基于Kriging代理模型的飞行器结构刚度气动优化设计

大飞机具有轻质大柔性特点,使得气动/结构耦合作用增强,在设计过程中需要考虑这种耦合效应,直接调用CSD/CFD方法计算周期长,无法满足工程需要.代理模型方法由于能显著提高工程优化设计的效率,已广泛应用于飞行器气动外形优化设计中.采用Kriging方法建立代理模型,通过求解EI函数最大值得到需添加的样本点以更新代理模型,提高代理模型的拟合精度,结合改进的粒子群最优化方法对大飞机的结构刚度进行了优化设计.结果表明,该优化方法能够处理复杂目标的全局优化问题,在保证升力系数及纵向稳定性能不恶化的前提下,降低飞机巡航状态的飞行阻力.      

基于粒子群优化算法的光刻机光源优化方法EI北大核心CSCD

提出了一种基于粒子群优化算法的光刻机光源优化方法。将光源信息编码为粒子,利用图形误差作为评价函数,通过更新粒子的速度与位置信息不断迭代优化光源图形。对周期接触孔阵列和含有交叉门的复杂掩模图形的仿真验证表明,两者的图形误差分别降低了66.1%和27.3%,有效提高了光刻成像质量。与基于遗传算法的光源优化方法相比,该方法具有更快的收敛速度。另外,还研究了像差和离焦对本方法稳健性的影响。     

基于多角度表观反射率模型的交叉定标方法EI北大核心CSCD

为了满足多类型光学载荷在轨空间辐射基准传递的需求,减小角度差异对辐射定标频次和精度的影响,开展基于多角度表观反射率模型的交叉定标方法研究。利用高精度长时序卫星的多角度、高光谱表观反射率数据,构建地球稳定目标利比亚4的大气层顶表观反射率模型。通过区域匹配、时间匹配和云剔除后,利用该模型对2019—2020年风云三号D星中分辨率成像仪和AQUA/MODIS的可见-短波红外波段开展了205次交叉定标应用,并与同步星下点观测的交叉定标方法进行了26次比对。结果表明,两种方法的平均相对偏差优于2.1%,验证了该方法的适用性和准确性。该方法可以解决对人工测量定标场地表多角度数据的依赖,适用于观测天顶角差异0°~60°、观测波段400~2400 nm以内的多光谱、多角度载荷的交叉定标,减小交叉定标中角度匹配误差,显著提高多类型卫星的定标频次,为空间辐射基准传递和多载荷数据融合应用提供基础技术支撑。     

基于优化YOLOv4算法的行驶车辆要素检测方法北大核心CSCD

随着车辆数量的急剧增加,带来了一系列管理问题,智能交通系统是一种有效的解决方式。由于传统的目标识别方式受天气、距离、角度、光照等因素的影响较大,且基于原YOLOv4算法的驾驶员面部、手部等信息检测的准确率不高,提出一种基于优化YOLOv4算法的检测定位方法。在给原YOLOv4网络增加一个更小的检测尺度的同时,使用模糊ISODATA动态聚类算法对先验框数目进行优化,并使用真实十字路口数据集进行实验。实验证明,优化后的网络在训练集中的类间平均准确率为98.56%,检测帧频为41.43帧/s,均高于原网络。     

基于色块和服装色的颜色协调模型评测和优化EI北大核心CSCD

为了对现有颜色协调模型进行评测,选择了CIELAB颜色空间中均匀分布的24个颜色,分别以色块和服装为应用场景,组织观察者进行了颜色协调度评判实验.通过心理物理实验采集到了不同观察者的颜色协调度评判结果,分别对颜色协调原则和现有的颜色协调模型进行验证,发现以色块和服装为应用场景的颜色协调度呈现弱相关性(R2<0.126),现有颜色协调模型的预测结果与实验数据呈现弱相关性(R2<0.5).对Ou模型(基于匀色色块建立)和Luo&Ou模型(基于服装色建立)进行优化后,预测不同场景的颜色协调度性能有较大地改善,研究成果可为颜色协调度的预测提供一种研究方法和参考依据.     

