LOOK-AHEAD ALGORITHM FOR VELOCITY CONTROL BASED ON PARAMETERIZED CURVE INTERPOLATOR

To avoid suffering gouge and transient overshooting in high speed cutting machining, a novel parametefized curve interpolator model with velocity look-ahead algorithm is proposed. Based on a prearrangement step interpolation algorithm for parameterized curves and considering high curvature points, parameterized curve tool path is divided into acceleration segments and deceleration segments by look-ahead algorithm. Under condition of characteristics of acceleration and deceleration stored in control system, deceleration before high curvature points and acceleration after high curvature points are realized in real-time in high speed cutting machining. Based on new parameterized curve interpolator model with velocity look-ahead algorithm, a real cubic spline is machined simulativly. The simulation results show that velocity look-ahead algorithm improves velocity changing more smoothly.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

基于改进YOLOv5s的硬核期葡萄簇检测

为实现自然环境下硬核期葡萄簇的快速精准检测,提出一种改进的YOLOv5s网络。首先,将YOLOv5s主干特征提取网络和加强特征提取网络中的卷积模块(Conv)替换为拥有更强特征提取能力的RepConv模块;然后,将YOLOv5s主干特征提取网络中C3结构的BottleNeck也替换为RepConv模块;接下来,将高效通道注意力模块(ECA)添加到YOLOv5s加强特征提取网络中的C3结构;最后,将YOLOv5s卷积模块中的激活函数SiLU改为ReLU6。试验结果表明,改进YOLOv5s网络对葡萄簇检测的精确率为96.5%、召回率为94.5%、平均精度均值为98.0%、检测速度为260 f/s。相比Faster R-CNN、SSD、RetinaNet、YOLOv3(Ultralytics)、YOLOXs和YOLOv5s,其平均精度均值分别高10.4、44.1、13.9、0.2、8.9和1.0个百分点。提出的改进网络能够较好地检测自然环境下模糊、遮挡、簇粘连、不完整、昏暗及逆光等各种状态的硬核期葡萄簇,且方便在移动设备上部署。

落地扇风叶参数化气动优化设计研究

以某落地扇风叶为研究对象,建立风叶参数化模型,搭建多学科优化设计平台,以风扇风量和扭矩分别为目标开展单目标优化,优化设计变量为翼型安装角、翼型弦长、叶片弯度和叶片积叠线弯度及掠度。采用粒子群优化算法进行寻优,分别得到了不同指标下最优的扇叶配置,并确定了扇叶关键设计参数对扇叶气动性能的设计权重以及影响规律。对风量和扭矩影响最大的都是翼型安装角和弦长,随着翼型安装角和弦长的增大,风量增大,扭矩也增大。

空间六边形提篮拱安装线形算法研究

异形拱肋安装过程中,易出现控制点不能通视或棱镜无法保持垂直等情况,需重新计算新建控制点安装坐标。而拱肋处于空间倾角姿态,坐标计算极为复杂、计算量大,且对加工偏差的节段修正难度大,安装线形控制极为困难。针对实际问题,同时考虑有限元计算得到的预拱度值,将空间拱肋结构转化为几何模型,提出了一种空间六边形提篮拱安装控制点坐标参数化算法。通过合龙后线形测量结果发现,本方法具有较高的计算精度,且有效地解决了拱肋安装过程中新建控制点坐标计算的时效性和准确性。

基于室内舒适时长的寒冷地区居住建筑被动式技术适用性研究

本文选取河北省保定市地区居住建筑为研究对象,通过分析被动式技术的适用性,得到不同被动技术对居住建筑自然室温舒适时长的影响。同时,提出一种基于遗传算法的多参数寻优方法,以室内舒适时长最大为目标,经过遗传算法的迭代寻优得到被动式技术的最优解组合,大量节省了被动式技术方案比选时间,为设计师提供定量的参考依据,有益于推动我国节能减排事业的发展。

Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow

The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes. However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conven- tional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values： TM=180℃; Pinj = 20MPa; Phold= 16MPa and thold=8s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa. The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant （below 1%）. Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid.

Adaptive backtracking search optimization algorithm with pattern search for numerical optimization

The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

The Maximum Hamilton Path Problem with Parameterized Triangle Inequality

Given a complete graph with edge-weights satisfying parameterized triangle inequality, we consider the maximum Hamilton path problem and design some approximation algorithms.

