窄带随机噪声激励下线性碰撞系统的响应

研究了单自由度线性单边碰撞系统在窄带随机噪声激励下的次共振响应问题.用Zhurav-lev变换将碰撞系统转化为连续的非碰撞系统,然后用随机平均法得到了关于慢变量的随机微分方程.在约束距离为0时,用矩方法给出了系统响应幅值二阶矩的解析表达式.在约束距离不为0时,近似地得到了系统响应幅值二阶矩的解析表达式.讨论了系统阻尼项、窄带随机噪声的带宽和中心频率以及碰撞恢复系数等参数对于系统响应的影响.理论计算和数值模拟表明,系统响应幅值将在激励频率接近于次共振频率时达到最大,而当激励频率逐渐偏离次共振频率时,系统响应迅速衰减.数值模拟表明提出的方法是有效的.     

含有约束的两个状态变量系统的转迁集计算

周期解的分岔广泛存在于实际的非线性动力学系统中.该文对两个状态变量系统的约束分岔进行了讨论.在约束条件下系统将产生新的转迁集.此外,以一个二维系统为例,对含有约束条件和不含有约束条件的分岔特性进行了比较.所得的结果可以为系统的设计和参数选择提供理论依据.     

匀速转动内悬臂梁系统的建模与分岔研究

应用广义Hamilton原理建立旋转刚环内接悬臂梁系统在材料本构关系为线性时Rayleigh梁大挠度非线性动力学模型,并讨论当假定梁为Euler-Bernoulli型时的蜕化结果.研究表明,上述两种模型对系统的临界分岔值及分岔解静态没有影响.据此,采用了假设模态法解析地研究了匀速转动的Euler-Bernoulli梁模型的分岔行为,得到两种在物理上存在的分岔类型.为校验解析分析的结果,使用了有限元及打靶法两种数值研究工具.计算结果肯定了研究结论.     

约束含参分岔问题的分类

约束边界与分岔参数有关的约束分岔问题,称为约束含参分岔问题.通过引入适当的变换,将约束含参分岔问题转化为新变量的非约束分岔问题,推导出了约束含参分岔问题转迁集的一般形式,结果表明只有约束分岔集受约束含参的影响,其它转迁集与不含参约束分岔的转迁集相同.以含参约束树枝分岔为例分析了此类问题的分岔分类,讨论了约束含参对分岔分类的影响.     

有界噪声参激一类三维系统的矩Liapunov指数

对于一类三维中心流形上受有界噪声参激的余维2分岔系统,计算了它的矩Liapunov指数.根据随机动力系统理论,首先建立了系统矩Liapunov指数求解的特征值问题,然后由奇异摄动法,得到了弱噪声展开的矩Liapunov指数的二阶渐近解析表达式和数值结果.接着进一步研究了有界噪声和系统参数对矩Liapunov指数和稳定指标的影响.结果表明:系统的随机稳定性有被有界噪声加强的可能性.     

能源价格系统在随机干扰作用下的分岔研究

采用Khasminskii极限定理,随机平均法和FPK方程,研究了能源价格系统在随机干扰作用下的Hopf分岔特性,得到了分岔参数,并讨论了分岔参数对系统性态的影响.进而得出能源经济系统的相关结论.     

非线性颤振系统中既是超临界又是亚临界的Hopf分岔点研究

研究了二元机翼非线性颤振系统的Hopf分岔.应用中心流形定理将系统降维,并利用复数正规形方法得到了以气流速度为分岔参数的分岔方程.研究发现,分岔方程中一个系数不含分岔参数的一次幂,故使得分岔具有超临界和亚临界双重性质.用等效线性化法和增量谐波平衡法验证了所得结果.     

一类双面碰撞振子的对称性尖点分岔与混沌

讨论了一类单自由度双面碰撞振子的对称型周期n-2运动以及非对称型周期n-2运动.把映射不动点的分岔理论运用到该模型,并通过分析对称系统的Poincaré映射的对称性,证明了对称型周期运动只能发生音叉分岔.数值模拟表明:对称系统的对称型周期n-2运动,首先由一条对称周期轨道通过音叉分岔形成具有相同稳定性的两条反对称的周期轨道;随着参数的持续变化,两条反对称的周期轨道经历两个同步的周期倍化序列各自生成一个反对称的混沌吸引子.如果对称系统演变为非对称系统,非对称型周期n-2运动的分岔过程可用一个两参数开折的尖点分岔描述,音叉分岔将会演变为一支没有分岔的分支以及另外一个鞍结分岔的分支.     

