基于变参数环板等效叶片组的叶盘模型耦合振动分析方法

转子系统常见于旋转机械装置中,在航空、电力、化工等工业和民用的诸多行业中发挥着重要作用.转子系统一般由轴和多级叶轮组成,存在强度、疲劳、振动和噪声等一系列问题,亟待通过优化设计等手段提高产品的各项性能.由于每级叶轮都含有由多条叶片构成的叶片组,因此在应用有限元等方法分析时,即便利用了回转周期的性质,转子系统的整体自由度数目依然庞大,优化设计的计算效率有待提高.为了提高转子系统分析的效率,可将根据气动性能要求设计的单级叶盘视为状态已经确定的子系统,在具备足够计算精度的前提下对其有限元模型进行缩聚或简化,以降低整体系统的自由度.文章介绍叶片组模型的等效建模方法,以固有振动特性相近为等效准则,将有限元模型中周向环绕的叶片组等效为变参数圆柱型正交各向异性环形板,并通过理论推导和计算得到了环板模型的几何与材料等物理参数.以航空发动机低压涡轮叶盘模型为例,实现了叶片组的模型等效过程.结果表明,等效方法可在保证精度的前提下大大降低模型的自由度数目,为后续整体转子系统的优化设计提供了高效的叶盘建模方法.     

融合生理因子的植物叶片表观建模

植物叶片具有复杂的内部结构、生理过程和光学特性,使得在计算机上准确模拟叶片的表观十分困难.为此提出一种基于生理因子的植物叶片表观材质模型.首先考虑单双子叶植物在结构上的差别,利用单N层板和双N层板抽象叶片内部结构;然后基于PROSEPCT模型推导得到叶片材质模型的漫反射和透射项,并采用CookTorrance模型作为高光反射项,形成BRDF+BTDF形式的参数化材质模型;最后采用一种叶色变化算法将模型扩展为时间-空间变化的叶片参数化材质模型.实验结果表明,该模型可以通过控制叶绿素、胡萝卜素和叶片结构参数等生理因子来实时地调整叶片的表观,实现空间变化、时间变化的叶片老化模拟,具有较好的真实感效果和绘制速度,能够满足实时交互的需要.     

航空发动机压气机转子叶片声激振试验研究

对引起航空发动机压气机高压转子叶片振动故障的原因进行了分析.指出压气机在某种非正常工作状态下产生的高声强噪声中所包含的高强度声波,是激起转子叶片共振或颤振的原因之一,通过理论分析和实验研究,得出了如下结论:当转子叶片在机械激振和气动激振作用下已处于高应力工作状态时,如果再叠加由声波激起的共振应力,就会导致裂纹甚至折断.     

变壁厚涡轮叶片参数化设计

提出管道交线投影法的中弧线算法,通过圆形管道创建、管道求交和交线投影来计算中弧线.基于中弧线提出涡轮叶片变壁厚算法,通过定义每条截面线上壁厚标志点处的壁厚参数来定义壁厚,将壁厚设计变量从数十个减少至6个,便于参数化设计和优化设计;根据壁厚定义公式,可以设计工程中常见的4种壁厚分布的叶片.最后开发了变壁厚涡轮叶片参数化设计系统,给出设计实例.     

双参数法辨识叶片同步振动的研究

基于叶尖定时测振原理,对双传感器测量叶片同步振动的双参数分析方法进行理论推导,建立叶片同步振动分析模型并进行计算机仿真,得出双参数辨识叶片同步振动的具体方法.利用自主开发的叶尖定时测振系统在某型号航空设备上成功完成振动检测试验,采用双参数法对试验数据分析处理,准确辨识出叶片同步共振的幅值、固有频率、倍频等参数,所得结果与理论分析基本一致.     

玉米叶片物理建模与参数确定的运动学分析方法

基于物理的方法是解决植物动态建模与仿真的有效途径,而模型参数的确定是基于物理方法所面临的重要问题.为实现基于生物物理属性特征的玉米叶片高精度、高真实感动态建模与仿真,提出一种基于运动学分析的玉米叶片物理建模与参数确定方法.首先利用运动捕捉技术获取玉米叶片的运动数据;然后根据运动捕捉特征点构建基于质点-弹簧系统的物理模型;最后基于运动学分析建立物理模型参数解析方程组,利用最小二乘法计算物理模型的参数.以此参数构建物理模型实现玉米叶片的动态模拟,并给出2种模型构造方式的玉米叶片虚拟仿真.结果表明,该方法以一种数据驱动的机制有效估算出对应的物理模型参数,生成的玉米叶片动态虚拟仿真过程具有与实际情况较为接近的真实感效果,为虚拟植物建模与应用提供了一种可行的物理建模及参数确定方法.     

