基于PSO-ELM的军用飞机维修保障系统效能评估研究

维修保障系统效能评估是军用飞机精细化、规范化管理的一项重要措施,科学的维修保障系统效能评估是精细化保障的基础,是提升战斗力的保证。针对军用飞机的维修保障系统效能评估问题,在总结军用飞机维修保障系统效能评估体系的基础上,提出基于粒子群优化极限学习机算法(PSO-ELM)的维修保障系统效能评估模型;并通过实例进行验证。结果表明:该算法应用于军用飞机维修保障系统的效能评估是可行的,可为军用飞机维修保障效能评估提供借鉴。     

基于改进匈牙利算法的航空企业动态调度方法研究

航空零件生产调度时,应尽量满足多目标和实时性的要求。结合某航空制造企业的实际生产情况,提出一种基于"穷尽成对比较"技术和改进匈牙利算法的动态调度方法。首先以航空零件调度时的最大完工时间、生产加工成本以及生产能耗为优化目标,构建柔性作业车间多目标动态调度数学模型;然后利用基于"穷尽成对比较"技术的权重参数调节模型对数学模型中各个目标的权重参数进行实时动态调整;最后以各个目标的加权值为总目标,采用改进匈牙利算法求得工序的最优分配结果。结果表明:与传统的动态调度方法相比,动态调度方法能够有效地提高航空企业的生产效率、减少航空企业的生产加工成本并降低对环境的污染,具有较好的综合调度性能。     

基于状态维修的预防性维修策略优化模型研究

基于状态的维修着眼于系统的具体运行状况,对被监测装备的真实状态进行评估,克服了维修的盲目性,可有效减少由于维修不足而引起的意外故障和由于维修过剩带来的资源浪费。提出一种基于状态的多部件系统非周期预防性维修计划的优化方法,引入"机会维修阈值"概念,优化不同维修方式转移概率的表达式,将多个维修作业按照机会维修阈值进行归并,运用马尔可夫决策过程理论,给出不同维修方式转移概率的表达式和目标函数,并进行算例分析。结果表明:提出的优化模型减少了预防性维修活动,节约了维修工时和经费,可为系统预防性维修策略决策提供参考。     

浅析民用飞机研制总体参数优化

分析了在概念设计阶段民用飞机总体设计工作内容和工作流程,并结合时下国外在用软件优缺点,构思了民用飞机总体设计参数综合优化的技术方案,包括优化设计架构、分析模型和优化算法等,为形成具有自主知识产权的民机总体设计综合分析和优化程序打下基础。     

Shaping low-thrust trajectories with thrust-handling feature

Shape-based methods are becoming popular in low-thrust trajectory optimization due to their fast computation speeds. In existing shape-based methods constraints are treated at the acceleration level but not at the thrust level. These two constraint types are not equivalent since spacecraft mass decreases over time as fuel is expended. This paper develops a shape-based method based on a Fourier series approximation that is capable of representing trajectories defined in spherical coordinates and that enforces thrust constraints. An objective function can be incorporated to minimize overall mission cost, i.e., achieve minimum $\mathrm{\Delta }V$. A representative mission from Earth to Mars is studied. The proposed Fourier series technique is demonstrated capable of generating feasible and near-optimal trajectories. These attributes can facilitate future low-thrust mission designs where different trajectory alternatives must be rapidly constructed and evaluated.     

Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.     

Upgrading CCIR's foF2 maps using available ionosondes and genetic algorithms

We have developed a new approach towards a new database of the ionospheric parameter $f_{o}F2$. This parameter, being the frequency of the maximum of the ionospheric electronic density profile and its main modeller, is of great interest not only in atmospheric studies but also in the realm of radio propagation. The current databases, generated by CCIR (Committee Consultative for Ionospheric Radiowave propagation) and URSI (International Union of Radio Science), and used by the IRI (International Reference Ionosphere) model, are based on Fourier expansions and have been built in the 60s from the available ionosondes at that time. The main goal of this work is to upgrade the databases by using new available ionosonde data. To this end we used the IRI diurnal/spherical expansions to represent the $f_{o}F2$ variability, and computed its coefficients by means of a genetic algorithm (GA). In order to test the performance of the proposed methodology, we applied it to the South American region with data obtained by RAPEAS (Red Argentina para el Estudio de la Atmósfera Superior, i.e. Argentine Network for the Study of the Upper Atmosphere) during the years 1958–2009. The new GA coefficients provide a global better fit of the IRI model to the observed $f_{o}F2$ than the CCIR coefficients. Since the same formulae and the same number of coefficients were used, the overall integrity of IRI’s typical ionospheric feature representation was preserved. The best improvements with respect to CCIR are obtained at low solar activities, at large (in absolute value) modip latitudes, and at night-time. The new method is flexible in the sense that can be applied either globally or regionally. It is also very easy to recompute the coefficients when new data is available. The computation of a third set of coefficients corresponding to days of medium solar activity in order to avoid the interpolation between low and high activities is suggested. The same procedure as for $f_{o}F2$ can be perfomed to obtain the ionospheric parameter M(3000)F2.     

基于故障行为的惯导产品贮存寿命试验设计

以典型惯导产品加速度计贮存寿命评估为例,提出了一种基于故障行为模型的加速退化试验设计方法,解决了缺乏预试验数据情况下试验应力水平设计的问题。在通过对产品特性和主机理分析基础上,确定表征产品贮存寿命特征参数及试验应力,构建了考虑材料等分散性的加速度计故障行为模型,用以描述参数加速时变规律的特性。在试验应力最高水平确定的情况下,基于给定置信度和故障行为模型,以高应力水平下退化量的置信下限与低应力水平下退化量的置信上限不交叉为原则,采用基于仿真的加速退化试验设计方法,优化确定低应力和中间应力水平,从而设计给出试验剖面,并给出了加速退化试验方案优选流程,最终从备选方案集中确定加速度计试验最优方案。通过实际案例分析,验证了该方法的有效性。      

多模函数优化的改进花朵授粉算法

为了探讨花朵授粉算法（FPA）在解算多模函数优化问题中存在的不足,通过定义种群多样性及差异性指标,定性分析了FPA在多模复杂函数优化中的寻优缺点。基于模拟退火思想优化全局授粉过程,并利用Nelder-Mead单纯形搜索技术对花朵局部授粉进行重构,提出一种新的花朵授粉寻优架构。仿真结果表明,相对于基本的FPA、布谷鸟算法、萤火虫算法,改进花朵授粉算法能够有效避免陷入局部最优,具备优异的全局勘探和局部开采能力,对多模优化问题具有一定优势。