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。     

用Kriging方法构建中纬度区域电离层TEC地图

提出了中国中北部及周边(30°N～55°N,70°E～140°E范围内)区域电离层电子浓度总含量(TEC)地图(简称CNC TEC Map)的Kriging算法.比较了目前被广泛使用的电离层模型(Klobuchar,IRI和JPLGIM)与真实的TEC分布的符合情况;比较了常数漂移(普通Kriging方法)、线性漂移和二次漂移的Kriging方法;在三种不同的时间分辨率(5min,30min和120min)下,比较了Kriging算法、就近插值算法和多项式回归算法(5阶)绘制CNC TEC Map的效果.结果显示在本文研究区域内常用的电离层模型与真实TEC分布存在较大的区别;使用普通Kriging方法就可以较好的表示华北地区TEC分布;Kriging方法的结果优于其余两种插值算法,尤其是当时间分辨率提高到5 min时,Kriging算法的优势比较明显.      

An efficient algorithm for calculating Profust failure probability

For efficiently estimating the Profust failure probability based on probability input variables and fuzzy-state assumption, a General Performance Function(GPF) expression is established under the strict mathematical derivation for the Profust reliability model. By constructing the GPF,the calculation of the Profust failure probability can be transformed into the calculation of the traditional failure probability. Then various existing methods for the traditional failure probability can be used to estimate the Profust failure probability. Due to the high efficiency of the Adaptive Kriging(AK) model and the universality of the Monte Carlo Simulation(MCS), AK inserted MCS(abbreviated as AK-MCS) has been proven to be an efficient method for estimating the failure probability. Therefore, the AK-MCS combined with the GPF(abbreviated as AK-MCS + GPF)is proposed for estimating Profust failure probability. The proposed method greatly reduces the computational cost while ensuring the accuracy. Finally, four examples are given to validate the proposed AK-MCS + GPF. The results of the examples show the rationality and the efficiency of the proposed AK-MCS + GPF.     

压气机叶片加工误差影响不确定分析

为了正确评估加工误差的影响,提出了一种考虑加工误差大小、分布随机性的数值研究方法以实现叶片性能不确定性分析。采用NURBS(非均匀有理B样条) 方法实现叶型参数化,通过检测及统计手段获取误差真实分布,运用蒙特卡洛模拟生成随机样本,并训练Kriging代理模型进行不确定性分析,得到了叶片加工误差对气动性能的影响。结果表明:加工误差对气动性能的影响与所处工况有关,最高使得损失增加0.853%;叶型性能对于吸力面表面型线更为敏感,在加工及检测过程应给予更高要求。      

Rotorcraft flight endurance estimation based on a new battery discharge model

To avoid the numerical complexities of the battery discharge law of electric-powered rotorcrafts,this study uses the Kriging method to model the discharge characteristics of Li-Po batteries under standard conditions.A linear current compensation term and an ambient temperature compensation term based on radial basis functions are then applied to the trained Kriging model,leading to the complete discharged capacity-terminal voltage model.Using an orthogonal experimental design and a sequential method,the coefficients of the current and ambient temperature compensation terms are determined through robust optimization.An endurance calculation model for electric-powered rotorcrafts is then established,based on the battery discharge model,through numerical integration.Laboratory tests show that the maximum relative error of the proposed discharged capacity-terminal voltage model at detection points is 0.0086,and that of the rotorcraft endurance calculation model is 0.0195,thus verifying their accuracy.A flight test further demonstrates the applicability of the proposed endurance model to general electric-powered rotorcrafts.     

基于梯度增强型Kriging模型的气动反设计方法

基于Kriging模型的代理优化算法目前在气动优化设计中得到了广泛应用。但在高维(设计变量大于30个)气动优化中,计算量过大的问题对其进一步发展产生了严重制约。将翼型和机翼气动反设计问题转化为优化问题,采用Adjoint方法进行快速梯度求解,利用基于梯度增强型Kriging(GEK)模型的代理优化算法分别开展了18、36和108个设计变量的气动反设计。首先,通过采用在设计空间局部建立GEK模型的方法成功地将基于代理优化算法的气动反设计问题的维度拓展到了100维以上。其次,研究了梯度计算精度对基于GEK模型的反设计的影响,发现梯度精度越高,反设计的最终效果越好,同时效率相当。最后,通过不同维度的气动反设计算例,比较了改进拟牛顿法(BFGS)、基于GEK模型和Kriging模型的代理气动反设计方法,结果表明基于GEK模型的代理优化算法的效率大幅度高于基于Kriging模型的代理优化算法,并且维度越高,效率优势越明显;同时,基于GEK模型的代理优化算法在优化效果及分析程序调用次数上相比于BFGS方法也略有优势。     

涡轮叶片的气动-热-结构多学科设计优化研究

涡轮叶片设计是典型的多学科问题,在保证结果精度的同时必须重视计算效率.通过控制样本的数量和质量,近似模型能够在保证一定精度的前提下,简化多学科优化过程中的数据管理,并提高优化效率.通过分析涡轮转子叶片的气动-热-结构三个学科的设计过程和数据传递关系,充分利用各学科现有的分析工具,建立了涡轮叶片的气动-热-结构多学科优化设计框架.对某涡轮转子叶片分别使用Kriging近似模型和精确分析方法进行优化对比,可以看出两者的结果误差约为1.86%,而效率提高了将近46%,表明采用近似方法的优化结果在工程上是可用的,而且在计算效率更占优势.      

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.     

耦合梯度与分级Kriging模型的高效气动优化方法

Kriging代理模型中引入梯度信息能够提高模型的预测精度,但常规耦合梯度的方法都有不足之处。本文结合分级Kriging模型,提出了一种变可信度分级Kriging模型耦合梯度（GEHK）信息的新方法。首先利用梯度信息,选取扰动步长得到初始样本点附近的派生点,以派生点拟合出能够预测目标函数趋势的低精度Kriging模型。然后以初始样本点修正该模型得到高精度的Kriging模型。翼型减阻优化设计算例表明,与常规耦合梯度的Kriging模型相比,基于分级Kriging的梯度耦合方法对于扰动步长引入的误差不敏感,明显提高了模型预测精度,优化效率因此提升并使得目标函数值下降得更加迅速。相比Euler解作为低精度数据的常规分级Kriging模型,由梯度信息得到的派生点为模型提供了更准确的全局趋势预测,取得了更好的优化结果。本文方法成功应用于翼型多点减阻优化问题,说明该方法对复杂设计问题有很好的适应性。基于分级Kriging模型的耦合梯度方法克服了常规方法的缺点,提高了模型全局拟合精度,是一种优化效率更高的Kriging方法。