叶片-轮盘榫联结构的接触分析

叶片与轮盘之间的榫联结构存在接触和摩擦组合运动,在较高的热-机械载荷作用下容易发生微动磨损并导致疲劳破坏.本文采用有限元法对叶片-轮盘榫联结构进行接触分析,计算不同摩擦系数和不同转速情况下的叶片榫头和轮盘榫槽之间的接触压力、接触滑动距离.结果表明,摩擦系数增大,榫联结构接触面上的接触压力和滑动距离减小;转速增加,则接触压力和滑动距离增大.     

发动机叶片多功能测控试验系统的实现方法

围绕着发动机强度、振动试验方面的要求,并将近年来广泛提及的发动机试验与测试技术中的自动化、综合化和智能化的思想引入到试验系统的设计中,主要给出了发动机叶片多功能测控试验系统的实现方法,并对使用VisualC＋＋语言构建虚拟仪器的原理和方法作了一定论证,所设计的系统是非接触式测控系统,在测量准确性、频响范围、操作简便等方面,具有较多的优点．并具有一定的实际应用价值。     

植物叶片表面质感建模与真实感绘制研究进展

植物表观的真实感建模是计算机图形学领域的重要研究内容,叶子作为植物最重要的器官,尤其受到广泛的关注。近年来,随着计算机硬件技术和图形算法的快速发展以及对植物叶子生理机理研究的不断深入,植物叶片表观质感建模和真实感绘制的研究取得了很多成果。植物叶片表面光学特性的采集与建模技术是其中的研究热点和难点。从植物叶片真实感质感模型的定义出发,介绍了近年来国内外在植物叶片表面质感建模和真实感绘制方面取得的最新研究进展,并给出详细的分析和总结。最后对该领域研究存在的问题和未来发展方向提出了一些看法。     

虚拟植物叶片的可视化建模技术研究

植物叶片的真实感建模技术一直是虚拟植物仿真技术的研究热点和难点。从植物叶片的形态结构和纹理特征出发,提出一种基于图像的树叶仿真方法。通过对一幅树叶图像进行边缘检测并配合Marching Square算法,实现对树叶复杂轮廓信息的准确提取;利用Delaunay三角剖分的优化算法对叶片进行三角形网格化处理,得到具有均匀三角形网格的叶片模型;最后以叶脉作为变形控制骨架,采用基于Laplace方程的迭代变形算法完成二维叶片向三维变形叶片的重建,以模拟叶片表面的自然弯曲变形效果,增强叶片模型的真实感。相比于以往的树叶建模仿真方法,突出了叶脉在网格划分及树叶形变中所起的作用,所建模型能更逼真地反映真实植物叶片的形态特征,提高了叶片的真实感仿真程度。     

基于变参数模型的锂电池荷电状态观测方法

锂电池荷电状态(SOC)观测技术作为电池管理系统(BMS)的关键技术,在维持电池系统设备安全高效运作、延长电池组整体生命周期等方面均起着不可或缺的作用.本文以改善锂电池荷电状态的观测结果为目的,对锂离子电池荷电状态的观测方法进行了研究,基于二阶变参数锂电池模型,设计了一种有效的改善SOC观测精度的方法.首先,根据SOC的定义,建立了安时积分估计(AH),通过引入二阶变参数锂电池模型建立扩展卡尔曼滤波估计器(EKF),然后结合Takagi-Sugeno模糊模型原理,设计Takagi-Sugeno和EKF联合估计器(TS–EKF).最后,在Simulink仿真平台上验证了SOC观测方法的准确性和实用性.结果表明,本文所设计的Takagi-Sugeno和EKF联合估计器可以改善SOC观测精度.     

An equivalent linearization method for nonlinear Van der Pol oscillator subjected to random vibration using orthogonal functions

This paper deals with the idea of the orthogonal functions in the equivalent linearization of the nonlinear systems. Block Pulse (BP) function gives effective tools to approximate complex problems. The aim of this work is on using properties of the BP function as an orthogonal function in process of linearization. The BP functions have been used to propose an equivalent linearization method in the time domain to determine the unknown linearization coefficients. The accuracy of the proposed method compared with the other equivalent linearization approaches, including the regulation linearization and the dual criterion linearization methods. This study exploited the nonlinear Van der Pol oscillator system under stationary random excitation to demonstrate the feasibility of the proposed method. The validity of the analytical method is verified by applying different values of nonlinearity and intensity of excitation. Besides, by comparing the mean square responses and frequency response functions of the linearized systems for a wide range of nonlinearity depicted the present method is in agreement with other methods.     

