基于自适应优化方法的响应时间数据分析

温度传感器的响应时间是其重要的特性参数,反应了其动态测试性能。在实际测试中,对于响应时间测试数据采用人工读取的方法得到结果,存在人为因素较多,响应时间结果多次读取不一致,精度难以统一的问题。因此提出采用基于Kriging代理模型的自适应序贯优化方法对测试数据进行寻优拟合计算。通过理论阶跃响应曲线的拟合结果,并加入适当修正,最终得到温度传感器的响应时间计算结果。计算结果表明,该方法可以自动精确计算得到温度传感器的响应时间结果,同时可以对同一温度传感器的多次测试数据进行对比量化判断。与传统人工读取方法相比,该方法具有计算精度统一,自动高效,人为因素较少等优势。     

基于Kriging模型和遗传算法的齿轮修形减振优化

针对齿轮修形优化时计算啮合刚度计算量大、计算精度低、操作繁琐等问题,提出一种基于Kriging模型和遗传算法的齿轮减振修形优化算法.以典型直齿轮传动为例开展齿轮修形优化,通过拉丁抽样建立Kriging模型,解决齿轮修形优化的多响应和隐式函数的问题,通过Kriging预测的啮合刚度与有限元法的对比可知,时变啮合刚度函数各参数的误差最大值为7.79×10-5,1.20×10-3及1.30×10-4,验证了Kriging多响应预测啮合刚度函数的精确性.将Kriging预测函数代入直齿轮啮合传动的动力学微分方程,采用遗传优化算法时将齿轮动态传动误差响应波动最小作为优化目标,得到最优的齿轮修形参数.算例表明:相比于ISO(International Standardization Organization)修形和未修形的齿轮,该算法的减振效果最好,验证了基于遗传算法与Kriging模型对齿轮进行修形优化的正确性、高效性.相比于直接采用有限元法进行齿轮修形优化,该算法计算时间由26.91d减小为2.24h,证明了该算法计算效率的优越性.      

Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10⁻⁵) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.     

Variable-fidelity optimization with design space reduction

Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity (LF) model provides physics-based approximation of the high-fidelity (HF) model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.     

Aerodynamic design optimization of nacelle/pylon position on an aircraft

The arbitrary space-shape free form deformation (FFD) method developed in this paper is based on non-uniform rational B-splines (NURBS) basis function and used for the integral parameterization of nacelle-pylon geometry. The multi-block structured grid deformation technique is established by Delaunay graph mapping method. The optimization objects of aerodynamic characteristics are evaluated by solving NavierStokes equations on the basis of multi-block structured grid. The advanced particle swarm optimization (PSO) is utilized as search algorithm, which com-bines the Kriging model as surrogate model during optimization. The optimization system is used for optimizing the nacelle location of DLR-F6 wing-body-pylon-nacelle. The results indicate that the aerodynamic interference between the parts is significantly reduced. The optimization design system established in this paper has extensive applications and engineering value.     

基于加速度频响函数小波分解的模型修正方法

为提高模型修正效率，满足修正方法对于实测环境噪声的鲁棒性，将Kriging模型和小波分解引入加速度频响函数模型修正。首先，将加速度频响函数进行小波分解，用得到的第1层幅值较大的小波系数来表征原频响函数。其次，采用拉丁超立方抽样对初选待修正参数进行设计，根据设计结果对各参数进行灵敏度分析，从而确定模型修正的待修正参数，以待修正参数作为Kriging模型输入，所对应的小波系数作为Kriging模型输出，通过混合灰狼算法寻得最优Kriging模型相关系数，建立精确有效的Kriging模型。最后，以目标加速度频响函数小波分解的小波系数与Kriging模型输出的小波系数差值最小为目标，通过水循环算法求解模型待修正参数。数值算例表明，所提模型修正方法具有良好的修正效果，即使在加速度频响函数中加入信噪比为5 dB的高斯白噪声时，修正误差也低于4%，证明了该方法对于随机噪声的鲁棒性。     