基于水平集方法的二维Stokes多工况流动的拓扑优化EI北大核心CSCD

本文采用水平集方法进行了多工况条件下Stokes流动问题的拓扑优化,研究了进出口边界变化对流体拓扑优化结果的影响。在拓扑优化过程中,采用无需样条参数化的重新网格划分方法来实现物理场和水平集类方程交替求解。以最小能量耗散为目标,通过对三工况条件下流动通道的拓扑优化设计,表明多工况条件下的流动拓扑优化比单工况条件下获得的拓扑优化设计结果更为合理。     

基于Kriging模型的水中放电沉积能量优化分析

水中脉冲放电过程较为复杂,放电参数与放电沉积能量之间没有明确的函数关系。为了获得最佳沉积能量,明晰不同放电参数相互作用对沉积能量的影响,获得最佳放电参数组合,本文搭建了水中高压脉冲放电实验平台,结合Kriging代理模型探究了电压、电极间距和电导率三种放电参数对水中放电沉积能量的影响;利用遗传算法进行全局寻优,确定了最佳放电参数组合。研究结果表明：通过交叉验证评估该模型的均方根误差为6.95%,满足精度要求;外加电压一定时,在电极间距和电导率的协同作用下,沉积能量的变化呈现多峰值特性;在电压、电极间距和电导率分别为17 kV、2.28 mm和0.8 mS/cm的条件下产生的沉积能量最大,为最佳参数组合;通过实验验证了在最佳点的预测值和实际值相对偏差在8%以内。     

基于时变小世界模型的航空网优化评估

航空网的优化设计对于优化资源配置、改善航运效率、提高航空公司竞争力等具有重要的现实意义.而航线结构与航班计划密不可分.本文首先讨论了航空网的时变特征,揭示了航班频率与航线距离之间的时空耦合关联.通过构建时变空间小世界模型,揭示了时变条件下网络的最优结构指数与时空耦合强度的惟一约束关系.以运行总成本最小化为主要优化目标,提出了一种可以快速评估航线结构优化情况的方法.该方法能根据网络客流分布情况快速推算出航线网络的最优拓扑及相应的航班频率分布.并用2001—2010年中国航空网络数据对此方案进行实证研究,发现预测与实际数据基本符合,并逐渐趋于稳定.这一方法能把复杂问题简单化,对各个航空公司每年的航线航班调整是否合理,现有的航空网络是否在逐步优化做出动态评估.分析航空网络的发展趋势,从而对未来的优化提供建议.     

Combustion kinetic model development using surrogate model similarity method

An ideal combustion kinetic model needs to be validated by different experimental targets over a wide range of temperatures and pressures that represent operating conditions in real engines. However, conditions of laboratory experiments for model validation are often limited by the constraint of experimental techniques. In order to improve model predictions under certain conditions (for example, at a relatively higher pressure), it is often needed to use the experimental data obtained under other conditions. In this work, the surrogate model similarity (SMS) method is proposed to find the experimental conditions or targets for model optimisation under certain conditions where the experiments are hard to be conducted. The similarity coefficient is calculated by the cosine similarity between the characteristic coefficients (vectors) of the High Dimensional Model Representation (HDMR) models for different model predictions. A larger similarity coefficient represents a closer relationship between two model predictions. The experimental data with larger similarity coefficients could be more effective to model uncertainty reduction under the concerned conditions. To demonstrate this method, simulations were conducted for two selected combustion systems with hydrogen or methanol as the fuel. In addition to its strength in available experimental data selection for model optimization, this method can be used to screen out experimental targets with strong constraint effect beforehand, thus providing an effective way to maximise utilisation of experimental resources.     

ANN based surrogate model for key Physico-chemical effects of cavitation

Intense and localised physico-chemical effects realised by cavitation such as generation of hydroxyl radicals, high-speed jets, and very high energy dissipation rates are being harnessed for a wide range of applications from emulsions, crystallisation, reactions to water treatment and waste valorisation. Single cavity models are typically used to quantitatively estimate such localised effects of cavity collapse. However, these models demand significant computing resources for resolving fast dynamics and therefore are very difficult, if not impossible, to integrate with CFD based cavitation device or reactor scale models. This severely limits the utility of device/ reactor scale models in simulating key applications of interest. In this work, we present, for the first time, artificial neural network (ANN) based surrogate models which accurately represent complex physico-chemical effects of cavity collapse. Recently developed cavity dynamics model was used for generating training data set encompassing both acoustic and hydrodynamic cavitation. Appropriate methodology for training ANN was developed. A shallow three hidden layer dense ANN was found to be more effective for estimating three main effects of cavity collapse: jet velocity, •OH generation and localised energy dissipation rate. The performance of trained ANN was then evaluated by comparing the predictions with the totally unseen data obtained from the cavity dynamics model. The developed ANN was shown to simulate unseen data very well not just within the range of training data (interpolation) but also beyond (extrapolation). Algebraic equations representing ANN are included to facilitate incorporation in device/ reactor scale CFD models. The presented methodology and results will be useful for developing high-fidelity CFD models of cavitation devices/ reactors based on key physico-chemical effects of cavity collapse.     