弹性支承-刚性转子系统同步全周碰摩的分岔响应

基于航空发动机转子系统的结构特点,将航空发动机转子系统简化为一个非线性弹性支承的刚性转子系统.根据Lagrange方程建立了弹性支承-刚性不对称转子系统同步全周碰摩的运动方程;采用平均法进行求解,得到了关于系统振幅的分岔方程;根据两状态变量约束分岔理论,分别给出了系统在无碰摩和碰摩阶段参数平面的转迁集和分岔图,讨论了转子偏心、阻尼对系统分岔行为的影响;应用Liapunov稳定性理论分析了系统碰摩周期解的稳定性和失稳方式,给出了系统参数——转速平面上周期解的稳定范围;该文的研究结果对航空发动机转子系统的设计有一定的理论意义.     

具有单边约束的基本分岔问题的新分岔模式

含约束分岔是非线性动力系统周期解分岔研究中遇到的普遍问题，然而现有的奇异性理论关于此类问题的结果还很少。作为探讨和补充，给出余维数不大于3的10种基本分岔在约束情况下的转迁集和摄动保持分岔图的计算结果，可为约束分岔问题的研究提供直接利用的结果。     

Operator differential-algebraic equations with noise arising in fluid dynamics

We study linear semi-explicit stochastic operator differential algebraic equations (DAEs) for which the constraint equation is given in an explicit form. In particular, this includes the Stokes equations arising in fluid dynamics. We combine a white noise polynomial chaos expansion approach to include stochastic perturbations with deterministic regularization techniques. With this, we are able to include Gaussian noise and stochastic convolution terms as perturbations in the differential as well as in the constraint equation. By the application of the polynomial chaos expansion method, we reduce the stochastic operator DAE to an infinite system of deterministic operator DAEs for the stochastic coefficients. Since the obtained system is very sensitive to perturbations in the constraint equation, we analyze a regularized version of the system. This then allows to prove the existence and uniqueness of the solution of the initial stochastic operator DAE in a certain weighted space of stochastic processes.     

A continuous review replenishment–disposal policy for an inventory system with autonomous supply and fixed disposal costs

In this study, we analyze an inventory system facing stochastic external demands and an autonomous supply (independent return flow) in the presence of fixed disposal costs and positive lead times under a continuous review replenishment–disposal policy. We derive the analytical expressions of the operating characteristics of the system; and, construct the objective function to minimize the total expected costs of ordering, holding, purchasing and disposal per unit time subject to a fill rate constraint. An extensive numerical analysis is conducted to study the sensitivity of the policy parameters and the benefit of employing a policy which allows for disposal of excess stock in this setting. We model the net demand process as the superposition of normally distributed external demand and inflows, which is expressed as a Brownian motion process. Our findings indicate that the disposal option results in considerable savings even (i) in the presence of non-zero fixed disposal costs, (ii) large actual demand rates with high return ratios (resulting in small net demands) and (iii) for moderate return ratios with high demand variability.     

Penalized sample average approximation methods for stochastic programs in economic and secure dispatch of a power system

In this paper, we develop a stochastic programming model for economic dispatch of a power system with operational reliability and risk control constraints. By defining a severity-index function, we propose to use conditional value-at-risk (CVaR) for measuring the reliability and risk control of the system. The economic dispatch is subsequently formulated as a stochastic program with CVaR constraint. To solve the stochastic optimization model, we propose a penalized sample average approximation (SAA) scheme which incorporates specific features of smoothing technique and level function method. Under some moderate conditions, we demonstrate that with probability approaching to 1 at an exponential rate with the increase of sample size, the optimal solution of the smoothing SAA problem converges to its true counterpart. Numerical tests have been carried out for a standard IEEE-30 DC power system.     

Well-Posedness and Asymptotic Behaviors for a Predator-Prey System with Lévy Noise

In this paper, well-posedness and asymptotic behaviors for a predator-prey system with Lévy noise are studied by using stochastic analytical techniques. Firstly, the existence and uniqueness of positive global solution with positive initial value is proved. Then, stochastic permanence for the system is investigated. Finally, persistence in mean and extinction for the system are discussed and some numerical simulations are provided to support our results.     

