Modelling and Optimization of a Silicon Tuning Fork Gyroscope

This contribution deals with the systematic derivation of an analytical model for a silicon tuning fork gyroscope with piezoelectric drive and piezoresistive read‐out. The mathematical model serves as a basis for optimizing the overall sensor structure, for testing the electronic circuitry and for investigating the influence of disturbances, e.g. due to fabrication tolerances, on the measurement quality. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adaptive Modelling,Simulation and Optimization of Gas and Water Supply Networks

We consider the simulation and optimisation of transport processes through gas and water supply networks. Using a consistent modeling of the network, adjoint equations for the whole system including initial, coupling and boundary conditions can be derived. These are suitable to compute gradients for optimization tasks but can also be used to estimate the accuracy of models and the discretization with respect to a given cost functional. We show the applicability of an adaptive algorithm that automatically steers the discretization and models while maintaining a given accuracy in an optimisation framework. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Polynomial Optimization of Stochastic Feedback Control for Stable Plants

A rigorous derivation is given of the polynomial method foroptimizing the stationary stochastic feedback control of linearmultivariable plants. The same approach is used for both discrete-timeand continuous-time control. In each case, the method involvestwo spectral factorizations and, by considering only stableplants, the solution of one diophantine equation.     

A Knowledge-Based Approach To Response Surface Modelling in Multifidelity Optimization

This paper is concerned with approximations for expensive function evaluation – the expensive functions arising in an engineering design context. The problem of reducing the computational cost of generating sufficient learning samples is addressed. Several approaches of using a priori knowledge to achieve computational economy are presented. In all these, the results of a cheap model are treated as knowledge to be incorporated in the training process. Several approaches are described here: in particular, we focus on neural based systems. This approach is then developed as a new knowledge-based kriging model which is shown to be as accurate as neural based alternatives while being much easier to train. Examples from the domain of structural optimization are given to demonstrate the approach.     

利用故障数据和估计的检查数据建立维修优化模型

利用时间延迟概念,根据故障记录数据和估计的检查数据建立了预防维修模型.通过对故障记录数据统计分析,提出了模型的假定条件.采用最大似然估计法,估计参数,包括缺陷发生率、不完全检查概率和时间延迟分布.建立了有关预防维修间隔期和总停机时间之间关系的检查模型,并根据估计参数和检查模型,计算最佳维修间隔期.     

Modelling of Heat Transfer and Vehicle Dynamics for Thermal Load Reduction by Hypersonic Flight Optimization

Heat input reduction by appropriate, optimal trajectory control is considered for the range cruise and the return-to-base cruise of a hypersonic vehicle propelled by a turbo/ram jet engines combination. A mathematical model is developed for describing the unsteady heat transfer through the thermal protection system. This model is coupled to the model of the dynamics of the vehicle. An efficient optimization technique is applied for constructing a solution for the two cruise problems. The results show that significant heat input reductions can be achieved with only a small penalty in fuel consumption.     

Modelling and Reality

Some authors have expressed concern about the concept of optimisation and the emphasis which is placed in the O.R. literature on the mathematical aspects of models. Case studies are often described as though the mathematical solutions were the most important parts and seldom does the opportunity arise to expose the more behavioural issues of a problem.The reason for the concentration on mathematics may be the form of the classical model of the process of O.R. and the inherent expectation that a study should conform to it. An earlier paper by this author is recognised to have been written in the mode of the classical model and now the issues of a non-mathematical nature are brought to the fore. It is suggested that there is scope for improving the classical model, which could in turn allow the introduction of greater realism into the case studies which are reported.     

基于粒子群算法的分布式多工厂批量计划问题研究

研究了分布式多工厂协同生产的约束批量计划问题,以产品的生产成本、库存成本、调整准备成本和运输成本之和最小为目标,构建了生产能力有限情况下的数学模型,提出了用于求解该问题的粒子群算法方案,阐明了该算法方案的具体实现过程.对典型算例进行了仿真,并与LINGO软件的求解结果进行了比较,结果表明粒子群算法方案的有效性和可行性.     

Theory of Modelling and Simulation

Journal of the Operational Research Society -     

计及碳排放配限额的虚拟电厂多目标风险规避优化模型

将风电场、光伏发电、生物质发电、储能和燃气轮机及柔性负荷聚合为虚拟电厂(Virtual power plant,VPP).进一步,为刻画风光不确定性风险,分别利用条件风险价值方法(Conditional risk at value,CVaR)构造最小化运营风险目标函数及利用鲁棒随机优化理论转化含不确定性变量约束条件,并选取最大化运营收益和最小化碳排放总量,构建VPP多目标风险规避优化模型.最后,选取改进IEEE30节点系统进行算例分析,结果表明:1)所提风险规避模型能够兼顾效益、风险和碳排放多方诉求;特别是,当鲁棒系数Γ≤0.85,较小的不确定性会带来较大的风险,表明决策者风险态度会影响VPP调度方案;2)预测误差e较高时,相同的Γ增长幅度会带来更高的CVaR增长幅度,表明较低的预测精度会放大不确定性风险,意味着决策者需通过提升预测精度以降低VPP运营风险;3) META能凸显清洁能源环境友好特性,实现VPP整体的最优均衡运行.综上,所提模型能够为决策制定最优VPP调度策略提供决策支撑.