A coupled reactive chemical transport model:: mixing cell model with two solid phases and its application

A research tool for modeling the reactive flow and transport of groundwater contaminants in a one-dimensional scheme is presented. The use of a mixing cell concept with two solid phases for hydrological transport, and the incorporation of chemical reactions, recoil processes and phase transformation of minerals are some features of the model that make it a versatile research tool for the modeling of a wide variety of laboratory and field experiments. The treatment of chemical reactions is general so that the model can deal with aqueous complexation, adsorption, ion exchange, precipitation reaction, and decay reaction. The model has been applied to the analysis of uranium data obtained from the weathered zone of Koongarra uranium ore deposit in Australia, which illustrates some of these features.     

A flexible modeling framework for hydraulic and water quality performance assessment of stormwater green infrastructure

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructure (GI) practices. The framework conceptualizes GI practices using blocks (spatial features) and connectors (interfaces) representing functional components of a GI. The blocks represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, or a generic storage zone) and water quality constituents including chemical constituents and particles. The hydraulic module can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models. The particle transport module is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed, bound to particles, and undergo user-defined transformations. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating urban GI performance.     

Multicomponent reaction modelling using a stochastic algorithm

The numerical simulation of multicomponent reactive transport has become a dynamic field of subsurface hydrology. Efficient simulation models are necessary which are capable of representing a multitude of parameters influencing geochemical reactions. The mathematical structure of reactive transport problems in porous media is determined by the decision in the chemical model part whether reactions are kinetically controlled or whether a local equilibrium assumption is made between various species involved in the geochemical reactions. The assumptions made in deterministic models are not hold true for reactions involving chemical species of low concentration. Here the emergent phenomenon evolving out of the random fluctuations of the species is difficult to be captured. The stochastic behaviour of the physicochemical processes can be modelled at mesoscopic level by application of stochastic algorithms. The proposed model was verified by comparing the results of Gillespie’s algorithms with a deterministic solution for PCE and BTEX degradation reactions. Research of the authors is partly supported by the Deutscher Akademischer Austauschdienst (DAAD) and the Deutsche Forschungsgemeinschaft (German Research Foundation) under the grant Ko 1573/8-2, Germany.     

Syntax and consistent equation semantics of hybrid Chi

The hybrid χ (Chi) formalism integrates concepts from dynamics and control theory with concepts from computer science, in particular from process algebra and hybrid automata. It integrates ease of modeling with a straightforward, structured operational semantics. Its ‘consistent equation semantics’ enforces state changes to be consistent with delay predicates, that combine the invariant and flow clauses of hybrid automata. Ease of modeling is ensured by means of the following concepts: (1) different classes of variables: discrete and continuous, of subclass jumping or non-jumping, and algebraic; (2) strong time determinism of alternative composition in combination with delayable guards; (3) integration of urgent and non-urgent actions; (4) differential algebraic equations as a process term as in mathematics; (5) steady-state initialization; and 6) several user-friendly syntactic extensions. Furthermore, the χ formalism incorporates several concepts for complex system specification: (1) process terms for scoping that integrate abstraction, local variables, local channels and local recursion definitions; (2) process definition and instantiation that enable process re-use, encapsulation, hierarchical and/or modular composition of processes; and (3) different interaction mechanisms: handshake synchronization and synchronous communication that allow interaction between processes without sharing variables, and shared variables that enable modular composition of continuous-time or hybrid processes. The syntax and semantics are illustrated using several examples.     

