装载机工作机构SQP算法优化及虚拟样机仿真

依据四连杆机构的求解方法,创建装载机Z形连杆机构的数学模型。在MATLAB软件上,运用SQP算法对徐工LW540F装载机工作机构的非线性数学模型进行优化设计,快速且准确地得到了全局最优解。优化结果改善了工作机构的举升平移性,提高了收斗稳定性,使转斗和举升油缸设计、传动角以及整机协调性满足了设计要求,优化了整机的作业性能,提升了作业效率和经济性能。利用ADAMS软件,对优化后的工作机构进行虚拟样机仿真,仿真结果验证了SQP算法解决装载机工作机构非线性模型优化的可行性。     

基于ADAMS的装载机工作装置仿真与优化

利用ADAMS软件建立了装载机工作装置的虚拟样机模型,对其进行动力学仿真分析,对工作装置进行优化设计,从而获得工作装置综合优化的结果,提高装载机的性能。     

基于遗传算法的机械式挖掘机工作装置优化

建立了机械式挖掘机工作装置的数学模型,并利用MATLAB软件,采用遗传算法对某机械式挖掘机工作装置进行了优化设计。优化结果提高了工作装置挖掘过程中的平稳性,同时减小了功率消耗。在ADAMS中对某机械式挖掘机工作装置进行虚拟样机仿真,仿真结果验证了数学模型的正确性。     

基于遗传算法的装载机传动系统参数优化

在ADVISOR汽车仿真软件的基础上,建立装载机工况循环模型、四轮驱动模型、铲装阻力模型和液压系统功率分流数学模型,二次开发建立了装载机动力性和燃油经济性仿真计算模型。实车试验装载机动力性和燃油经济性,并与仿真计算结果相比,以验证仿真模型的正确性。基于遗传算法,借助装载机动力性和燃油经济性仿真计算模型,对装载机动力传动系统参数进行优化设计。算例计算结果表明,优化方案改善了装载机传动系统参数,其燃油经济性改善较明显。     

装载机工作装置优化设计研究

传统装载机工作装置连杆机构设计方法难以同时兼顾连杆机构举升平稳和自动放平等要求,本文在仿真软件(ADAMS)中构建了装载机工作装置参数化模型,并对机构进行了优化设计研究,优化机构满足设计性能参数及自动放平要求;利用有限元结构分析和优化软件(Opti Struct)对摇臂进行形状优化,使摇臂的应力降低20%;并对工作装置进行了实验测试,测试与仿真结果一致。     

遗传算法定制设计高卸型装载机工作装置

为了拓宽装载机的用途,满足特殊工况下的卸高要求,在普通标准型产品的基础上,定制设计了ZLSO高卸型装载机工作装置。为了降低成本,提高产品中零部件的通用性,只对六连杆机构中个别杆件的参数进行了变动,其他参数保持不变。采用遗传算法作为优化方法,对高卸型装载机工作装置的性能和结构参数进行优化设计。结果表明,定制设计的装载机工作装置符合设计要求。     

装载机工作装置优化设计

对装载机工作装置建立了正确的数学模型,并利用Excel软件实现工作装置的优化设计,结合ZL40装载机工作装置的性能,同时根据不同的工作特性要求,得到了两个优化方案。     

滑移装载机垂直举升工作装置

介绍滑移装载机垂直举升工作装置设计方法,首先运用点位软件进行工作装置的点位设计、性能求解和动力学分析,然后运用P ro/E进行三维模型的创建,再使用ANS YS Workbench对主要结构件的强度进行应力仿真分析,最终得到满足设计要求的垂直举升工作装置。     

基于ADAMS的装载机工作装置仿真及优化设计

对装载机工作装置进行运动学和动力学仿真,并应用ADAMS中的优化设计模块,以铲斗下插角最大为目标函数,以各铰点坐标为设计变量,以连杆机构传动角、卸载角、卸载高度、卸载距离及转斗液压缸力等为约束条件,对装载机工作装置进行优化设计。根据优化结果建立工作装置最优结构动力学模型并进行仿真验证,结果表明优化后铲斗的下插角由0°增加到3.1°,最小传动角、卸载高度及卸载距离等性能参数得到提升,结构受力状态得到改善。     

基于COSMOSMotion的装载机工作装置仿真分析

采用SolidWorks软件建立装载机工作装置的虚拟样机模型,用COSMOSMotion软件进行仿真分析。分析结果对缩短产品的开发周期,提高设计质量有指导意义。所使用的仿真方法对装载机工作装置设计具有重要的参考价值。     

装载机工作装置八杆机构优化设计

在四杆机构运动分析的基础上,本文阐述了装载机工作装置八杆机构的优化设计。着重讨论了八杆机构优化设计数学模型的建立以及运用优化理论求解该模型问题;编制了相应的优化程序;并通过生产实例验证了本优化设计方法正确性和程序运行的可靠性。     

遗传算法在级配碎石配合比设计中的应用

将遗传算法引入级配碎石配合比设计领域,利用遗传算法的普适性和全局搜索特性找到最佳的配合比,通过实际工程数据对比说明,遗传算法得到的配合比优于传统方法得到的配合比,得出了遗传算法在该领域是一种可行、有效方法的结论。     

Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications.     

