高速压力机转子-轴承系统的动力学研究

以有限元方法为基础,建立了曲柄压力机的转子-轴承系统的运动方程,通过计算压力机的曲柄连杆之间的作用力,获得主轴转子的径向载荷,并集成建立转子-轴承系统的振动模型;采用Newmark方法进行求解,进行模拟仿真,分析转子-轴承系统的振动响应;对主轴转子的振动特性进行实验研究,验证了转子-轴承系统的振动模型及其模拟仿真,进而为压力机转子-轴承系统的设计提供了数值分析及实验研究依据。     

基于AK-MCS法的主轴系统振动可靠性分析

采用集中质量法对数控车床主轴系统进行了简化,建立了考虑主轴刚度、阻尼、偏心质量和成组滚动轴承非线性接触力的十自由度非线性振动模型,使用Runge-Kutta数值积分法求得了主轴-轴承系统振动微分方程的数值解。考虑影响主轴振动的相关参数的随机性,以轴端轴心的振动幅值作为衡量主轴振动可靠性的指标,采用Kriging和Monte Carlo法相结合的方法(AK-MCS)计算了主轴振动可靠度。研究结果表明:采用AK-MCS算法与直接Monte Carlo法计算的失效概率几乎相等,而AK-MCS算法的运算效率明显更高,说明了该研究采用的AK-MCS算法的准确性与高效性,也验证了该方法适用于强非线性系统的可靠性计算。     

大型水轮发电机组主轴系统受电磁、水流激发的多重共振

建立了70 MW大型水轮发电机组主轴系统垂直振动的多自由度物理模型,采用轴向电磁力的解析表达式及推力轴承油膜刚度和阻尼的解析表达式,并考虑了蜗壳不均匀流场、导叶水流不均匀,水道水锤三种频率的水流力联合激发下,对主轴系统垂直振动的四重共振进行了研究,得到了多重共振的特性和规律,对大型水轮发电机组的振动和运行可靠性有实际应用价值.     

带冷却系统的XK717数控铣床主轴部件热特性分析

机床主轴系统是机床的核心部件，其热特性的好坏，直接影响机床的加工精度；以建立主轴系统的有限元模型，结合主轴系统与普通水箱之间的冷却模型，对带有冷却套的主轴系统进行了热特性分析，得出了主轴系统的热特性曲线；根据分析结果，对XK717数控铣床主轴部件冷却套的布局进行了改进设计，为优化数控铣床主轴系统提供了理论依据。     

激光捷联惯导振动耦合问题的精细建模

激光陀螺在惯性测量单元(IMU)中的测量精度对比单表测试结果下降是激光捷联惯导研究的难点之一。工程实践中发现,激光陀螺在IMU运行时保持单表性能的关键在于振动解耦。从建立振动模型出发,导出了振动解耦的理论约束。具体的,从动量矩定理和拉格朗日方程出发建立IMU的六自由度振动模型;进而为了实现振动解耦,类比惯性椭球,定义了减振系统的弹性椭球,分析了惯性椭球与弹性椭球不同位置关系时IMU的振动响应特点。主要结论为:惯性椭球与弹性椭球球心重合且主轴重合为IMU的最佳设计状态,此时六个自由度的振动相互解耦。     

基于气弹模型的串列主缆气动干扰试验研究

通过气弹模型风洞试验,研究了不同间距、风偏角、风攻角及固有频率下,串列主缆间的气动干扰特性.研究发现,上游模型的干扰会导致下游模型发生面外振动为主的尾流驰振,且上游模型会跟随下游模型出现同一模态的小幅振动;随间距减小,尾流驰振临界风速相应降低;在负风偏角及正风攻角下模型更易出现尾流驰振;固有频率变大,总体上可提高尾流驰振临界风速.发生尾流驰振时,有多个模态共同参与振动,且各模态参与程度随风速增加规律性变化.模型运动轨迹为主轴在一、三象限内的椭圆振动,随风速增加主轴方向没有明显改变.最后,通过CFD数值模拟方法对试验结果合理性进行了验证.     

