Double-stage Metamodel and Its Application in Aerodynamic Design Optimization

Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.

Contact Stress Reliability Analysis Model for Cylindrical Gear with Circular Arc Tooth Trace Based on an Improved Metamodel

Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis.In this study,a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel.To enhance the model’s accuracy,we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm,leveraging the strengths of each.Additionally,the“parental inheritance+self-learning”optimization model is used to fine-tune the KrigingMetamodel’s parameters.Following this,amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace(VH-CATT)gears using the optimized Kriging model was developed.We then examined how different gear parameters affect the VH-CATT gears’contact stress.Our simulation results show:(1)Improvements in R2,RMSE,and RMAE.R2 rose from0.9852 to 0.9974(a 1.22%increase),nearing 1,suggesting the optimized Kriging Metamodel’s global error is minimized.Meanwhile,RMSE dropped from3.9210 to 1.6492,a decline of 57.94%.The global error of the GA-IQPSO-Kriging algorithm was also reduced,with RMAE decreasing by 58.69%from 0.1823 to 0.0753,showing the algorithm’s enhanced precision.In a comparison of ten experimental groups selected randomly,the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress.Results revealed a maximum error of 12.11667 MPA,which represents 2.85%of the real value.(2)Several factors,including the pressure angle,tooth width,modulus,and tooth line radius,are inversely related to contact stress.The descending order of their impact on the contact stress is:tooth line radius>modulus>pressure angle>tooth width.(3)Complex interactions are noted among various parameters.Specifically,when the tooth line radius interacts with parameters such as pressure angle,tooth width,and modulus,the resulting stress contour is nonlinear,showcasing amultifaceted contour plane.However,when tooth width,modulus,and pressure angle interact,the stress contour is nearly linear,and the contour plane is simpler,indicating a weaker coupling among these factors.

A Double-Stage Surrogate-Based Shape Optimization Strategy for Blended-Wing-Body Underwater Gliders

In this paper,a Double-stage Surrogate-based Shape Optimization(DSSO)strategy for Blended-Wing-Body Underwater Gliders(BWBUGs)is proposed to reduce the computational cost.In this strategy,a double-stage surrogate model is developed to replace the high-dimensional objective in shape optimization.Specifically,several First-stage Surrogate Models(FSMs)are built for the sectional airfoils,and the second-stage surrogate model is constructed with respect to the outputs of FSMs.Besides,a Multi-start Space Reduction surrogate-based global optimization method is applied to search for the optimum.In order to validate the efficiency of the proposed method,DSSO is first compared with an ordinary One-stage Surrogate-based Optimization strategy by using the same optimization method.Then,the other three popular surrogate-based optimization methods and three heuristic algorithms are utilized to make comparisons.Results indicate that the lift-to-drag ratio of the BWBUG is improved by 9.35%with DSSO,which outperforms the comparison methods.Besides,DSSO reduces more than 50%of the time that other methods used when obtaining the same level of results.Furthermore,some considerations of the proposed strategy are further discussed and some characteristics of DSSO are identified.

基于MDR2023的元数据值域语义约束注册标准化模型研究

元数据注册(MetadataRegistry,MDR)是数据治理中元数据精确表达语义的必要前提。通过全面系统地分析国内外的MDR系统,发现国内外MDR更多关注基本数据元素,数据语义约束注册方面的研究缺乏。因此,首先基于ISO/IEC11179:2023(MDR2023)系列标准提出元数据语义约束外延分类模型,明确元数据语义约束范围,并选取其中的值域语义约束详细研究;其次,基于MDR2023标准提出元数据值域语义约束注册元模型,为元数据语义约束注册提供标准化且完整的注册算法流程,从而为元数据值域语义约束注册提供解决方案;最后,以石油领域著名的POSC标准为需求背景,对其中的值域语义约束进行注册,据此实现石油领域元数据值域语义约束的标准化,验证提出的元数据值域语义约束注册元模型的合理性和可行性。提出的元模型对于其他领域数据治理具有普适性。

基于最佳预后元模型的颗粒污垢特性全局敏感性分析

通过敏感性分析可识别出重要变量和无关变量,有效筛选数据降低模型复杂程度,并辅助优化设计。以内置转子传热管内的颗粒污垢为研究对象,采用欧拉-欧拉模型和颗粒污垢模型进行数值实验获取训练数据空间,基于最佳预后元模型开展全局敏感性分析,定量比较了颗粒直径、颗粒浓度、入口流速和入口温度的影响程度。对颗粒污垢四种沉积速率分析表明,入口温度对扩散沉积和热泳沉积的影响最显著,总影响分别为65.4%、58.6%;颗粒直径对湍流泳沉积和重力沉积的影响最明显,总影响分别为53.9%、75.0%。在此基础上,进一步分析了沉积率和污垢热阻的总影响、主影响及交互影响。结果表明:颗粒直径对沉积率和污垢热阻的影响均最大,总影响分别为52.7%、60.2%,各输入变量对沉积率和污垢热阻的交互影响的和分别为59.7%、42.5%,且随着四个输入变量的增加,沉积率和污垢热阻均增大。颗粒直径是影响内置转子传热管颗粒污垢问题的首要因素,在换热器设计中应予以重点考虑,其次是流体温度与流速。