岩体渗透系数反演的遗传模拟退火方法及其在边坡工程中的应用

基于岩体内地下水渗流的数学模型,根据观测信息采用反演的方法确定岩体的渗透系数。将遗传算法和模拟退火算法相结合,建立遗传模拟退火算法以保证反演结果的全局最优性并提高收敛速度。结合边坡工程实例检验了所提出反演方法的有效性。     

Prediction of the optimum slope and surface azimuth angles using the Genetic Algorithm

In this paper, the Genetic Algorithm is applied to calculate the optimum slope and surface azimuth angles for solar collectors to receive maximum solar radiation. An area of Iran is selected to verify the results of this algorithm. The optimum angles and the collector input solar energies for these angles are calculated in hourly, daily, monthly, seasonally and yearly bases respectively. Then, the influence of different combinations of solar radiation components on the optimum slope angle and the energy gain is investigated. The results show that the Genetic Algorithm is a useful technique to find the optimum angles specifically when the number of independent parameters is large. The results show that the daily, monthly and yearly optimum surface azimuth angles for receiving the maximum solar energy are zero. Adjusting the collector at the daily optimum slope angle slightly increases the collector input energy compared with the case of monthly optimum slope angle so that the gain of solar energy is almost the same. The results also show that the hourly optimum surface azimuth angle is not zero and mounting the solar collector at the hourly optimum slope and azimuth angles increases the input energy significantly compared with the case of daily optimum angles. It is shown that the optimum slope angles are mostly dependent on the beam solar radiation. Furthermore, the results indicate that the optimum slope angles of solar collector and Photovoltaic panels are almost the same.     

Optimal design of district heating and cooling pipe network of seawater-source heat pump

The district heating and cooling (DHC) system of a seawater-source heat pump is large system engineering. The investments and the operational cost of DHC pipe network are higher than a tradition system. Traditional design methods only satisfy the needs of the technology but dissatisfy the needs of the economy, which not only waste a mass of money but also bring problems to the operation, the maintenance and the management. So we build a least-annualized-cost global optimal mathematic model that comprises all constrict conditions. Furthermore, this model considers the variety of heating load and cooling load, the operational adjustment in different periods of the year. Genetic algorithm (GA) is used to obtain the optimal combinations of discrete diameters. Some operators of GA are selected to reduce the calculation time and obtain good calculation accuracy. This optimal method is used to the design of the DHC network of Xinghai Bay commercial district which is a real engineering. The design optimization can avoid the matter of the hydraulic unbalance of the system, enhance the running efficiency and greatly reduce the annualized-cost comparing with the traditional design method.     

遗传算法在空间杆系结构优化中的应用

介绍了一种基于遗传算法和满应力准则法的空间杆系结构优化方法，该法综合了满应力准则法的搜索定向性和遗传算法的全局搜索能力，数值计算实例表明该法可以显著提高收敛速度，具有很强的适应性。     

建筑结构优化设计研究

对建筑结构的优化设计进行了科学的定义,分析了影响结构优化设计发展的因素,详细论述了结构优化设计的数学模型的建立和优化方法的选择。对优化设计综合评价指标体系的建立和使用方法进行了研究,并对结构优化设计的发展进行了展望。     

Optimum design of a non-conventional multiple tuned mass damper for a complex power plant structure

This paper implemented an optimal design of non-conventional multiple tuned mass damper (NC-MTMD) system for coal scuttles in power plant. The NC-MTMD was introduced using base isolation for the coal scuttles. Mathematical models of the NC-MTMD were derived by considering multiple three-dimensional (3D) modes. Base seismic excitation is represented by 3D Kanai-Tajimi filtered white noise. Properties of the base isolation (stiffness, damping) were calibrated through global optimisation using base shear and torsion as the objective function. Derivative (e.g. Global Search, Multi Start), or derivative free (e.g. Pattern Search, Genetic Algorithm and Simulated Annealing) algorithms were considered. Pattern Search algorithm showed better convergence, efficiency and stability performance. A comparative study was performed for single- and multi-mode cases. Robustness of the optimum design was investigated quantitatively. Design parameter variation and mass variation (due to different levels of coal storages) were considered and their influences were studied.