Sintered silver finite element modelling and reliability based design optimisation in power electronic module

This paper discusses the design for reliability of a sintered silver structure in a power electronic module based on the computational approach that composed of high fidelity analysis, reduced order modelling, numerical risk analysis, and optimisation. The methodology was demonstrated on sintered silver interconnect sandwiched between silicon carbide chip and copper substrate in a power electronic module. In particular, sintered silver reliability due to thermal fatigue material degradation is one of the main concerns. Thermo-mechanical behaviour of the power module sintered silver joint structure is simulated by finite element analysis for cyclic temperature loading profile in order to capture the strain distribution. The discussion was on methods for approximate reduced order modelling based on interpolation techniques using Kriging and radial basis functions. The reduced order modelling approach uses prediction data for the thermo-mechanical behaviour. The fatigue lifetime of the sintered silver interconnect and the warpage of the interconnect layer was particular interest in this study. The reduced order models were used for the analysis of the effect of design uncertainties on the reliability of the sintered silver layer. To assess the effect of uncertain design data, a method for estimating the variation of reliability related metrics namely Latin Hypercube sampling was utilised. The product capability indices are evaluated from the distributions fitted to the histogram resulting from Latin Hypercube sampling technique. A reliability based design optimisation was demonstrated using Particle Swarm Optimisation algorithm for constraint optimisation task consists of optimising two different characteristic performance metrics such as the thermo-mechanical plastic strain accumulation per cycle on the sintered layer and the thermally induced warpage.     

三维地质建模的应用示范——以白音诺尔铅锌矿为例

以国产DIMINE三维矿业软件为基础,系统收集整理了白音诺尔铅锌矿南矿段1号矿脉群750~800 m中段地质勘探资料,通过建立地质数据库,系统建立矿岩三维模型、数据的分析与处理以及资源储量估算整个流程,对比矿体与围岩模型特点、普通克里克估算方法与应用特点,研究结果表明：圈定矿体5条,模型显示矿体主要赋存在闪长玢岩与结晶灰岩接触带上,属典型的矽卡岩型矿床,空间展布形态呈囊状、大脉状、透镜状,整体北东走向;依据地质统计学理论,拟合求得理论变异函数应用于普通克里格估值,分别估算控制和探明资源量合计18.66万t,Pb+Zn品位为4.04%;与传统方法相较,普通克里格估算误差均在合理的范围之内,应用效果良好,估算精度高,可为矿山采矿设计提供参考,为实现矿山的数字化提供重要的理论依据。     

一种融合天地基多源数据的电离层反演方法

数据融合是观测数据稀疏条件下电离层精确反演的重要技术途径.文中提出了一种融合天地基多源数据的电离层反演方法.选择地基GNSS、低轨卫星(Low Earth Orbiting,LEO)掩星、卫星信标及垂测仪等手段为观测系统,国际参考电离层(International Reference Ionosphere,IRI)为背景模型,利用改进的克里格插值及乘法代数重构方法实现多源数据的有效融合.以中国区域为例,观测系统模拟试验的结果表明:文中提出的方法能将各类观测资料有效地融合到背景模式中,反演得到的总电子含量及电子密度误差相比经验模型均有显著降低;在地基GNSS观测的基础上,融入地基垂测与天基掩星可有效提升电子密度的反演精度.相关研究结果可为中国现有电离层观测系统的优化提供科学依据.     

地质统计学及其在某铜矿山储量计算中的应用

首先论述了地质统计学的基本理论及应用其计算地质储量的方法步骤；然后以计算机为工具，运用地质统计学对某铜矿山铜金属储量进行了分析计算，并将计算结果与真实储量进行了对比分析。通过对比分析，认为运用地质统计学计算储量具有先进性及实用性。     

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research,a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation.The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs.In this procedure,the Artificial Neural Network was trained,tested and validated using assay values obtained from exploratory drillholes.Next,the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively.Finally,the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model.The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades.The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement.It could also be an effective mineral reserve evaluation method that could produce optimum block model for mine design.     

Multilevel surrogate modeling of an amine scrubbing process for CO2capture

Surrogate models provide a powerful method for simplifying calculations within complex simulations. While surrogate models are broadly applied within chemical engineering, little research exists investigating the level of surrogacy's impact on a simplified process model. In this work, artificial neural networks (ANN) and Kriging models are used as surrogate models at the process, process unit, and thermodynamic levels for a CO₂ amine scrubbing process. The surrogated models are evaluated against an Aspen Plus simulation for accuracy, convergence behavior, computational cost, and ability to extrapolate. The thermodynamic and process unit models can better handle discontinuous, non-smooth behavior, and convergence issues in the surrogated truth model, but poor conditioning in the final system of equations results in a lower accuracy and convergence rate than the process level surrogate. Beyond model accuracy, availability of diverse data, intended re-usability, and the desired outputs must be considered when selecting a level of abstraction.     

基于主动学习Kriging模型的地下管线抗震可靠度分析

为提升地下承插式接口铸铁管线抗震可靠度的计算效率,提出基于主动学习Kriging代理模型的Monte Carlo模拟方法(AK-MCS)进行地下管线抗震可靠度计算.采用梁单元模拟管线结构,均布弹簧反映管土相互作用,接口弹簧模拟邻接管道约束作用,建立了地下管线地震响应分析模型;考虑管线接口允许位移、管线埋深、土体容重和内...     

基于再淹没现象的RBF神经网络和Kriging的代理模型应用及误差分析

为了将代理模型应用于核工程反应堆热工水力现象中,以再淹没现象为研究对象,构建代理模型,并通过控制实验参数的数量,来化解精度与效率之间的矛盾。首先使用拉丁超立方方法抽取输入样本,并通过RELAP5建模获取输出样本,由MATLAB分别构建RBF神经网络代理模型和Kriging代理模型,对包壳峰值温度（PCT）进行拟合。然后分析比较了2种代理模型的拟合结果,发现2种代理模型的相对误差值均小于0.15%,均适用于再淹没现象;Kriging代理模型的拟合精度高于RBF神经网络代理模型。     

基于Kriging统计的移动曲面拟合

通过方位取点法选取采样点,移动拟合出二次曲面来逼近实际地形。对采样点处的逼近误差进行Kriging统计,获得待定点处高程逼近误差的最优估值。进行了基于Kriging统计的移动曲面拟合综合模型的模拟计算,通过单一曲面模型和Kriging统计模型的逼近误差及精度的比较,验证了综合内插模型的优越性。     

基于Kriging近似模型的轿车翼子板冲压工艺优化

近似模型法是目前最有代表性的高效优化方法。因基于多项式的近似模型很难满足板料成形的高度非线性,所以基于Kriging近似模型法,以翼子板的各拉深筋单位长度上所受的拉深筋阻力及压边力为设计变量,以成形后板料的最小厚度为响应量,建立近似模型。通过自适应模拟退火算法寻优,并约束最小厚度的变化范围,提高优化效率。通过优化,成形后板料的最小厚度从0．521mm增加至0．544mm,消除了局部破裂趋势,有效地改善了翼子板的成形质量。研究表明,Kriging近似模型法对板料成形有较高的精度。