基于多项式混沌展开法的涡流无损检测高效元模型辅助探测概率的分析

探测概率对于量化涡流无损检测系统的检测能力非常重要。模型辅助探测概率的参数需要大量试验或仿真数据才可以确定,往往难以实现。应用基于退化核函数加速的边界元法的数值模型,提出使用基于普通最小二乘法的多项式混沌展开算法的元模型提升三维涡流无损检测问题探测概率的效率。通过有限截面线圈检测金属板面槽的算例,引入线圈位置和提离距离为不确定传播的参数,测试结果表明,该元模型预测的模型参数与基于退化核函数加速的边界元法物理模型计算的模型参数相对误差在1%以内,极大降低了所需的计算开销。

Static recrystallization behaviors and mechanisms of 7Mo super-austenitic stainless steel with undissolved sigma precipitates during double-stage hot deformation

Static recrystallization(SRX)behaviors and corresponding recrystallization mechanisms of 7Mo super-austenitic stainless steel were studied under different deformation conditions.The order of influence of deformation parameters on static recrystallization behaviors,from high to low,is followed by temperature,first-stage strain and strain rate.Meanwhile,the effect of holding time on static recrystallization behaviors is significantly controlled by temperature.In addition,with the increase in temperature from 1000 to 1200°C,the static recrystallization mechanism evolves from discontinuous static recrystallization and continuous static recrystallization(cSRX)to metadynamic recrystallization and cSRX,and finally to cSRX.The cSRX exists at all temperatures.This is because high stacking fault energy(56 mJ m−2)promotes the movement of dislocations,making the deformation mechanism of this steel is dominated by planar slip of dislocation.Large undissolved sigma precipitates promote static recrystallization through particle-stimulated nucleation.However,small strain-induced precipitates at grain boundaries hinder the nucleation of conventional SRX and the growth of recrystallized grains,while the hindering effect decreases with the increase in temperature.

地铁列车IPIS系统设计建模方法

针对基于文档(TBSE)方法在设计地铁列车乘客信息系统(PIS)时所面临的设计集成度低、开发效率低、功能之间的联系性差及可追溯性差等问题,将PIS的3个主要功能进行模块化处理,提出智慧乘客信息系统(IPIS),并运用基于模型的系统工程(MBSE)思想对系统进行设计建模。提出了IPIS系统从黑盒到白盒的设计建模方法,涵盖了利益相关方分析、涉众需求分析、用例和用例场景分析、黑盒活动分析、白盒活动分析、功能分析、系统需求分析、系统架构设计8个阶段,并利用系统建模语言(SysML)构建IPIS系统功能元模型,以提高模型重用率。通过一个IPIS系统设计实例验证该方法的有效性和可行性。

基于书签法的学科关键能力水平划分研究

对学科关键能力的水平划分是研制和应用学业质量标准的基础。相比安戈夫法等传统水平划分方法,书签法在扩大评价工具适用范围、降低专家判断负担等方面具有明显优势,近年来在国际大型教育测评项目中得到越来越广泛应用。本研究聚焦科学教育领域的模型认知与模型实践能力,首先通过理论研究确定能力维度和水平预设,随后在实证研究中使用书签法厘定分界分数、描述表现水平,并据此提出七维度五水平的模型认知与模型实践能力表现框架。研究过程展现了应用书签法对学科关键能力进行水平划分的技术路线,对书签法的应用实践提出遵循规范流程、简化供给专家数据、平等讨论作出决策等操作建议。

电能表数据采集终端负载自适应均衡方法研究

针对电能表数据采集终端数据量及访问量高导致的负载失衡问题,研究基于元模型的电能表数据采集终端负载自适应均衡方法。将电能表数据采集终端的电能表数据作为模型元数据,经数据提取、转化、加载处理后,将处理完成的数据存储至元数据仓库内。在元数据负载大小未知条件下,创新性地采用基本负载均衡算法分配元数据仓库中元数据的负载,以求解元数据的负载量。当负载过大时,通过迁移负载低节点的目录子树,均衡处理了元数据的负载量,实现了电能表数据采集终端访问负载均衡。实例测试结果表明,该方法应对速度快。该方法降低了电能表数据采集终端负载,解决了元数据请求激增问题,提高了集群负载的均衡程度。

A Metamodeling Method Based on Support Vector Regression for Robust Optimization

Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.