MPEG VBR视频流—统计特性及其模型

该文分析了已有的MPEG VBR视频流模型及其缺点,并在此基础上对VBR视频流的统计特性进行了研究,实验结果表明,通过对整个视频流进行场景划分（聚类）,聚类间用Markov调制链建模,而每一聚类中独立的场景则可以采用TES模型基于GOP（Group Of Picture)而非帧进行建模,则该方法既能避免状态空间过大,又能避免帧间周期性的自相关,因而能够更好地拟合VBR视频流序列的一阶和二阶统计特性,同时,对独立场景的GOP分布函数可以采用Gamma函数进行拟合,自相关函数则可以采用双指数函数更好地拟合。     

New construction of highly nonlinear resilient S-boxes via linear codes

Highly nonlinear resilient functions play a crucial role in nonlinear combiners which are usual hardware oriented stream ciphers. During the past three decades, the main idea of construction of highly nonlinear resilient functions are benefited from concatenating a large number of affine subfunctions. However, these resilient functions as core component of ciphers usually suffered from the guess and determine attack or algebraic attack since the n-variable nonlinear Boolean functions can be easily given rise to partial linear relations by fixing at most n/2 variables of them. How to design highly nonlinear resilient functions (S-boxes) without concatenating a large number of n/2 variables affine subfunctions appears to be an important task. In this article, a new construction of highly nonlinear resilient functions is proposed. These functions consist of two classes subfunctions. More specially, the first class (nonlinear part) contains both the bent functions with 2 k variables and some affine subfunctions with n/2 − k variables which are attained by using [ n/2 − k, m, d] disjoint linear codes. The second class (linear part) includes some linear subfunctions with n/2 variables which are attained by using [ n/2, m, d] disjoint linear codes. It is illustrated that these resilient functions have high nonlinearity and high algebraic degree. In particular, It is different from previous well-known resilient S-boxes, these new S-boxes cannot be directly decomposed into some affine subfunctions with n/2 variables by fixing at most n/2 variables. It means that the S-boxes (vectorial Boolean functions) which use these resilient functions as component functions have more favourable cryptography properties against the guess and determine attack or algebraic attacks.     

Process situation assessment: From a fuzzy partition to a finite state machine

Process situation assessment plays a major role in supervision of complex systems. The knowledge of the system behavior is relevant to support operators in their decision tasks. For complex industrial processes such as chemical or petrochemical ones, most of supervision approaches are based on data acquisition techniques and specifically on clustering methods to cope with the difficulty of modeling the process. Consequently, the system behavior can be characterized by a state space partition. This way, situation assessment is performed online through the tracking of the system evolution from one class to another. Furthermore, a finite state machine that is a support tool for process operators is elaborated to model the system behavior. This article presents theoretical aspects according to which the intuition that the trajectory observation of a dynamical system by a sequence of classes, to which the actual state belongs, gives valuable information about the real behavior of the system is substantiated. Thus, practical aspects are developed on the state machine construction and illustrated by two simple applications in the domain of chemical processes.     

An integrated model suite for sediment and pollutant transport in shallow lakes

To assess effectively the impact of sediments and pollutants input to a water body, it is necessary to predict their spatial and temporal distributions in the water column and in the sediment substrate, resulting from physico-chemical processes such as advection, dispersion, aggregation, deposition, erosion and adsorption. Physical or mathematical representation of these processes requires a good estimate of the velocity field and circulation patterns induced by hydrodynamic forcing functions. The modeling strategy adopted was to link similarly structured two-dimensional, vertically averaged finite element models for hydrodynamics and sediment-pollutant transport. To enhance model use and interaction, these models were interfaced with pre- and post-processing modules. Using the same spatial grid and temporal scale, dynamic simulations were performed to investigate mercury pollution in a shallow lake. Results show that the integrated model suite provided a reasonable representation of the hydrodynamics and its influence on sediment and mercury transport processes.     

