数学解题中的分离例说

美国著名数学家G·波利亚在《数学的发现》中,曾对数学解题的探索性思维总结了一个图解模式(图1)即通过动员和组织、分离和组合、辨认和回忆题中的各种元素和因素,包     

竞争不可逆化学反应过程工艺特征数学模型的建立

讨论提出竞争不可逆反应过程工艺方案和建立过程工艺数学模型,包括反应物完全转化条件下主反应的选择率数学模型,过程稳定态数学模型,特定极限分离边界数学模型和极值边际数学模型.     

层流管流入口压力突然升高引起的水力瞬变的理论分析

研究了层流状态下管道入口压力突然升高引起的水力瞬变过程,建立了瞬态压力分布的偏微分方程和初边值条件,用分离变量法求得了压力的理论解．根据压力和流量间的约束关系,得到了关于流量的偏微分方程和初边值条件,用分离变量求得了瞬变过程流量分布理论解．最后,用特征线法(MOC)对该问题进行了数值求解,理论解和数值解吻合很好．     

含周期脉动的大密度比气液同轴射流的Floquet稳定性分析

基于周期脉动速度激励下气液同轴射流的数学模型,运用线性稳定性理论,采用Chebyshev配点法和Floquet理论,将含周期脉动分层流的Floquet稳定性分析扩展到大密度比的情况.研究了液铝-氮气射流的参数共振特性,分析了不同的物理参数对系统稳定性的影响,计算了实验工况并和实验结果进行了比较.     

正三角形排列管束结构流弹失稳流体力模型数值研究

研究了流弹失稳流体力模型.选取阻尼机理控制下的流弹稳定性问题为研究对象,在多种入口流速下对正三角形排列管束结构中单管可动情况的流致振动过程进行了模拟.用管子速度和位移的多项式函数作为流体力模型,结合流体力和管子位移数值模拟结果,计算了流体力系数.对不同流速工况下的流体力系数与流速之间的关系进行拟合,将流速的影响引入到流体力模型中.最终得到了与管子速度、位移以及入口流速相关的流体力模型.用建立起的流体力模型对管束结构流弹失稳临界流速进行了预测,结果较好.这种以管束结构流致振动数值仿真为基础,结合给定的函数形式建立起的流体力模型,能反映管束结构和流体相互作用过程中的主要特征,该模型对流弹失稳临界流速有一定的预测能力.     

广义分布参数系统的状态反馈稳定性问题(1)

关于系统的状态反馈稳定性问题的研究一直是现代控制理论研究的重要问题之一.广义分布参数系统是比分布参数系统更广的一类系统,在研究复合材料热导体中的温度分布等问题时会出现这样的系统.本文讨论了H ilbert空间中一阶广义分布参数系统的状态反馈稳定性问题.应用泛函分析及线性算子半群理论的方法给出了使闭环广义分布参数系统渐进稳定的充要条件,充分条件及状态反馈的构造性表达式.这对研究广义分布参数系统的状态反馈稳定性问题具有重要的理论价值.     

CO_2驱分层注气井注入系统数学模型及其应用

CO_2驱分层注气是控制CO_2气窜和提高开发效果的有效手段.确定合理的分层注气参数是CO_2驱分层注气现场试验的关键环节.本文在CO_2驱单管分层注气和同心双管分层工艺的基础上,通过分析注入CO_2在地面管道、井筒、气嘴及地层中的流动规律,分别建立了两种注气工艺注入系统的数学模型,利用节点系统分析方法对数学模型进行求解和敏感性分析,能够确定不同注气工艺条件下各层合理的注气压力和注气量.以吉林油田CO_2驱注气井为例,结合实际地层参数,确定了各层合理的注气参数,为CO_2驱分层注气的现场实施提供理论依据和指导.     

基于非线性冗余算法的凝汽器状态评价

凝汽器状态评价指标是具有不同量纲的特征参数,为避免对具有不同量纲的特征空间实施无量纲化变换造成的信息失真,隶属度转换模式成为凝汽器状态评价的基本模式.由于特征参数状态渐变连续,所以表征参数状态的指标隶属度是模糊隶属度.模糊隶属度表征模糊状态,模糊隶属度转换实现模糊状态转换.用冗余理论界定模糊隶属度转换的非线性,用非线性去冗算法实现模糊隶属度转换并建立凝汽器状态评价的非线性评价模型.     