Discrete Variable Structural Optimization based on Multidirectional Fuzzy Genetic Algorithm

Round method is the common method for discrete variable optimization in optimal design of complex mechanical structures;however,it has some disadvantages such as poor precision,simple model and lacking of working conditions＇ description,etc.To solve these problems,a new model is constructed by defining parameterized fuzzy entropy,and the rationality of parameterized fuzzy entropy is verified.And a new multidirectional searching algorithm is further put forward,which takes information of actual working conditions into consideration and has a powerful local searching capability.Then this new algorithm is combined with the GA by the fuzzy clustering algorithm（FCA）.With the application of FCA,the optimal solution can be effectively filtered so as to retain the diversity and the elite of the optimal solution,and avoid the structural re-analysis phenomenon between the two algorithms.The structure design of a high pressure bypass-valve body is used as an example to make a structural optimization by the proposed HGA and finite element method（FEM）,respectively.The comparison result shows that the improved HGA fully considers the characteristic of discrete variable and information of working conditions,and is more suitable to the optimal problems with complex working conditions.Meanwhile,the research provides a new approach for discrete variable structure optimization problems.

Push-Pull Finite-Time Convergence Distributed Optimization Algorithm

With the widespread application of distributed systems, many problems need to be solved urgently. How to design distributed optimization strategies has become a research hotspot. This article focuses on the solution rate of the distributed convex optimization algorithm. Each agent in the network has its own convex cost function. We consider a gradient-based distributed method and use a push-pull gradient algorithm to minimize the total cost function. Inspired by the current multi-agent consensus cooperation protocol for distributed convex optimization algorithm, a distributed convex optimization algorithm with finite time convergence is proposed and studied. In the end, based on a fixed undirected distributed network topology, a fast convergent distributed cooperative learning method based on a linear parameterized neural network is proposed, which is different from the existing distributed convex optimization algorithms that can achieve exponential convergence. The algorithm can achieve finite-time convergence. The convergence of the algorithm can be guaranteed by the Lyapunov method. The corresponding simulation examples also show the effectiveness of the algorithm intuitively. Compared with other algorithms, this algorithm is competitive.

基于局部几何参数化和JFNG算法的微电网群分布式连续潮流计算

由于分布式电源、电动汽车等新型电气化负荷内存在无功限幅约束,传统连续潮流在分析微电网的带负荷能力和微电网之间的联络能力时存在因错误识别节点类型而导致计算失败的现象,并且其不适配于微电网群的分布式管理模式。提出一种局部几何参数化连续潮流算法,相较于弧长参数化,该算法具有参数化方程易于解耦、分岔点计算精度较高等优点;采用费舍尔伯明斯特互补函数来处理非光滑无功限幅特性,避免节点类型频繁切换的问题。基于连续潮流模型,结合非精确牛顿-广义最小残差(JFNG)算法分布式框架,提出仅交换边界信息就可收敛的微电网群分布式连续潮流算法。算例结果验证了所提算法的有效性。

Z字形折叠无人机气动优化设计

针对Z字形无人机存在滚转力矩和偏航力矩的问题,提出一种基于混合翼型参数化的整机多目标优化方法,以非支配排序遗传算法(NSGA-Ⅱ)为核心与混合翼型参数化、SCDM流场建模、Fluent Meshing网格划分以及Fluent Solution流场计算相结合,建立优化模型,进而开发出基于Isight平台的整机自动优化流程,实现Z字形无人机的气动优化设计。结果表明,基于混合翼型参数化的优化平台,增加了翼型优化的搜索空间,实现了多目标预定数值的优化,减小了Z字形无人机的滚转力矩和偏航力矩,提高了其整体的气动性能。所提方法能消除有限翼展无人机的力矩,为解决不对称无人机滚转和偏航问题提供了技术参考。

多频未知时变扰动下的结构微振动鲁棒自适应控制

本文针对多频窄带未知和时变扰动,基于内模原理和Y-K参数化方法,提出一种反馈鲁棒自适应振动的主动控制算法。该算法通过设计PID中央鲁棒控制器,有效解决了次级通道模型未知情况下的鲁棒控制器参数设计问题。同时提出一种变步长最小均方(Variable Step Size Least Mean Square,VSSLMS)方法,可以在保证稳态误差的基础上大幅提升收敛速度,并通过系统辨识实验验证了所提VSSLMS方法相较于其他VSSLMS算法在收敛性能上的优越性。通过结构微振动主动控制实时实验,对比验证了单独采用滤波x最小均方(Least Mean Square,LMS)自适应控制算法、基于LMS算法的鲁棒自适应控制算法和基于VSSLMS算法的鲁棒自适应控制算法的抑振效果。实验结果表明,本文基于VSSLMS算法的鲁棒自适应控制算法在面向双频正弦窄带扰动以及其频谱、幅值突变情况时,都具有较好的收敛性和鲁棒性。

离心压缩机三维叶片参数化气动优化研究

为提高离心压缩机气动性能,考虑二元叶轮优化多以构型参数为变量,不适用于变量间存在交互作用的三维叶片。以某三元叶轮为优化对象,考虑各变量间的交互作用,采用Bézier曲线加载叶片角,直接选取三维叶片型线为优化变量进行气动优化。结合CFD数值仿真、非线性映射能力较强的BP神经网络与全局寻优能力较强遗传算法,以提升等熵效率和压比为目标进行优化。结果表明:设计工况下,优化后的叶轮等熵效率同比提升1.9%,压比同比提升0.3。三维叶片中部向吸力面偏移,叶片尾缘向压力面偏转可同时提升叶轮的效率和压比。