Optimization with multivariate stochastic dominance constraints

We consider stochastic optimization problems where risk-aversion is expressed by a stochastic ordering constraint. The constraint requires that a random vector depending on our decisions stochastically dominates a given benchmark random vector. We identify a suitable multivariate stochastic order and describe its generator in terms of von Neumann–Morgenstern utility functions. We develop necessary and sufficient conditions of optimality and duality relations for optimization problems with this constraint. Assuming convexity we show that the Lagrange multipliers corresponding to dominance constraints are elements of the generator of this order, thus refining and generalizing earlier results for optimization under univariate stochastic dominance constraints. Furthermore, we obtain necessary conditions of optimality for non-convex problems under additional smoothness assumptions.     

A discrete optimality system for an optimal harvesting problem

In this paper, we obtain the discrete optimality system of an optimal harvesting problem. While maximizing a combination of the total expected utility of the consumption and of the terminal size of a population, as a dynamic constraint, we assume that the density of the population is modeled by a stochastic quasi-linear heat equation. Finite-difference and symplectic partitioned Runge–Kutta (SPRK) schemes are used for space and time discretizations, respectively. It is the first time that a SPRK scheme is employed for the optimal control of stochastic partial differential equations. Monte-Carlo simulation is applied to handle expectation appearing in the cost functional. We present our results together with a numerical example. The paper ends with a conclusion and an outlook to future studies, on further research questions and applications.     

Alternate risk measures for emergency medical service system design

The stochastic nature of emergency service requests and the unavailability of emergency vehicles when requested to serve demands are critical issues in constructing valid models representing real life emergency medical service (EMS) systems. We consider an EMS system design problem with stochastic demand and locate the emergency response facilities and vehicles in order to ensure target levels of coverage, which are quantified using risk measures on random unmet demand. The target service levels for each demand site and also for the entire service area are specified. In order to increase the possibility of representing a wider range of risk preferences we develop two types of stochastic optimization models involving alternate risk measures. The first type of the model includes integrated chance constraints (ICCs ), whereas the second type incorporates ICCs  and a stochastic dominance constraint. We develop solution methods for the proposed single-stage stochastic optimization problems and present extensive numerical results demonstrating their computational effectiveness.     

Optimal Keyword Bids in Search-Based Advertising with Stochastic Advertisement Positions

US expenditures on search-based advertising exceeded $12 billion in 2010. Advertisers bid for keywords, where bid price determines ad placement, affecting click-through and conversion rates. Advertisers must select keywords, allocating each a proportion of their fixed daily budget. In this paper, we construct a stochastic model for the selection and allocation process. We provide analytical results for the single-keyword problem and examine the multiple-keyword problem numerically. We investigate trade-offs between keywords given varying levels of risk and return. We show the implications of enforcing a probabilistic budget constraint. Our paper provides a critical analysis of the advertiser’s problem that may guide future research.     

Threshold Boolean form for joint probabilistic constraints with random technology matrix

We develop a new modeling and solution method for stochastic programming problems that include a joint probabilistic constraint in which the multirow random technology matrix is discretely distributed. We binarize the probability distribution of the random variables in such a way that we can extract a threshold partially defined Boolean function (pdBf) representing the probabilistic constraint. We then construct a tight threshold Boolean minorant for the pdBf. Any separating structure of the tight threshold Boolean minorant defines sufficient conditions for the satisfaction of the probabilistic constraint and takes the form of a system of linear constraints. We use the separating structure to derive three new deterministic formulations for the studied stochastic problem, and we derive a set of strengthening valid inequalities. A crucial feature of the new integer formulations is that the number of integer variables does not depend on the number of scenarios used to represent uncertainty. The computational study, based on instances of the stochastic capital rationing problem, shows that the mixed-integer linear programming formulations are orders of magnitude faster to solve than the mixed-integer nonlinear programming formulation. The method integrating the valid inequalities in a branch-and-bound algorithm has the best performance.     

一类带概率互补约束的随机优化问题的最优性条件

主要讨论了一类带概率互补约束的随机优化问题的最优性条件.首先利用一类非线性互补(NCP)函数将概率互补约束转化成为一个通常的概率约束.然后,利用概率约束的相关理论结果,将其等价地转化成一个带不等式约束的优化问题.最后给出了这类问题的弱驻点和最优解的最优性条件.