An adaptive Kriging method with double sampling criteria applied to hydrogen preparation case

Kriging model has been widely used to approximate expensive black-box problems in many engineering design fields. How to choose an appropriate sampling strategy to produce new expensive updated points is crucial. For this purpose, an adaptive Kriging method with double sampling criteria (AKM-DSC) is proposed. During every iteration of it, maximum curvature criterion and maximum variance criterion based on Kriging model are respectively optimized by trust region (TR) strategy to produce two candidate points. And then, a new screening method is used to determine final expensive-evaluation points from the two candidates. The proposed method is compared with the two typical Kriging modeling methods. The comparison results of seventeen benchmark functions verify that the proposed method can generate higher accuracy Kriging model. Finally, a hydrogen preparation case illustrates the engineering application value of the AKM-DSC method.     

Multi-objective optimisation using surrogate models for the design of VPSA systems

Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO₂/N₂ separation. A 5 times reduction in computational effort is observed.     

Efficient multi-criteria optimization on noisy machine learning problems

Recent research revealed that model-assisted parameter tuning can improve the quality of supervised machine learning (ML) models. The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches. However, the advantages of the tuning often came along with high computation times, meaning a real burden for employing tuning algorithms. While the training with a reduced number of patterns can be a solution to this, it is often connected with decreasing model accuracies and increasing instabilities and noise. Hence, we propose a novel approach defined by a two criteria optimization task, where both the runtime and the quality of ML models are optimized. Because the budgets for this optimization task are usually very restricted in ML, the surrogate-assisted Efficient Global Optimization (EGO) algorithm is adapted. In order to cope with noisy experiments, we apply two hypervolume indicator based EGO algorithms with smoothing and re-interpolation of the surrogate models. The techniques do not need replicates. We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling (LHS), as well as EGO variants with replicates.     

基于PSO-BP数据融合的环境监控系统设计

传统的温室大棚监控系统一般都采用手动布线,存在控制难度大、成本高等问题,已不能满足农业发展的需求。针对上述问题,该文设计了一款基于PSO-BP的无线温室大棚环境监控系统。在大棚内部署Zig Bee传感器节点网络采集环境信息,利用ECDS算法选择路由节点。同时为了能够直观地展示大棚内各环境信息的分布情况,使用Kriging方法构建环境信息分布图。构造了反馈调节系统:由于大棚的各因数之间是相互作用的,为了有一个准确的决策结果,该系统将BP神经网络和PSO算法结合。利用改进后算法良好的映射能力和优化能力,对温室数据进行融合,精确决策。实验结果表明,该系统能够合理分配路由节点节省网络开销,较好地展示环境信息分布情况,同时能够精确地调节大棚环境。     

改进克里金插值算法的井下无线定位指纹库构建方法

针对煤矿井下巷道中构建一个采集点密集分布的无线定位指纹库,需要逐点采集信号,会耗费大量的人力和时间,为此提出基于模拟退火SA(Simulated Annealing)人工蜂群ABC(Artificial Bee Colony)混合算法优化克里金(Kriging)插值算法的构建井下无线定位指纹库的方法。首先通过无线网络采集井下巷道部分采集点的指纹数据,并建立采集指纹数据库。其次利用采集指纹数据库构建Kriging插值算法模型,再通过SA-ABC算法对变异函数的参数寻优,建立SA-ABC-Kriging插值算法模型。再次用插值算法估算出预测点的信息数据,并建立插值指纹数据库。最后将采集指纹数据库和插值指纹数据库构建井下无线定位指纹库。实验表明,该构建方法比传统Kriging插值算法的定位精度和插值精度更高,而且可减少50%的人工采集指纹数据工作量。     