水室封头重型铣削加工刀盘与主轴系统振动模态分析

核岛AP1000蒸发器水室封头是当今世界应用最广、制造难度最大的第三代核电核心部件。重型铣削加工过程中的振动冲击与颤振,严重影响和制约了加工质量和生产效率。为研究水室封头重型铣削刀盘与主轴系统的固有振动特性,首先分析重型铣削加工振动产生特点,建立了专用机床(TK6920型数控落地镗铣床)与刀盘(Φ200R8圆弧刀片刀盘)的三维模型,进行有限元模态仿真分析;进而在生产现场进行模态实验,采集和处理数据并进行实验模态分析;通过对比实验数据与仿真模态分析结果,最后总结出重型铣削机床刀盘与主轴系统的固有振动特性,为研究重型铣削过程中振动特性奠定基础。     

基于HHT的数控机床主轴振动监测系统的研制

为监测数控机床主轴的运行状态,针对机床主轴在工况条件变化或故障发生时振动信号的非平稳特性,研制基于HHT时频分析方法的数控机床主轴振动监测软、硬件系统。硬件系统包括基于FPGA主控模块与PC104总线的数据采集模块;软件系统包括时域波形监测与特征数据监测两模块,其中特征数据监测模块具有监测频谱分布及时频分布功能。为更直观、准确反映数控机床主轴振动信号的非平稳特性,提出基于HHT的主轴振动信号特征提取方法,实现对振动信号时频分布的实时监测。数控机床主轴振动信号测试结果表明,该系统在监测信号时域波形与频谱分布的同时,能利用HHT的瞬时频率描述特性,实现对数控机床主轴振动信号时频分布的实时监测。     

高速主轴动平衡及其在线控制技术

针对机床主轴在线自动平衡控制问题,阐述了高速主轴不平衡识别方法和在线自动平衡技术国内外现状,分析了喷液式在线自动平衡装置原理,设计了喷液式平衡系统,并通过高速主轴实验对该系统的有效性进行了验证。研究结果表明,主轴经过平衡后,不平衡量振动值由1.60mm/s降至0.34mm/s,主轴失衡振动得到了有效抑制。     

时变主轴转速再生切削系统的动态响应特征

考虑多重再生切削效应,建立了在时变主轴转速下再生切削系统的时滞动力学模型及递推求解算法,该算法适用于主轴转速变化的任意轨迹.将再生切削系统相继两转的动态响应作为观测数据,提出了基于互四阶累积量的谐参数联合估计的TLS-ESPRIT方法.根据该方法,研究了时变主轴转速对再生切削系统动态响应的影响,揭示了再生切削系统动态响应的频率、相继两转切削间的相移和互功率等谐参数对主轴转速依赖的一些新特征.     

Riemannian optimization and multidisciplinary design optimization

Riemannian Optimization (RO) generalizes standard optimization methods from Euclidean spaces to Riemannian manifolds. Multidisciplinary Design Optimization (MDO) problems exist on Riemannian manifolds, and with the differential geometry framework which we have previously developed, we can now apply RO techniques to MDO. Here, we provide background theory and a literature review for RO and give the necessary formulae to implement the Steepest Descent Method (SDM), Newton’s Method (NM), and the Conjugate Gradient Method (CGM), in Riemannian form, on MDO problems. We then compare the performance of the Riemannian and Euclidean SDM, NM, and CGM algorithms on several test problems (including a satellite design problem from the MDO literature); we use a calculated step size, line search, and geodesic search in our comparisons. With the framework’s induced metric, the RO algorithms are generally not as effective as their Euclidean counterparts, and line search is consistently better than geodesic search. In our post-experimental analysis, we also show how the optimization trajectories for the Riemannian SDM and CGM relate to design coupling and thereby provide some explanation for the observed optimization behaviour. This work is only a first step in applying RO to MDO, however, and the use of quasi-Newton methods and different metrics should be explored in future research.     

Aircraft technology portfolio optimization using ant colony optimization

Technology portfolio selection is a combinatorial optimization problem often faced with a large number of combinations and technology incompatibilities. The main research question addressed in this article is to determine if Ant Colony Optimization (ACO) is better suited than Genetic Algorithms (GAs) and Simulated Annealing (SA) for technology portfolio optimization when incompatibility constraints between technologies are present. Convergence rate, capability to find optima, and efficiency in handling of incompatibilities are the three criteria of comparison. The application problem consists of finding the best technology portfolio from 29 aircraft technologies. The results show that ACO and GAs converge faster and find optima more easily than SA, and that ACO can optimize portfolios with technology incompatibilities without using penalty functions. This latter finding paves the way for more use of ACO when the number of constraints increases, such as in the technology and concept selection for complex engineering systems.     