基于Grasshopper的三维地质快速建模程序开发与应用

地质勘查作为建筑工程过程中不容有失的环节,对于建筑工程的质量、造价等有着极大的影响。随着计算机技术的发展,地勘数据开始呈现出由二维图纸向三维模型转变的趋势,由此也导致了数据处理的软件精度差异,并且由于数据的不互通导致了软件之间的信息壁垒。本文围绕这一问题,基于Grasshopper参数化平台,利用Python作为开发语言对平台进行二次开发,实现了多源地质信息的快速处理,提升了三维地质模型的建模效率,形成了开放的数据格式以便打通相关软件之间的数据壁垒。通过一个工程实例应用,验证了地质建模程序的有效性和实用性,在简化地勘模型搭建方式及完成对地质模型分析的同时,也为后续桩基础工程量的优化等工作打下基础。

增强型海鸥优化算法及其在动态优化问题中的应用

近年来群智能优化算法因其原理简单且具有良好的全局寻优性能,在控制向量参数化方法的框架下,被广泛应用于化工动态优化问题的求解。为进一步提高化工动态优化问题的求解精度,提出一种增强型海鸥优化算法。该算法采用约束初始化思想来生成初始种群,引入动态收敛因子优化海鸥的攻击模式,以及将Levy飞行策略与动态收敛因子结合并应用到海鸥位置的更新过程,以增强算法跳出局部最优的能力。最后,将提出的改进算法应用于管式反应器的平行反应和批式反应器的优化求解,并与其他智能优化算法以及相关文献的优化结果进行对比分析。实验结果表明,增强型海鸥优化算法可以取得有竞争力的结果,算法具有较强的寻优性能。

基于多目标遗传算法的差速器壳体轻量化设计

针对某新能源汽车差速器壳体需在满足行驶工况的刚强度要求下实现轻量化,提出一种基于多目标遗传算法NGSA-Ⅱ和参数化模型相关联的优化设计方法。首先通过SolidWorks和ANSYS的联合仿真,对差速器壳体的大端、小端、法兰及主减速器外圆等部位的厚度实现参数化手段,并通过模态分析和静力学计算证明壳体有足够的优化空间;然后构建尺寸变量同优化目标之间的响应曲面;最后基于多目标遗传算法,建立以质量最小和壳体最大应力最小为目标的优化模型,对差速器壳体进行轻量化设计。结果显示:优化后壳体总质量从6.80 kg减为6.10 kg,减少了10.3%;壳体最大应力降低了9.9%;同时壳体的最大变形量在安全范围内,证明基于多目标遗传算法进行轻量化设计是可行的。

高速列车气动外形优化研究进展

随着运行速度的提升,高速列车对气动外形的要求也越来越高,追求性能优异、美观大方的气动外形是新型高速列车研发的一个重要方向.基于当前高速列车外形研发的思路,可以将气动外形优化概括为基于流场机理的改型优化和基于优化算法的外形优化两类.本文简要回顾了当前国内外在这两类优化途径上的系列工作,着重介绍了作者所在团队近年来做过的一系列气动外形优化工作.在基于流场机理的改型优化上,着重从“和谐号”和“复兴号”这两款主力车型的外形研发上探讨其改型优化的思路,主要探讨了空调导流罩、受电弓平台、风挡和转向架裙板几类对列车阻力影响较为明显的部件的优化设计,并介绍了其相对于上一代车型在气动性能上的提升.基于优化算法的外形优化方法,则因循气动外形优化流程,在列车外形已经具有较好性能的基础上,以高速列车头型流线型为主要优化对象,分别从高速列车参数化方法、替代模型开发以及优化算法改进三个方面进行介绍.其中,高速列车参数化方法主要介绍了局部型函数法、修正车辆造型函数法和类别/形状函数法三类;替代模型开发介绍了最优化替代模型和基于交叉验证的Kriging模型;在优化算法的改进上介绍了改进的非劣分类多目标粒子群算法和连续域混沌蚁群算法两方面的内容.基于上述三个方面介绍了气动外形优化策略在典型工程上的应用案例.

基于遗传算法的离心泵叶片水力性能优化

建立了一种基于遗传算法的离心泵叶片型线水力性能优化方法。区别于传统优化方法,此方法通过CFD技术求解离心泵叶片的水力性能,以离心泵水力效率及理论扬程作为优化目标进行多目标优化。包含3个主要步骤:基于蒙面法和三次B样条曲线的三维叶片型线参数化;基于NUMECA商用软件的计算域网格划分及流场求解;基于多目标遗传算法的全局优化。应用此方法对某单级单吸蜗壳离心泵水力性能进行了多目标优化,优化结果表明离心泵水力效率及理论扬程分别提高了0.35%和0.944%。