变计时过程ö变迁网模型及其应用研究

基于过程运行的离散标识(逻辑变量)和剩余时间连续标识(时间变量)，提出一种新的混合标识过程／变迁网——变计时过程／变迁网模型．基于该模型，混杂动力学系统离散事件的实时监控、连续子过程的实时调度等问题可得到有效解决．     

催化裂化过程稳态优化控制研究

催化裂化装置是一个高度非线性、时变、长时延、强耦合、分布参数和不确定性的复杂 系统.在研究其过程机理的基础上,定义了一种模糊神经网络用以建模,用自相关函数检验法检 验模型的正确性,再用改进的Frank-Wolfe算法进行稳态优化计算,并以一炼油厂催化裂化装 置为对象进行试验,研究其辨识、建模和稳态优化控制.这种模糊神经网络具有隐层数多、隐层 结点数多、泛化能力和逼近能力强、收敛速度快的优点,更突出的特点还在于可由输出端对输入 求导,为稳态优化计算提供了极大方便,它与改进的Frank-Wolfe算法相结合用于解决非线性 复杂生产过程的建模和稳态优化控制问题是可行的.     

轻型木结构住宅建筑CAD软件

针对目前国内无轻型木结构设计软件,且国外软件在规范和设计惯例等方面与国内实际的应用存在较大差距的问题,基于AutoCAD的ObjectARX开发一套轻型木结构住宅建筑CAD软件.该软件的模块架构将墙体、楼盖和屋盖等3个构件设计为平行层次,在构件层次之上再架设结构整体模块,处理构件间的彼此作用,很好地解决部分与整体的关系.运用面向对象编程方法进行软件开发,主要的核心类包括木构件基类、木构件聚合基类、木杆件类、墙体类、楼面类、桁架类、墙洞类和楼面洞口类等.通过实例介绍软件的主要功能,包括墙体和楼面建模,坡屋面和桁架建模,载荷布置与自动导算,墙体和楼面的结构计算以及结构施工图自动绘制等.     

The MesODiF package for gradient computations with the atmospheric model Meso-NH

The meso-scale meteorological model Meso-NH is able to simulate atmospheric events ranging from the meso-scale down to the micro-scale. It also allows for transport and diffusion of passive scalar quantities such as pollutants coming from the coupling with a chemical package. The MesODiF package was generated to complete Meso-NH with both a tangent linear code and an adjoint code. The differentiation work, realized with respect to the state variables, is done on the adiabatic part of the code. Physical parameterizations are not taken into account yet. The linearized codes are efficient both in time and memory consumptions. In particular, checkpoint schemes and an appropriate differentiation of the Leap-Frog scheme of Meso-NH allow the computation of gradients on simulations with a physical meaning at a low CPU-time cost. Sensitivity analysis and variational data assimilation experiments are now attainable by the Meso-NH model.     

Time-variant reliability-based design optimization using sequential kriging modeling

This paper presents a sequential Kriging modeling approach (SKM) for time-variant reliability-based design optimization (tRBDO) involving stochastic processes. To handle the temporal uncertainty, time-variant limit state functions are transformed into time-independent domain by converting the stochastic processes and time parameter to random variables. Kriging surrogate models are then built and enhanced by a design-driven adaptive sampling scheme to accurately identify potential instantaneous failure events. By generating random realizations of stochastic processes, the time-variant probability of failure is evaluated by the surrogate models in Monte Carlo simulation (MCS). In tRBDO, the first-order score function is employed to estimate the sensitivity of time-variant reliability with respect to design variables. Three case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.     

半Bent函数和多输出布尔函数的构造

半bent函数是一类非线性度几乎最优且平衡的布尔函数,它弥补了bent函数的一些不足,如变元个数可以是奇数,具有平衡性.半bent函数可用于对称密码系统的设计和CDMA系统中的正交可变扩频码的构造.本文利用不相交线性码构造了一类新的半bent函数,设输入维度为n,当n=2k+1时,将F2^n划分为2^k+1个[n,k]线性码和1个[n,k+1]线性码,通过从该码集中选取合适线性码作支撑集来构造新的半bent函数.另一方面,多输出布尔函数(向量值函数)在应用中的效率更高,因此其使用场景更为广泛.本文同时利用不相交线性码构造了(n,n-k)平衡的多输出布尔函数,其中n/3相似文献   

Toward improved simulation of river operations through integration with a hydrologic model