Gearing optimization

We consider an optimization problem that arises in machine-tool design. It deals with optimization of the structure of gearbox, which is normally represented by a graph. The edges of such a graph correspond to pairs of gear-wheels and the vertices stand for velocities. There is a designated input vertex and a set of output vertices. The problem is to create a graph with given number of output vertices while minimizing the total number of vertices. We present an integer programming formulation of this problem and propose an efficient solution in the special case of regular graphs. The author gratefully acknowledges the support of DIMAP—the Center for Discrete Mathematics and its Applications at the University of Warwick.     

Critical optimization

Critical optimization is a remarkable recently discovered thin film deposition phenomenon. Its extraordinary reproducibility, its applicability to an extremely wide range of properties and materials, its really accurate (and theoretically explicable) correlation with a simple fundamental property of the materials, and its ability to predict and explain technologically important property optima make it perhaps unique in thin film science. We here present a review, discussing both what is known and what remains to be done; we also propose a possible new mechanism, involving a connection with the “ideal amorphous state”, and point out that critical optimization could be applicable to deposition methods other than the presently considered vacuum evaporation and sputtering, and to an even wider range of materials.     

Effective multi-objective optimization with the coral reefs optimization algorithm

In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.     

Solving constrained optimization problems with hybrid particle swarm optimization

Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder–Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.     

Simulation response optimization via an alternating variable methodsimulation response optimization

Simulation response optimization has wide applications for management of systems that are so complicated that the performance can only be evaluated by using simulation. This paper modifies the Hooke-Jeeves alternating variable method used in deterministic optimization to suit the stochastic environment in simulation response optimization. The basic idea underlying the proposed method is to conduct several different replications at each trial point to obtain a reliable estimate of the theoretical response. To avoid misjudging the real difference between two points due to the stochastic nature, a t-test instead of a simple comparison of the mean responses is performed. Empirical results from a stochastic Watson function with nine variables, a queueing problem with two variables, and an inventory problem with two variables indicate that the alternating variable method modified in this paper is superior to the Nelder-Mead simplex method, two stochastic approximation methods, and Fu and Healy's hybrid method. It is also robust with respect to the parameter for deciding the number of replications conducted at each trial point.     

Automated structural optimization system for integrated topology and shape optimization

Abstract

This paper combines previously developed techniques for image‐preprocessing and characteristic image‐interpreting together with a newly proposed automated shape‐optimization modeling technique into an integrated topology‐optimization and shape‐optimization system. As a result, structure designers are provided with an efficient and reliable automated structural optimization system (ASOS). The automated shape‐optimization modeling technique, the key technique in ASOS, uses hole‐expanding strategy, interference analysis, and hole shape‐adjusting strategy to automatically define the design variables and side constraints needed for shape optimization. This technique not only eliminates the need to manually define design variables and side constraints for shape optimization, but during the process of shape optimization also prevents interference between the interior holes and the exterior boundary. The ASOS is tested in three different structural configuration design examples.     

A parallel particle swarm optimization algorithm for multi-objective optimization problems

In this article, a new proposal of using particle swarm optimization algorithms to solve multi-objective optimization problems is presented. The algorithm is constructed based on the concept of Pareto dominance, as well as a state-of-the-art ‘parallel’ computing technique that intends to improve algorithmic effectiveness and efficiency simultaneously. The proposed parallel particle swarm multi-objective evolutionary algorithm (PPS-MOEA) is tested through a variety of standard test functions taken from the literature; its performance is compared with six noted multi-objective algorithms. The computational experience gained from the first two experiments indicates that the algorithm proposed in this article is extremely competitive when compared with other MOEAs, being able to accurately, reliably and robustly approximate the true Pareto front in almost every tested case. To justify the motivation behind the research of the parallel swarm structure, the computational results of the third experiment confirm the PPS-MOEA's merit in solving really high-dimensional multi-objective optimization problems.     

Refrigeration system optimization

It must first be established that refrigeration is necessary and that the required cooling cannot be achieved more cheaply by other means. The operating costs of a refrigeration system depend mainly on power consumption and this can be reduced by a wide variety of methods. The operating and capital costs should be brought together in the net present value for an appropriate life span. Various case studies are presented. Other relevant factors to be considered in system optimization include control methods, plant siting and the form of contract used.