Identification of multibody vehicle models for crash analysis using an optimization methodology

This work proposes an optimization methodology for the identification of realistic multibody vehicle models, based on the plastic hinge approach, for crash analysis. The identification of the design variables and the objective function and constraints are of extreme importance for the success of the optimization. The characteristics of the plastic hinges are used as design variables while the objective functions are formulated with measures of the difference between the dynamic response of the model and a reference response. The sequential application of genetic and gradient-based optimization methods is used to solve the optimization problem constituting a systematic approach to the automatic identification of vehicle multibody models. The methodology is demonstrated with the identification of the multibody model of a large family car for side and front crash. The vehicle model is developed in the MADYMO multibody code which is linked with the optimization algorithms implemented in the Matlab Optimization Toolbox.     

Design Considerations of a Prototype VTOL Robotic Vehicle through Market Survey Data Collection

A detailed technical design and performance comparison of current unmanned Vertical Take-Off and Landing (VTOL) vehicles is presented as a function of vehicle maximum speed, payload, range, endurance, and propulsion configurations. Mission applications and VTOL market characteristics are used to define design specifications for a new prototype unmanned VTOL vehicle suitable for a wide range of (mostly) civilian applications. The proposed VTOL vehicle's design phase is presented, including: performance capabilities calculation, fuselage strength evaluation and weight optimization via crash/drop tests. Drop tests performed have followed standard regulations used for airworthiness certification of such vehicles. The proposed VTOL vehicle is currently under prototype development.     

Multidisciplinary optimization of car bodies

Rising complexity of industrial development in the automotive industry is leading to a higher degree of interdisciplinarity, which is especially true in the virtual design area. New methods and solution procedures have to be evaluated and integrated in the overall process. For example, in car body design process, a new topic emerged recently: the multidisciplinary optimization of car bodies with respect to crash and NVH (noise, vibration, and harshness). Because rigorous evaluation of appropriate numerical algorithms is still missing, an intense study was realized at the research center of BMW. The results are summarized in this article. Four benchmarks have been studied: (a) a full vehicle model for NVH analysis, (b) a simplified multidisciplinary problem with a single crash case and linear statics and dynamics, (c) a lateral impact problem for multi-criteria optimization, and finally, (d) a small shape optimization problem was included to demonstrate the potential of transferring the results to the more complex problem of optimizations based on real changes in the shape of the structures. Because response surface methods have already been discussed in the literature and because of their failure in certain industrial cases, the focus was set on the evaluation of stochastic algorithms: simulated annealing, genetic and evolutionary algorithms were tested. Finally, a complete industrial multidisciplinary example from the current development process was studied for the validation of the results.     

Crash worthiness design optimization using multipoint sequential linear programming

A design optimization tool has been developed for the crash victim simulation software MADYMO. The crash worthiness optimization problem is characterized by a noisy behaviour of objective function and constraints. Additionally, objective function and constraint values follow from a computationally expensive numerical analysis. Sequential approximate optimization is used to deal with both the noisy functional behaviour and the high computational costs. By means of multipoint approximations, a sequence of linear programming problems is generated that can be easily solved. The optimization approach is illustrated for an analytical test problem and an industrial crash worthiness design problem.     

基于改进分支定价法的车辆路径优化研究

为克服分支定价算法中基于{0,1}的分支策略在求解车辆路径问题时效率和稳定性方面的缺陷,提出了一种双重禁用的分支策略。该分支策略在分支阶段首先通过筛选一组出弧数量最多的集合,然后按照一定的规则将其分为两组,左右分支分别对包含这两组弧的路线进行禁用,禁用的范围不仅局限于分支阶段,在之后的定价阶段同样需要禁止该弧的使用。双重禁用的分支策略不仅实现了分支定界树所需的分支功能,而且达到了求解效率和质量的平衡。通过采用包含强时间窗约束、载重约束、里程约束的车辆路径问题相关的算例,验证了相对于基于{0,1}的分支策略具有较强的寻优和稳定性能。     

Multidisciplinary Optimization of Multibody Systems with Application to the Design of Rail Vehicles

A methodology for the design optimization of multibody systems is presented. The methodology has the following features: (1) multibody dynamics is employed to model and simulate complex systems; (2) multidisciplinary optimization (MDO) methods are used to combine multibody systems and additional systems in a synergistic manner; (3) using genetic algorithms (GAs) and other effective search algorithms, the mechanical and other design variables are optimized simultaneously. The methodology is shown to handle the conflicting requirements of rail vehicle design, i.e., lateral stability, curving performance, and ride quality, in an effective manner. By coordinating these conflicting requirements at the system level, three multibody models corresponding to each of these requirements for a rail vehicle are optimized simultaneously.     

