used six data mining approaches to select the individual risk factors of these two diseases, and then determined the common risk factors using the voting principle. Next, we used the Multivariate Adaptive Regression Splines (MARS) method to construct a multiple predictive model for hypertension and hyperlipidemia. This study uses data from a physical examination center database in Taiwan that includes 2048 subjects. The proposed analysis procedure shows that the common risk factors of hypertension and hyperlipidemia are Systolic Blood Pressure (SBP), Triglycerides, Uric Acid (UA), Glutamate Pyruvate Transaminase (GPT), and gender. The proposed multi-diseases predictor method has a classification accuracy rate of 93.07%. The results of this paper provide an effective and appropriate methodology for simultaneously predicting hypertension and hyperlipidemia.     

Characterisation of nonlinear and linear time-varying systems by Laplace transformation

In this article we elaborate on various characterisations of nonlinear systems. Specifically, we focus on the frequency-domain characterisation of the class of single-input single-output (SISO) linear time-varying (LTV) systems. We use the model introduced by Wiener using Volterra functional and later elaborated by others. The application of the multidimensional Laplace transform (MDLT) method as a fundamental tool in analysing variable systems and understanding system dynamic behaviours is emphasised. In particular, applications of double or two-dimensional Laplace transform (2DLT) for the analysis and synthesis of LTV circuits and systems are detailed. Examples worked out that illustrate the method and demonstrate its validity in the frequency-domain.     

Forecasting of monthly river flow with autoregressive modeling and data-driven techniques

This study was conducted by using autoregressive (AR) modeling and data-driven techniques which include gene expression programming (GEP), radial basis function network and feed-forward neural networks, and adaptive neural-based fuzzy inference system (ANFIS) techniques to forecast monthly mean flow for K?z?l?rmak River in Turkey. The lagged monthly river flow measurements from 1955 to 1995 were taken into consideration for development of the models. The correlation coefficient and root-mean-square error performance criteria were used for evaluating the accuracy of the developed models. When the results of developed models were compared with flow measurements using these criteria, it was shown that the AR(2) model gave the best performance among all developed models and the GEP and ANFIS models had good performance in data-driven techniques.