OPTIMIZATION METHOD FOR VIRTUAL PRODUCT DEVELOPMENT BASED ON SIMULATION METAMODEL AND ITS APPLICATION

Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization problems in VPD. Optimization method based on simulationmetamodel for virtual product development is proposed to satisfy the needs of complex optimaldesigns driven by VPD. This method extends the current design of experiments (DOE) by variousmetamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes,so as to provide link between optimization and simulation, or serve as a bridge for simulationsoftware integration among different domains. An example of optimal design for composite materialstructure is used to demonstrate the newly introduced method.

Optimization Strategy Using Dynamic Metamodel Based on Trust Region and Biased Sampling Method

Combining a trust region method with a biased sampling method,a novel optimization strategy(TRBSKRG)based on a dynamic metamodel is proposed.Initial sampling points are selected by a maximin Latin hypercube design method,and the metamodel is constructed with Kriging functions.The global optimization algorithm is employed to perform the biased sampling by searching the maximum expectation improvement point or the minimum of surrogate prediction point within the trust region.And the trust region is updated according to the current known information.The iteration continues until the potential global solution of the true optimization problem satisfied the convergence conditions.Compared with the trust region method and the biased sampling method,the proposed optimization strategy can obtain the global optimal solution to the test case,in which improvements in computation efficiency are also shown.When applied to an aerodynamic design optimization problem,the aerodynamic performance of tandem UAV is improved while meeting the constraints,which verifies its engineering application.

Metamodel-based Global Optimization Using Fuzzy Clustering for Design Space Reduction

High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.

An ontological metamodeling framework for semantic simulation model engineering

Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelations,and constraints.On the other hand,metamodel is a more classical,but more powerful model in which concepts and relationships are represented in a prescriptive way.This study firstly clarifies the difference between the two approaches,then explains their advantages and limitations,and attempts to explore a general ontological metamodeling framework by integrating each characteristic,in order to implement semantic simulation model engineering.As a proof of concept,this paper takes the combat effectiveness simulation systems as a motivating case,uses the proposed framework to define a set of ontological composable modeling frameworks,and presents an underwater targets search scenario for running simulations and analyzing results.Finally,this paper expects that this framework will be generally used in other fields.

Robust Design Optimization and Improvement by Metamodel

The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.

Multi-Criterion Optimal Design of Automotive Door Based on Metamodeling Technique and Genetic Algorithm

A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.

A MOP Based Constructive Reflective State Metamodel

Construction, management and extension of state space is crucial for many applications, Combining with frequent change of requirement of state space, these applications are rather complicated. Synthesizing techniques of refection architecture, MOP mechanism and metamodel, this paper advances a MOP(Meta Object Protocol) based constructive state metamodel, which defines construct and rules for building models with extensible structure for state space. Based on this metamodel, modeling with extension of state space that adopts decorator pattern and role object pattern is discussed. An implementation of modeling based on this metamodel is presented. With appropriate extension, models based on this metamodel can dynamically adapt state space change. Key words metamodel - reflection - MOP - state space CLC number TP 311.5 Foundation item: Supported by the National Natural Science Foundation of China (60373086)Biography: Liu Jin (1977-), male, Ph. D candidate, research direction: metamodeling and ontology application.

Model Transformation Using a Simplified Metamodel

Model Driven Engineering (MDE) is a model-centric software development approach aims at improving the quality and productivity of software development processes. While some progresses in MDE have been made, there are still many challenges in realizing the full benefits of model driven engineering. These challenges include incompleteness in existing modeling notations, inadequate in tools support, and the lack of effective model transformation mechanism. This paper provides a solution to build a template-based model transformation framework using a simplified metamode called Hierarchical Relational Metamodel (HRM). This framework supports MDE while providing the benefits of readability and rigorousness of meta-model definitions and transformation definitions.

A Metamodel-Driven Business Process Modeling Methodology and Its Integrated Environment for Reusing Business Processes

Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models and best practices, we propose a methodology and an integrated environment for business process modeling driven by the metamodel. Furthermore, we propose a process-template design method to unify the granularity and separate the commonality and variability of business processes so that business process models can be reused across different enterprise software systems. The proposed methodology enables to create reuse-oriented business process templates before the business process modeling. To support the proposed methodology, we developed an integrated environment for creating, reusing and verifying the business process models. As the key techniques, we describe the methodology and its integrated environment, including a metamodel and notations. We applied the methodology and integrated environment to an actual enterprise software development project, and evaluated that the productivity of business process modeling is improved by at least 46%. As the conclusion, this paper contributes to prove the effectiveness of the meta-model driven business process modeling methodology for the reuse of business process models.