Development of generic multibody road vehicle models for crashworthiness

The crashworthiness analysis of road vehicles requires detailed data of the vehicles that the automotive manufacturers are, generally, unable to release due to commercial or legal restrictions. In the development of passive safety subsystems or substructures, the overall crash response of a vehicle model used to support it, must mimic that of the real vehicle; if this exists, regardless of any particular constructive detail of its structure provided that it is not located in the vicinity of such subsystem. This work proposes a methodology for the development of multibody models of road vehicles, for passive safety analysis, which include all general structural and mechanical features of real vehicles and start by exhibiting impact dynamic responses similar to the top of line vehicles. These vehicle models, designated as generic, do not require the knowledge of most of the particular details of the design of the real vehicle, which the manufacturers are unable to release, but can be adjusted to have crash responses similar to those of the real vehicle. Based on an existing finite element model of a car, which has all constructive features of vehicles of the chosen class, a multibody model is built applying the plastic hinge approach. By using a selected number of crash scenarios, defined in international standards such as the EuroNCAP, selected parameters of the vehicle multibody model are adjusted to ensure a good correlation between its impact responses and those of the finite element model. The crash responses are measured in terms of structural deformations, velocities and accelerations, occupant injury measures and structural energy absorption capabilities. Assuming that the plastic hinge constitutive equations of the multibody model are not exactly known, their parameters are used here as the multibody vehicle model that are adjusted. The methodology proposed is demonstrated by its application to the identification of the vehicle multibody model of a large family car for which the reference vehicle is available as a detailed finite element model.     

Inducing finite state machines from training samples using ant colony optimization

A method for control finite state machine (FSM) induction in which an ant colony optimization algorithm is used for search optimization is proposed. The efficiency of this method is estimated using the generation of FSMs for controlling a model of an unmanned aerial vehicle (UAV). It is shown that the proposed method outperforms the method based on genetic algorithms both in terms of performance and quality.     

基于数值仿真的人车碰撞事故再现研究

对于对未留制动印迹的人车碰撞事故采用数值仿真方法进行再现研究,提出了通过调整碰撞车速使得仿真结果(人体抛距、人体损伤情况等)逼近真实事故结果的方法估计碰撞车速,并应用于一起真实事故的再现研究,初步验证了模型和方法的可行性.采用上述经过验证的数值模型研究人车碰撞初始条件对仿真结果的影响发现:人车相对接触位置接近汽车前部一侧时对人体抛距值的影响较大,因此单纯根据人体抛距估计碰撞车速可能会造成再现结果的失真.     

基于划分的蚁群算法求解货物权重车辆路径问题

考虑单产品分销网络中的车辆路径问题(VRP:vehicle routing problem).与以往诸多研究不同的是,建立了一种带货物载重量的VRP模型(weighted VRP),即车辆在两个顾客之间行驶时的载重量也作为影响运输费用的一个因素考虑.因此,需求量较大的顾客拥有较高的车辆运输优先权.在分析了问题性质的基础上,提出一种基于划分策略的蚁群算法PMMAS求解货物权重车辆路径问题,并与其他常用的启发式算法进行比较分析,表明了算法的有效性.     

多目标多时间窗车辆路径问题的鸽群-水滴算法

针对智能水滴算法求解带时间窗车辆路径规划收敛速度慢、计算精度差的问题,根据带时间窗车辆路径问题的应用要求,利用整数线性规划方法,以配送车辆的最小运输总成本、最短运输距离和最少安排数量为目标,综合考虑了车辆出发点、服务点、装载量、行驶距离、服务时间窗等诸多约束条件,构建了多目标多时间窗车辆路径模型;为了精准快速求解多目标多时间窗车辆路径模型,提出一种鸽群-智能水滴互补改进优化算法,将河道水滴离散二进制变换后,采用地图罗盘算子和地标算子分别改进水滴的流动速度和方向,并利用自适应变邻域扰动策略干扰水滴携带的泥土量,提高水滴算法的开发和探索能力;利用理想点法和罚函数与多目标优化混合方法分别处理多目标函数与约束条件,并以两种经典的带时间窗车辆路径问题为实例,通过与遗传算法、智能水滴算法和鸽群-水滴算法的计算结果进行比较,结果表明：在相同的算法参数和经济指标下,鸽群-水滴算法相比于智能水滴算法求解模型中的运输路径缩短20 km左右、运输成本节约403元左右,且该算法的求解时间和迭代次数也明显优于其他两种人工智能算法。     

车辆路径问题的轻鲁棒优化模型与算法

针对不确定旅行时间下的车辆路径问题,以总变动成本最小为优化目标,建立了一种轻鲁棒优化模型,提出了一种针对问题特征的超启发式粒子群算法.在算法中,利用基于图论中深度优先搜索的初始化策略加快算法的早期收敛速度,引入基于均衡策略的启发式规则变换方式来提高算法的寻优能力,重新设计的粒子更新公式确保生成低层构造算法的有效性.实验结果表明:所提算法能有效地求解不确定旅行时间下的车辆路径问题.     