资本市场的最佳投资组合

市场上有多种可提供投资者选择的资产。本文试图对各种收益和风险进行分析,在一定的标准下给出全部资产组合的效益前沿,即有效资产组合,为投资者提供参考。 在建立模型时,考虑到资本市场的实际情况,我们对题目的条件作了适当的简化和补充。由于用于投资的资金M很大,我们忽略了单位资产的交易费用u_i。同时我们允许从银行贷款进行投资,以增加投资的灵活性,我们把资产的平均收益率和风险损失率作为各种资产及组合的收益和风险地定量描述,用计算机模拟了各种可能的投资组合,得到了完整的风险——收益图,直观地给出了有效资产组合的区域,并给出了精确的计算方法计算资产组合集合的效率前沿。使不同类型的投资者都可以找到最佳的投资组合。 最后,我们指出了一些在模型中没有考虑进去的因素,并分析了这些因素可能对模型产生的影响,并提出了模型的改进方向,以满足对预测的可靠程度要求更高的投资者的需求。     

基于管柱式气液旋流分离器的井口多管束分离装置结构优化

单井计量是油田开发过程中的一项重要工作,而气液分离效果直接影响到计量结果的准确性.管柱式气液旋流分离器(GLCC)因体积小、效率高、适用范围宽而被广泛应用,但基于GLCC发展多级一体化分离装置一直被受到关注.考虑一级分离入口斜管倾角、一级分离入口形状、气相分离弯管仰角及二级分离管束规格,采用多相流Mixture模型和雷诺应力RSM模型,对基于GLCC的井口多管束分离装置结构形式进行了优化,并定量揭示了不同结构形式下的气液分离效率.结果表明,在经过GLCC一级旋流分离后,进入多管束二级分离单元,能够进一步改善气液分离效果,提高气液分离效率,但两级分离单元的结构特征均对分离性能具有显著影响,优化GLCC一级旋流分离入口斜管的倾角为30°,入口形状为圆形,相应可获得稳定的压力分布、较为均匀的流场迹线和更高的气液分离效率,优化气相分离弯管的仰角为35°、二级分离细管束数量与管径分别为6支和40mm,可使对应分离压降在3kPa,气液分离效率接近70%.     

Comparison of Prioritization Techniques Using Interhierarchy Mappings

This paper discusses a method of comparing different prioritization techniques that entails mapping the input data from one system to another. The ranking vectors output by the methods should then be comparable. As an illustration, a method that uses pairwise comparison, e.g. the Analytic Hierarchy Process (AHP) of Saaty, itself viewed as a mapping, is compared with a method using cross impact analysis, the Edinburgh Approach. The problem centres on an appropriate inverse of the process mapping. Although the discussion is restricted to simple bilevel hierarchies, useful insight is provided into the methodologies in general and, in particular, the problem of inverse inconsistency associated with rank reversals. A way in which the theory of AHP might be incorporated into a Differential Hierarchy Process is suggested.     

Numerical simulation based on fuzzy stochastic analysis

In this paper mathematical methods for fuzzy stochastic analysis in engineering applications are presented. Fuzzy stochastic analysis maps uncertain input data in the form of fuzzy random variables onto fuzzy random result variables. The operator of the mapping can be any desired deterministic algorithm, e.g. the dynamic analysis of structures. Two different approaches for processing the fuzzy random input data are discussed. For these purposes two types of fuzzy probability distribution functions for describing fuzzy random variables are introduced. On the basis of these two types of fuzzy probability distribution functions two appropriate algorithms for fuzzy stochastic analysis are developed. Both algorithms are demonstrated and compared by way of an example.     

Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks

Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is detrimental to generalizability of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.     

A philosophy for the modelling of realistic nonlinear systems

A nonlinear dynamical system is modelled as a nonlinear mapping from a set of input signals into a corresponding set of output signals. Each signal is specified by a set of real number parameters, but such sets may be uncountably infinite. For numerical simulation of the system each signal must be represented by a finite parameter set and the mapping must be defined by a finite arithmetical process. Nevertheless the numerical simulation should be a good approximation to the mathematical model. We discuss the representation of realistic dynamical systems and establish a stable approximation theorem for numerical simulation of such systems.