Advanced modeling tools are needed for informed water resources planning and management. Two classes of modeling tools are often used to this end–(1) distributed-parameter hydrologic models for quantifying supply and (2) river-operation models for sorting out demands under rule-based systems such as the prior-appropriation doctrine. Within each of these two broad classes of models, there are many software tools that excel at simulating the processes specific to each discipline, but have historically over-simplified, or at worse completely neglected, aspects of the other. As a result, water managers reliant on river-operation models for administering water resources need improved tools for representing spatially and temporally varying groundwater resources in conjunctive-use systems. A new tool is described that improves the representation of groundwater/surface-water (GW-SW) interaction within a river-operations modeling context and, in so doing, advances evaluation of system-wide hydrologic consequences of new or altered management regimes.     

Numerical modeling of ground-penetrating radar in 2-D using MATLAB

We present MATLAB codes for finite-difference time-domain (FDTD) modeling of ground-penetrating radar (GPR) in two dimensions. Surface-based reflection GPR is modeled using a transverse magnetic (TM-) mode formulation. Crosshole and vertical radar profiling (VRP) geometries are modeled using a transverse electric (TE-) mode formulation. Matrix notation is used in the codes wherever possible to optimize them for speed in the MATLAB environment. To absorb waves at the edges of the modeling grid, we implement perfectly matched layer (PML) absorbing boundaries. Although our codes are two-dimensional and do not incorporate features such as dispersion in electrical properties, they capture many of the important elements of GPR surveying and run at a fraction of the computational cost of more elaborate algorithms. In addition, the codes are well commented, relatively easy to understand, and can be easily modified for the user's specific purpose.     

Optimization of fuzzy partitions for inductive reasoning using genetic algorithms

Fuzzy Inductive Reasoning (FIR) is a data-driven methodology that uses fuzzy and pattern recognition techniques to infer system models and to predict their future behavior. It is well known that variations on fuzzy partitions have a direct effect on the performance of the fuzzy-rule-based systems. The FIR methodology is not an exception. The performance of the model identification and prediction processes of FIR is highly influenced by the discretization parameters of the system variables, i.e. the number of classes of each variable and the membership functions that define its semantics. In this work, we design two new genetic fuzzy systems (GFSs) that improve this modeling and simulation technique. The main goal of the GFSs is to learn the fuzzification parameters of the FIR methodology. The new approaches are applied to two real modeling problems, the human central nervous system and an electrical distribution problem.     

New results on strong practical stability and stabilization of discrete linear repetitive processes

Discrete linear repetitive processes operate over a subset of the upper-right quadrant of the 2D plane. They arise in the modeling of physical processes and also the existing systems theory for them can be used to effect in solving control problems for other classes of systems, including iterative learning control design. This paper uses a form of the generalized Kalman–Yakubovich–Popov (GKYP) Lemma to develop new linear matrix inequality (LMI) based stability conditions and control law design algorithms for the strong practical stability property. Relative to alternatives, the LMIs for stability have a simpler structure and it is not required to impose particular structures on the matrix variables. These properties are extended to control law design, including those where state vector access is not required. Illustrative numerical simulation examples conclude the paper.     

A data-based framework for fault detection and diagnostics of non-linear systems with partial state measurement

A novel framework based on the use of dynamic neural networks for data-based process monitoring, fault detection and diagnostics of non-linear systems with partial state measurement is presented in this paper. The proposed framework considers the presence of three kinds of states in a generic system model: states that can easily be measured in real time and in-situ, states that are difficult to measure online but can be measured offline to generate training data, and states that cannot be measured at all. The motivation for such a categorization of state variables comes from a wide class of problems in the manufacturing and chemical industries, wherein certain states are not measurable without expensive equipments or offline analysis while some other states may not be accessible at all. The framework makes use of a recurrent neural network for modeling the hidden dynamics of the system from available measurements and uses this model along with a non-linear observer to augment the information provided by the measured variables. The performance of the proposed method is verified on a synthetic problem as well as a benchmark simulation problem.