基于地统计学的柬埔寨年降水量数据空间插值

为了更好地分析区域水资源量、水文平衡及灌溉需水量,需要对降水的时空分布进行定量分析。由于气象站点布设的局限性,针对资料短缺地区的空间插值就显得尤为重要。以柬埔寨的20个气象站降水数据为插值实验样本,研究基于Arc GIS的空间插值和分析功能,探讨普通克里金、简单克里金、析取克里金及经验贝叶斯克里金法等4种地统计学插值法的优劣及其适用性。通过插值误差分析来检验插值精度,并模拟年降水量的空间分布图来进行比较分析,结果表明经验贝叶斯克里金插值法最优。研究结果可为资料短缺地区降水量空间插值提供参考。     

Optimal curved fibre orientations of a composite panel with cutout for improved buckling load using the Efficient Global Optimization algorithm

Composite structures with cutouts (like panels with holes) are a challenge to design because discontinuities of this kind provoke stress concentrations and become critical regions. With curved fibres, the effect of these discontinuities can be decreased by choosing the fibre paths properly. In this article, fibre-path optimization to improve the buckling load of laminated composite panels with cutouts is studied. Two fibre path parameterizations are tested: the usual curvilinear Cartesian and the radial one, proposed in this article, in which the fibre orientations vary linearly with the Euclidean distance from the centre of the panel. To reduce the simulation costs associated with the optimization, the Efficient Global Optimization (EGO) algorithm is used. EGO is a technique based on a stochastic process approach (Kriging) that approximates expensive-to-evaluate functions and sequentially maximizes the expected improvement to update the surrogate at each iteration. A stiffened panel with a cutout subjected to compression and in-plane shearing loads is analysed. The results show that the buckling load when curved fibres are used is substantially higher than the buckling load for straight-fibre laminates. In addition, the optimization framework indicates a low final computational burden.     

An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis

In this paper, the active learning Kriging model (ALK), which has been studied extensively in recent years, has been expanded by combining with the directional importance sampling (DIS) method. The directional sampling method can reduce the dimensionality of the variable space by random sampling or interpolation in the direction of vector diameter, which can improve the efficiency of reliability analysis. It is especially suitable for the surfaces whose limit state is spherical or near-spherical. By introducing the control coefficient and constructing the directional importance sampling density function, the sampling efficiency can be further improved in the design point domain. A novel reliability analysis method called ALK-DIS method is proposed. The greatest advantage of the proposed method is its ability on great computational efficiency and dealing with small failure probability problem In addition, due to the excellent performance of directional sampling method in dealing with multi-failure model reliability problems, the ALK-DIS method has the advantage of being applied to system reliability analysis in this paper successfully. The applicability, feasibility and efficiency of the proposed method are proved on examples which contain linearity equation, non-linear numerical example, non-linear oscillator and system reliability engineering problems.     

基于DTW的地磁指纹定位方法

针对动态时间规整匹配(DTW)的奇异性问题,利用克里金插值方法(Kriging)基于156个参考点插值得到545个指纹点建立后台指纹库以减少离线采集的工作量,采用二次加权质心算法对DTW进行改进,首先利用一次加权质心算法获得结果位置点的初始位置,再用二次质心加权算法对定位结果位置点进行偏远误差点的剔除,最终获得可靠性较高的定位结果。实验表明,利用Kriging可以节省71.4%的工作量;在走廊和大厅的环境中定位精度分别可以达到2.01m和4.19m。改进的DTW在长廊和大厅环境中的平均定位误差分别为1.64m和2.74m,较原算法在定位精度上有明显提升。     

基于流固耦合及Kriging模型的离心泵叶轮优化

为提高离心泵叶轮的水力效率及结构性能,提出了流固耦合仿真技术与代理模型相结合的优化设计方法。通过均匀试验设计与CFD/CSD单向耦合仿真获得了样本数据,进而运用Kriging方法,分别建立离心泵叶轮效率、离心泵叶轮最大变形、离心泵叶轮最大应力与影响因素之间的代理模型。在此基础上,采用SQP算法求解以水力效率最大为目标函数,且考虑了离心泵叶轮的强度和刚度约束的优化模型。将该方法用于某离心泵叶轮的优化设计,结果表明取得了很好的效果。