In situ body mount optimization: theory and experiment

In this paper, an in situ method has been used to find the optimum set of body mounts for a given vehicle. The strength of this method is that it does not require the vehicle to be disassembled. Standard noise, vibration and harshness testing procedures are used to obtain frequency response function (FRF) models of the vehicle's subsystems. This is followed by FRF-based substructuring synthesis approach to obtain the overall vehicle model. The model is incorporated into an optimization algorithm to predict the optimum set of body mounts for a desired objective function. The in situ method is presented and applied to an experimental case study of a pick up truck. The experimental results confirmed the validity and effectiveness of this approach in finding the optimum body mount set, resulting in significant reduction in the vibration level of the vehicle.     

珠三角腹地软基十字板与静探试验成果的相关性研究

在原位十字板与静探数据的相关性分析中,考虑研究范围内土体强度可分别由两种测试数据独立线性表达,找出两种试验指标的等式,将试验参数综合考虑,建立多元线性回归方程,通过大量试验数据并借助数学软件对回归方程进行求解。通过实例分析,验证两种试验关系的准确性,为今后的勘察与研究提供参考和新的方法。     

考虑气动阻力和横风稳定的汽车车身多目标优化设计

综合考虑了气动阻力特性和横风稳定性,对车身外形参数进行了多目标自动优化设计。综合利用参数化建模技术、计算流体力学(CFD)仿真、试验设计方法、响应面模型和智能优化算法,集成Pro/Engineer参数化建模和ICEM网格划分工具以及Fluent仿真软件,在多学科优化平台modeFRONTIER上,搭建了一种自动优化设计流程。利用该流程,基于遗传算法(GA)对MIRA快背式模型车身几何外形进行了改型设计,得到了考虑车身气动阻力特性和横风稳定性的最优权衡设计解集。该结果使得气动阻力因数降低了5.2%,侧向力因数降低了5.8%。因而,实现了车身气动阻力和横风稳定性的多目标优化。     

路桥过渡段路基加固体的拓扑优化

应用拓扑优化理论,采用简化的假定条件,基于应变能最小即刚度最大原理,对路桥过渡段路基加固体的结构布置形式进行了优化。通过分析荷载模式、填高、加固长度和面积等因素对最优拓扑图形的影响,得出了路桥过渡段路基加固体的最优布置方式。拓扑优化结果表明：路桥过渡段路基加固的不同区域对结构刚度的贡献排序为,固定端附近区域-中间偏上区域-中间偏下区域-路基顶面远端区域;上长下短的倒梯形布置形式的结构刚度最大,其抵抗地基沉降变形的能力最强,表明目前桥头常用的倒梯形布置形式具有理论上的合理性;路基加固体底面布置长度应不小于2 m,由下向上斜率应缓于1∶1。     

An easy procedure to determine Magic Formula parameters: a comparative study between the starting value optimization technique and the IMMa optimization algorithm

In 2004, a new searching algorithm for Magic Formula tyre model parameters was presented. Now, a summary of the results, for pure and combined slip, that this algorithm is able to achieve is presented. The Magic Formula tyre model needs a set of parameters to describe the tyre properties. The determination of these parameters is dealt with in this article. A new method, called IMMa Optimization Algorithm (IOA), based on genetic techniques, is used to determine these parameters. Here, we show the computational cost that has been used to obtain the optimum parameters of every characteristic of the Magic Formula tyre model, called Delft Tyre 96. The main advantages of the method are its simplicity of implementation and its fast convergence to optimal solution, with no need of deep knowledge of the searching space. Hence, to start the search, it is not necessary to know a set of starting values of the Magic Formula parameters (null sensitivity to starting values). The search can be started with a randomly generated set of parameters between [0, 1]. Nowadays, MF-Tool, an application developed by TNO, uses an optimization technique to fit Magic Formula parameters from Matlab toolbox [van Oosten, J.J.M. and Bakker, E., 1993, {Determination of magic tyre model parameters}. Vehicle System Dynamics, 21, 19–29; van Oosten, J.J.M., Savi, C., Augustin, M., Bouhet, O., Sommer, J. and Colinot, J.P., 1999, {Time, tire, measurements, forces and moments, a new standard for steady state cornering tyre testing}. EAEC Conference, Barcelona, 30 June–2 July.]. We refer to that algorithm as the starting value optimization technique. The comparison between the optimization technique employed by TNO and the proposed IOA method is discussed in this article. In order to give a relative idea of adjustment accuracy, the sum-squared error and the mean-squared error, from the curves of the tyre model with the parameters optimized by both applications compared with test data are evaluated.     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization,multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.