A method for stochastic constrained optimization using derivative-free surrogate pattern search and collocation

Recent advances in coupling novel optimization methods to large-scale computing problems have opened the door to tackling a diverse set of physically realistic engineering design problems. A large computational overhead is associated with computing the cost function for most practical problems involving complex physical phenomena. Such problems are also plagued with uncertainties in a diverse set of parameters. We present a novel stochastic derivative-free optimization approach for tackling such problems. Our method extends the previously developed surrogate management framework (SMF) to allow for uncertainties in both simulation parameters and design variables. The stochastic collocation scheme is employed for stochastic variables whereas Kriging based surrogate functions are employed for the cost function. This approach is tested on four numerical optimization problems and is shown to have significant improvement in efficiency over traditional Monte-Carlo schemes. Problems with multiple probabilistic constraints are also discussed.     

A meshless method based on moving Kriging interpolation for a two-dimensional time-fractional diffusion equation

Fractional diffusion equations have been the focus of modeling problems in hydrology, biology, viscoelasticity, physics, engineering, and other areas of applications. In this paper, a meshfree method based on the moving Kriging inter- polation is developed for a two-dimensional time-fractional diffusion equation. The shape function and its derivatives are obtained by the moving Kriging interpolation technique. For possessing the Kronecker delta property, this technique is very efficient in imposing the essential boundary conditions. The governing time-fractional diffusion equations are transformed into a standard weak formulation by the Galerkin method. It is then discretized into a meshfree system of time-dependent equations, which are solved by the standard central difference method. Numerical examples illustrating the applicability and effectiveness of the proposed method are presented and discussed in detail.     

基于线性子链法的压水堆裂变产物源项一体化计算方法

核电厂设计基准源项计算可为核电厂安全评审提供依据,同时也是辐射屏蔽计算的基础。基于压水堆堆芯、一回路和气载源项的研究基础,类比衰变常数引入了迁移常数和核反应常数的概念,进而总结了一体化计算上述源项中裂变产物源项的源项方程。针对源项方程变系数、大型、稀疏和刚性的特点,在时间离散近似的基础上,基于线性子链算法编写程序求解了上述方程。通过与典型压水堆工程文件对比,证明了程序的正确性和必要性。     

基于线性子链法的压水堆裂变产物源项一体化计算方法

核电厂设计基准源项计算可为核电厂安全评审提供依据,同时也是辐射屏蔽计算的基础。基于压水堆堆芯、一回路和气载源项的研究基础,类比衰变常数引入了迁移常数和核反应常数的概念,进而总结了一体化计算上述源项中裂变产物源项的源项方程。针对源项方程变系数、大型、稀疏和刚性的特点,在时间离散近似的基础上,基于线性子链算法编写程序求解了上述方程。通过与典型压水堆工程文件对比,证明了程序的正确性和必要性。     

基于线性模型的自适应优化去雾算法

针对线性传输算法中透射率和大气光估计不足问题,提出一种基于线性模型的自适应优化去雾算法。利用边缘信息模型来增强初始透射率图的细节信息,使得复原后图像边缘区域细节更丰富;根据暗通道先验,得到自适应优化透射率,更好地处理包含景深区域图像;采用局部大气光估计方法代替四叉树方法,避免大气光估计不准确问题,并结合物理模型恢复图像。仿真实验在matlab2014中进行,实验结果表明,该算法具有较好的有效性和时效性。     

Complex coordinate rotation method based on gradient optimization

In atomic,molecular,and nuclear physics,the method of complex coordinate rotation is a widely used theoretical tool for studying resonant states.Here,we propose a novel implementation of this method based on the gradient optimization(CCR-GO).The main strength of the CCR-GO method is that it does not require manual adjustment of optimization parameters in the wave function;instead,a mathematically well-defined optimization path can be followed.Our method is proven to be very efficient in searching resonant positions and widths over a variety of few-body atomic systems,and can significantly improve the accuracy of the results.As a special case,the CCR-GO method is equally capable of dealing with bound-state problems with high accuracy,which is traditionally achieved through the usual extreme conditions of energy itself.