Preference-based topology optimization for vehicle concept design with concurrent static and crash load cases

In the simulation-based design process of automotive structures, an increasing amount of multi-disciplinary requirements have to be considered. Methods of topology optimization can be used to devise structural concepts early in the design process to obtain the best possible structural layout as starting point for further development steps. Especially relevant for the vehicle design process is the concurrent consideration of static load requirements representing normal operating conditions and energy absorption requirements targeting passive safety in crash events. When the disciplines are considered separately, the heuristic Hybrid Cellular Automaton topology optimization is a suitable method. However, in practical applications, both disciplines are usually addressed sequentially. This complicates the overall process and may reduce the quality of the final optimization result, since optimization objectives may be conflicting. We propose a preference-based Scaled Energy Weighting approach to address the topology optimization of both disciplines concurrently. The main idea is to decouple the user preference from the scaling of the different magnitudes of energies. This enables a multi-objective optimization and ultimately the selection of the desired trade-off solution. We first validate the capability of the method to provide structures optimized for stiffness and energy absorption objectives on beam examples. Finally, the method is applied to optimize a concept structure of an industrial vehicle body, demonstrating its practical feasibility.     

公路交通中车辆路径A＊算法优化的研究

公路交通管理中,最短路径是进行车辆路径优化的基础.论文在简单分析了一些经典的最短路径算法和这些最短路径算法单独应用于车辆路径优化存在的局限性的基础上,提出了一种改进A＊算法的方法[1～2],并用它来解决城市间道路网中实时最短路径问题,并以实例加以阐述.     

Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

This research is based on a new hybrid approach, which deals with the improvement of shape optimization process. The objective is to contribute to the development of more efficient shape optimization approaches in an integrated optimal topology and shape optimization area with the help of genetic algorithms and robustness issues. An improved genetic algorithm is introduced to solve multi-objective shape design optimization problems. The specific issue of this research is to overcome the limitations caused by larger population of solutions in the pure multi-objective genetic algorithm. The combination of genetic algorithm with robust parameter design through a smaller population of individuals results in a solution that leads to better parameter values for design optimization problems. The effectiveness of the proposed hybrid approach is illustrated and evaluated with test problems taken from literature. It is also shown that the proposed approach can be used as first stage in other multi-objective genetic algorithms to enhance the performance of genetic algorithms. Finally, the shape optimization of a vehicle component is presented to illustrate how the present approach can be applied for solving multi-objective shape design optimization problems.     

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.     

垃圾分类收运路径问题的新型混合蚁群算法求解

针对垃圾分类收运路径问题,考虑车辆装载容量约束、硬时间窗约束、装载率对成本的影响等条件下,以最小化运输成本和车辆固定成本为目标建立了数学模型。将考虑时间吻合度因子和车容量利用率因子的改进蚁群算法与混沌电磁场优化算法进行动态融合,并结合2-opt和两点交换的局部搜索方法,提出一种以改进蚁群算法为外部框架,混沌电磁场优化算法为内部模块的新型混合蚁群算法对城市生活垃圾分类收运问题进行求解。根据算法间优势互补的思想,利用两种算法的优点来弥补单个算法的缺陷,使其成功应用于该问题。最后,用车辆路径问题标准测试集和上海市杨浦区的数据作为实例进行测试与对比,验证了模型的正确性以及算法的有效性与优化能力。     

平衡装载约束下的车辆路径问题研究

针对车辆三维装载约束下的车辆路径问题（3L-VRP）进行研究,引进车辆的平衡装载约束,综合考虑传统的先进后出、局部支撑、脆弱性等约束,构建平衡装载约束下的车辆路径问题（BL-VRP）模型。针对模型中的平衡约束,提出一种接触面积的装载算法。在此基础上,构建以回溯遗传算法（B-GA）为骨架的多阶段算法框架,对车辆路径优化进行求解。研究结果表明,多阶段算法不仅在解决3L-VRP上好于目前已有算法,同时对BL-VRP表现优秀。提出的多阶段算法为解决BL-VRP问题提供一条参考思路,但在时效性上需要进一步完善。     

基于离散粒子群优化的工业无线网多信道分配算法

在充分考虑工业无线节点低能耗要求的基础上,提出一种工业无线网发射功率调节算法.在保证网络连通度的前提下,使无线节点发射功率尽可能小.同时,利用离散粒子群优化算法,提出一种解决工业无线网多信道分配问题的离散粒子群优化多信道分配算法（DPSO-CAA）.对标准离散粒子群优化算法加以改进.提高了所提出算法的全局搜索能力.通过将DPSO-CAA算法与已有算法进行的仿真对比实验,表明了所提出的DPSO-CAA算法能有效地降低数据碰撞和同信道干扰,而且能降低无线节点能耗.