基于Kriging模型的离心叶轮结构优化设计

研究了以减重为目标的航空发动机离心叶轮结构优化问题,将Kriging模型与遗传算法相结合,应用拉丁方试验设计和有限元分析生成初始样本数据,利用初始样本数据建立离心叶轮重量和最大应力等状态参数的Kriging模型,运用遗传算法对该Kriging模型在设计空间进行全局寻优,利用有限元分析方法计算近似最优设计点的状态参数,并以此更新已有的设计样本数据,不断提高Kriging模型的近似精度.计算结果表明,基于Kriging模型-遗传算法的离心叶轮结构优化设计方法不仅可以获得良好的优化结果,比直接用遗传算法寻优减少了大量计算时间,提高了设计效率,同多项式模型-遗传算法相比也有效率优势.     

空间可展开结构区间模型修正方法

传统的模型修正未考虑工程中存在的几何尺寸、材料参数、间隙等不确定性,修正后有限元模型预测精度较低。为提高有限元模型的预测精度,准确预测结构的静动力学特性,对考虑参数不确定性的模型修正进行了研究,提出了一种基于Kriging模型和泛灰数的区间模型修正方法。首先,通过灵敏度分析确定待修正参数,并以修正参数为变量,构造基于Kriging模型的区间响应目标函数;其次,引入泛灰数将区间优化问题转换为区间上限和区间直径两个全局优化问题;然后,利用Kriging模型结合遗传算法给出修正后的参数区间形式;最后,通过该方法对含铰链间隙的某空间可展开结构进行了模型修正。结果表明,修正后参数区间与真实区间重合度较高,修正后结构响应预测区间与实际区间吻合,验证了方法的有效性,为大型空间可展开结构的模型修正提供了一个有效可行的途径。     

考虑机翼尾流影响的运输类飞机后体气动外形优化设计（英文）

在某典型运输机翼身组合体的构型上,进行了考虑机翼尾流影响的机身后体气动外形优化设计研究。基于翼身组合体构型建立了考虑尾流影响和部分工程约束的优化设计系统,并对后体构型在巡航状态下进行了优化设计。以比较适合描述后体变形的NURBS样条基函数为空间控制体属性引入FFD自由变形技术,通过在FFD控制框架对该运输机后体进行了空间属性构建。采用无限差值动网格技术提高空间网格的更新效率并保证网格质量。利用改进Kriging代理模型、量子粒子群优化算法提高优化效率和全局寻优能力。为减小巡航阻力,对某运输机后体的上翘角、截面形状等参数进行了优化设计,优化结果显示,设计后的机身气动特性明显提高。     

基于正交法和Kriging模型的液体火箭发动机液膜冷却优化

通过FLUENT对火箭发动机推力室中跨临界甲烷液膜冷却稳态流场进行数值传热计算。根据正交法设计试验,得到不同膜孔孔径、轴向夹角、径向夹角和孔型四个影响因素共同作用下的冷却效果,选出最优的膜孔几何参数组合.在采用最优膜孔几何参数组合的条件下,基于最优拉丁超立方抽样建立Kriging模型,利用遗传算法得到多目标条件下最优的跨临界液膜质量流量、冷却环带的分配比和位置。结果表明,正交法和Kriging模型可以解决液体火箭发动机液膜冷却优化高设计成本和数值噪声问题。正交试验设计考虑的因素中,影响冷却效率和不均匀度的最大的因素依次为孔型、孔径、径向夹角和轴向夹角。最优的几何参数组合为孔径0.003mm,轴向夹角45°,径向夹角15°,孔型为扩散型。建立的Kriging模型能准确反映液膜质量流量、液膜分配比和冷却环带位置与目标函数的关系。最终得到的优化方案平均冷却效率提高4.9%,不均匀度减少0.025,比冲损失增加0.37%,总目标函数提高184%。优化后涡对的不对称性使得冷却剂展向分布更加均匀,同时反向涡对衰减更快,增强了液膜的附壁性,从而提高冷却效果。