An analytical investigation of an energy flow divider to attenuate hand-transmitted vibration

A five-degrees-of-freedom (DOF) bio-mechanical model of the hand-arm system is developed to study the vibration transmissibility characteristics of the human hand-arm. The model parameters are identified from the characteristics of vibration transmitted to the hand, forearm and upper arm, measured in the 10–200 Hz frequency range under a constant 25.0 N grip force. A concept of an energy flow divider is proposed to reduce the flow of vibration energy into the hand. The coupled hand-arm-divider is modeled as a six-DOF dynamical system and the response characteristics are evaluated for handle excitations caused by a palm-grip orbital sander. The response characteristics of the coupled hand-arm-divider model are compared to those of the hand-arm model to demonstrate the potential performance benefits of the proposed energy flow divider. The hand-transmitted vibration is further assessed using the overall weighted acceleration response, and it is concluded that the proposed energy flow divider can reduce the magnitude of hand-transmitted vibration considerably.     

An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects

Mathematical modeling of cerebral tumor growth is of great importance in clinics. It can help in understanding the physiology of tumor growth, future prognosis of tumor shape and volume, quantify tumor aggressiveness, and the response to therapy. This can be achieved at macroscopic level using medical imaging techniques (particularly, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI)) and complex mathematical models which are either diffusive or biomechanical. We propose an optimized generic modeling framework that couples tumor diffusivity and infiltration with the induced mass effect. Tumor cell diffusivity and infiltration are captured using a modified reaction-diffusion model with logistic proliferation term. On the other hand, tumor mass effect is modeled using continuum mechanics formulation. In addition, we consider the treatment effects of both radiotherapy and chemotherapy. The efficacy of chemotherapy is included via an adaptively modified log-kill method to consider tissue heterogeneity while the efficacy of radiotherapy is considered using the linear quadratic model. Moreover, our model efficiently utilizes the diffusion tensor of the diffusion tensor imaging. Furthermore, we optimize the tumor growth parameters to be patient-specific using bio-inspired particle swarm optimization (PSO) algorithm. Our model is tested on an atlas and real MRI scans of 8 low grade gliomas subjects. Experimental results show that our model efficiently incorporates both treatment effects in the growth modelingprocess. In addition, simulated growths of our model have high accuracy in terms of Dice coefficient (average 87.1%) and Jaccard index (77.14%) when compared with the follow up scans (ground truth) and other models as well.     

Listen and learn: An investigation of sonification as an instructional variable to improve understanding of complex environments

Sonification, the use of non-speech audio to convey information, was used to test whether the simultaneous presentation of visual and artificially created auditory (sound) information significantly improves scores on comprehension tests of complex, dynamic computer simulations of an ecology microworld over scores from college participants who were presented visual information alone. Brief training was provided and all tasks were completed online. Participants were also tested for musical aptitude and preferred learning modality. Results indicate that while age and grade point average significantly predicted score on the comprehension test, the treatment tested here did not. For one-month retention of knowledge, only grade point average was significant. Nevertheless, the experimental methodology and the demonstrated convenience of online data collection are a contribution to the field. Moreover, the size of several positive effects and the identification of opportunities for improvement in the presentation of information invite a follow-up study.     

An innovation approach to optimize a Kalman filter with an H ∞ error bound by secant method

Designing a Kalman filter with a constraint on the H _∞ norm of the estimation error was first developed by Bernstein and Haddad in 1989. The main result is a sufficient condition for characterizing the Kalman filter. In this paper, similar to the standard Kalman filter, the properties of orthogonal principles are also shown to be preserved. Furthermore, the uniqueness, as opposed to an H _∞ filter, of the filter is implied by the orthogonal principles. An innovative approach to obtaining the minimum energy with a constraint on the H _∞ norm of the estimation error is proposed since the original work of Bernstein and Haddad does not, in general, reach the minimum energy of the estimation error. By means of the Secant method, the energy of the estimation error can be reduced as much as possible, under the condition that the H _∞ error bound is still satisfied.     

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.     

An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration

The parallel preconditioned conjugate gradient method (CGM) is used in many applications of scientific computing and often has a critical impact on their performance and energy consumption. This article investigates the energy-aware execution of the CGM on multi-core CPUs and GPUs used in an adaptive FEM. Based on experiments, an application-specific execution time and energy model is developed. The model considers the execution speed of the CPU and the GPU, their electrical power, voltage and frequency scaling, the energy consumption of the memory as well as the time and energy needed for transferring the data between main memory and GPU memory. The model makes it possible to predict how to distribute the data to the processing units for achieving the most energy efficient execution: the execution might deploy the CPU only, the GPU only or both simultaneously using a dynamic and adaptive collaboration scheme. The dynamic collaboration enables an execution minimising the execution time. By measuring execution times for every FEM iteration, the data distribution is adapted automatically to changing properties, e.g. the data sizes.