Design optimization of a torpedo shell structure

An optimized methodology to design a more robust torpedo shell is proposed. The method has taken into account reliability requirements and controllable and uncontrollable factors such as geometry, load, material properties, manufacturing processes, installation, etc. as well as human and environmental factors. The result is a more realistic shell design. Our reliability optimization design model was developed based on sensitivity analysis. Details of the design model are given in this paper. An example of a torpedo shell design based on this model is given and demonstrates that the method produces designs that are more effective and reliable than traditional torpedo shell designs. This method can be used for other torpedo system designs.     

车身壁板自由阻尼层稳健性优化设计

在车身壁板自由阻尼层的优化设计中,提出了考虑阻尼材料参数不确定性波动的稳健性优化设计方法。首先,在车身防火墙、地板和顶棚等区域全敷阻尼材料;其次,以等效辐射声功率(ERP)为优化目标对阻尼层布局进行拓扑优化并验证优化效果;最后,以阻尼层厚度为随机设计变量,损耗因子和弹性模量为随机变量,质量最小为优化目标,并结合径向基函数(RBF)近似模型、蒙特卡洛模拟(MCS)和序列二次规划算法(SQP)对阻尼层进行6σ稳健性优化设计。结果表明,优化后车身自由阻尼层的总质量减少了50.45%,并且车身结构噪声性能达到了6σ质量水平,实现了保证车身轻量化要求下的阻尼层稳健性优化的目标。     

考虑行驶时间不确定性的合乘路径鲁棒优化方法

为应对实际合乘过程中时间不确定性带来的负面影响,本文研究不确定行驶时间下的合乘问题。采用预算不确定集合描述时间变量,引入不确定性水平可调节的预算系数,构建以车辆总里程最短和车辆数最少为目标的合乘路径鲁棒优化模型。并设计两阶段算法求解,第1阶段以两乘客间的可行合乘路径为基础,从车辆总里程节省率和乘客时间窗匹配灵活性两方面设计公式量化合乘匹配机会,以匹配机会为权重构建乘客图网络并聚类乘客需求;第2阶段设计以顺序插入启发式方法构造初始解的禁忌搜索算法求解。案例数据实验结果表明：本文聚类方法能保证优化质量并提高85%以上的计算效率,同时能缩减乘客等车时间和绕行距离;增大预算系数时解的鲁棒性逐渐提高,但会增加10%~40%的车辆数并降低1%~10%的里程节省率;大规模乘客案例和窄时间窗案例的合乘路径对不确定时间的敏感性更高,宽时间窗案例无需增加过多额外车辆和总里程就能达到较高水平的路径鲁棒性。     

Optimisation of lateral car dynamics taking into account parameter uncertainties

Simulation studies on an active all-wheel-steering car show that disturbance of vehicle parameters have high influence on lateral car dynamics. This motivates the need of robust design against such parameter uncertainties. A specific parametrisation is established combining deterministic, velocity-dependent steering control parameters with partly uncertain, velocity-independent vehicle parameters for simultaneous use in a numerical optimisation process. Model-based objectives are formulated and summarised in a multi-objective optimisation problem where especially the lateral steady-state behaviour is improved by an adaption strategy based on measurable uncertainties. The normally distributed uncertainties are generated by optimal Latin hypercube sampling and a response surface based strategy helps to cut down time consuming model evaluations which offers the possibility to use a genetic optimisation algorithm. Optimisation results are discussed in different criterion spaces and the achieved improvements confirm the validity of the proposed procedure.     

精确反馈线性化在船舶航向保持器上的应用

现有的船舶航向保持器的设计大都采用Nomoto线性模型，而忽略了船舶操纵运动的非线性因素，因此航向控制的精度比较低。选择状态反馈精确线性化方法，将响应型船舶运动的非线性数学模型进行线性化，再利用基于闭环增益成形算法的鲁棒控制器进行控制，然后在Maflab的Simulink下进行系统仿真，仿真结果表明，所设计的基于闭环增益成形算法的控制器比基于LQR的控制器具有更好的控制能力。     

Application of H∞ control in rudder/flap vector robust control of a ship＇s course

Given the uncertainty of parameters and the random nature of disturbances that effect a ships course, a robust course controller should be designed on the basis of rudder/flap vector control. This paper analyzes system uncertainty, and the choice of weighting functions is also discussed. When sea waves operate on a ship, the energy-concentrating frequency varies with the angle of encounter. For different angles of encounter, different weighting functions are designed. For the pole of a nominal model existing in an imaginary axis, the bilinear-transform method is used. The ＂2-Riccati＂ equation is adopted to solve the H∞ controller. A system simulation is given, and the results show that, compared with a PID controller, this system has higher course precision and more robust performance. This research has significant engineering value.     

基于容差模型的发动机悬置系统稳健优化设计

应用稳健优化设计理论,考虑设计变量的不确定性对结果的影响,建立稳健优化模型。以发动机悬置系统能量解耦为目标,用Pareto遗传算法对系统的刚度参数进行稳健优化,并将优化结果运用Monte Carlo方法进行分析.结果表明,优化方法可以有效提高悬置系统的稳健性。     

船舶航向保持的鲁棒神经网络控制

设计一个N5-5-5-5-5-5-1前向神经网络,经训练得到一个开环的船舶航向保持控制器,使其能够较好地保持典型输入信号作用下的船舶航向;然后应用闭环增益成形算法,形成闭环控制系统.这种基于神经网络的航向保持方案,将神经网络的适应能力、非线性函数映射能力和闭环增益成形算法的鲁棒性结合起来,改善了系统的鲁棒性能.这种方法设计过程简单,物理意义明显.     

应急设施鲁棒优化选址模型及算法

为解决不确定情况下应急设施选址问题,采用鲁棒优化方法处理应急节点权重的区间估计,基于最优的设施选址到各个应急节点的赋权距离之和最小,建立有限期要求的不确定性应急设施选址模型,并给出了模型的求解算法,比较分析了鲁棒解与确定情况下的最优解。分析结果表明:当情况发生变化后,在确定情况下得到的最优解将发生较大的偏差,而在所有可能发生的情况下,鲁棒解与最优解目标函数值的最大偏差最小,因此,不确定性应急设施选址模型的解可以有效规避风险。