     

Multiplicity and Stability of Equilibrium States of Three-Dimensional Nonlinear System

The multiplicity and stability of the equilibrium states of a three-dimensional differential system with initial conditions and three cross terms are studied in this paper. The existence and multiplicity of equilibrium states are given under the different qualifications of parameters. Besides, the local stability of the equilibrium state is shown by analyzing the eigenfunction of the Jacobi matrix. The global stability of the equilibrium state is obtained by constructing the Lyapunov function. Furthermore, the numerical simulation intuitively reflected the relationship of variables and verified the correctness of theoretical analysis.     

A phenomenon of waiting time in phase change problems driven by radiative heat transfer

In the paper (J. Food Process Eng. 2008; in press) we emphasized that during a phase change process in which the heat input is driven by a radiation transfer mechanism, a peculiar phenomenon may occur, characterized by a temporary stop of the increase of the boundary temperature due to a sudden change of the heat transfer coefficient upon phase transition. This time interval is needed to allow the thermal properties of the surface to evolve toward a state that is compatible with the heat intake rate corresponding to the new phase. The occurrence of the waiting time is motivated and studied for a general one‐dimensional Stefan problem. Then an application is presented to the much complicated problem considered in (J. Food Process Eng. 2008; in press), namely, the model for frying process. Copyright © 2008 John Wiley & Sons, Ltd.     

Method of separation of variables and Hamiltonian system

In the theory of mechanics and/or mathematical physics problems in a prismatic domain, the method of separation of variables ususally leads to the Sturm–Liouville-type eigenproblems of self-adjoint operators, and then the eigenfunction expansion method can be used in equation solving. However, a number of important application problems cannot lead to self-adjoint operator for the transverse coordinate. From the minimum potential energy variational principle, by selection of the state and its dual variables, the generalized variational principle is deduced. Then, based on the analogy between the theory of structural mechanics and optimal control, the present article leads the problem to the Hamiltonian system. The finite-dimensional theory for the Hamiltonian system is extended to the corresponding theory of the Hamiltonian operator matrix and adjoint symplectic spaces. The adjoint symplectic orthonormality relation is proved for the whole state eigenfunction vectors, and then the expansion of an arbitrary whole state function vector by the eigenfunction vectors is established. Thus the range of classical method of separation of variables is considerably extended. The eigenproblem derived from a plate bending problem in a strip domain is used for illustration. © 1993 John Wiley & Sons, Inc.     

Computing input multiplicity in anonymous synchronous networks with dynamic faults

We consider the following problem: Each processor of the network has assigned a (not necessarily unique) input value. Determine the multiplicity of each input value. Solving this problem means any input-symmetric function (i.e., function not sensitive to permutations of its input values) can be computed. We consider an anonymous synchronous network of arbitrary topology, in which dynamic link faults [P. Fraigniaud, C. Peyrat, Inform. Process. Lett. 71 (1999) 115–119; N. Santoro, P. Widmayer, in: Proc. SIGAL'90, Tokyo, 1990, in: LNCS, Springer, Berlin, 1990, pp. 358–369] may occur.

An instance of this problem has been stated as an open problem by N. Santoro at the rump session of SIROCCO'98: Is it possible to distributively compute parity function (XOR) on anonymous hypercubes with dynamic faults?

We show that if the network size N (the number of processors) is known, the multiplicity of inputs (and thus any input-symmetric function) can be computed on any connected network. The time complexity depends on the details of the model and the amount of topological information, but it is always a low polynomial in N.     

Real-time nonlinear global state observer design for solar heating systems

Nowadays it is important to investigate and develop solar water heating systems as an environmentally friendly technology. For this reason we introduce a physically-based nonlinear mathematical model that applies to a wide range of solar heating systems. In commercial solar heating systems not all state variables are monitored by direct measurements, since some of them may be technically difficult or expensive to measure. For a better monitoring and more efficient control of the system it may be useful to estimate the unmeasured state variables.As a novelty, we apply a global nonlinear state observer to a solar domestic water heating system. The state observer has been established relatively recently in the field of control theory. The state observer we worked out enables us to estimate the unmeasured state variables in real-time. This observer is global in the sense that it works starting from any initial state. A further contribution of this work is a rather general algorithm for the practical application of the real-time estimation process, and we also give bounds of the estimation error and a practical method to decrease this error.Comparing calculated and measured values for a real particular solar heating system, we justify the usability of the state observer and the estimation process.On the basis of measured data, we show that the nonlinear mathematical model corresponding to the applied nonlinear observer is more accurate than the linear model corresponding to the classical linear Luenberger-type observer, so it is reasonable to apply the nonlinear observer.