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.

基于主动学习方法的汽轮机排缸参数分析

It is well known that the efficiency of a steam turbine is affected by the pressure recovery performance of its low-pressure exhaust hood,and therefore,parametric analysis of the exhaust hood is of great importance in the steam turbine design process.In recent years,computationally inexpensive metamodels have been widely used in the parametric analysis of exhaust hood.However,the prediction accuracy of metamodels is highly dependent on the number and distribution of sample points in the design space.The purpose of active learning is selecting informative samples sequentially to obtain an accurate metamodel within a reasonable computational budget.However,the impact of active learning on the accuracy of metamodels such as exhaust hood parameter analysis has not been fully explored.Therefore,this paper investigates and compares four representative active learning methods on the parametric modeling of turbine exhaust hoods,and the comparison results highlight the advantages of active learning and the analysis of the exhaust hood based on the metamodel with the highest accuracy.

基于SPEM2XPDL模型转换的软件过程执行

SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件过程执行的方法.通过将SPEM模型转换为XPDL(XML process definition language)模型,利用XPDL引擎支持SPEM模型的执行.制定了SPEM和XPDL之间的映射规则,设计了转换算法并开发了转换引擎.该方法被应用在SoftPM项目中,成功地基于XPDL引擎Shark实现了对软件过程模型的执行支持.

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.

Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

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.

Kriging-based reliability analysis of the long-term stability of a deep driftconstructed in the Callovo-Oxfordian claystone

Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g.galleries/alveoli)within the disposal is assured by the liner,which includes two layers:concrete arch segment and compressible material.The latter exhibits a significant deformation capacity(about 50%)under low stress(<3 MPa).Although the response of these underground structures can be governed by complex thermo-hydro-mechanical coupling,the creep behavior of COx claystone has been considered as the main factor controlling the increase of stress state in the concrete liner and hence the long-term stability of drifts.Therefore,by focusing only on the purely mechanical behavior,this study aims at investigating the uncertainty effect of the COx claystone time-dependent properties on the stability of an alveolus of Cigéo during the exploitation period.To describe the creep behavior of COx claystone,we use Lemaitre’s viscoplastic model with three parameters whose uncertainties are identified from laboratory creep tests.For the reliability analysis,an extension of a well-known Kriging metamodeling technique is proposed to assess the exceedance probability of acceptable stress in the concrete liner of the alveolus.The open-source code Code_Aster is chosen for the direct numerical evaluations of the performance function.The Kriging-based reliability analysis elucidates the effect of the uncertainty of COx claystone on the long-term stability of the concrete liner.Moreover,the role of the compressible material layer between the concrete liner and the host rock is also highlighted.

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.

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.

从UML到元建模研究理解

随着UML的流行,掀起了人们对于元建模研究的热潮,UML是一种标准的图形化建模语言,但是人为的为不同的软件开发不同的建模语言费时费力,而元建模的诞生解决了这个问题。本文以UML功用和特点,元建模各个模块之间的关系做了浅显的分析和理解。

End to End Development Engineering

Raising software abstraction and re-use levels are key success factors for producing quality software products. Model-driven architecture (MDA) is an OMG initiative following this trend by mapping a conceptual model of application specified in platform independent model (PIM), to one or more platform specific models (PSM) automatically. Because there is little previous work tackling the development problem from specification through to implementation, this paper proposes End to End Development engineering (E2EDE) method using MDA methodology. E2EDE is intended to fill the mapping gap between PIM and PSM in MDA. The notion of variability is utilized from software product line and used to model design decisions in PSM. PIM is equipped with Nonfunctional requirements which borrowed from Design pattern to inform design decisions;thereby guiding the mapping process. In addition we have developed a strategic PSM for messaging systems can be configured to produce different applications such as the helpdesk system which is used as a case study.

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.

Soft Tissue Deformation Modeling in the Procedure of Needle Insertion:A Kriging‑Based Method

The simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite element method(FEM)are widely used to compute the deformations of soft tissue.However,it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties,geometries and interaction mechanisms.In this paper,a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback.Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth.The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth.The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths.The feasibility of the method is verified by investigating the adaptation to step variations.Compared with the ground truth of the finite element(FE)results,the maximum residual is less than 0.92 mm in the Y direction and 0.31 mm in the X direction.The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.

The Principal Component Transform of Parametrized Functions

Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a problem of their simplification has become of great importance. In particular, rather challengeable methods of estimation of parameters in these models may require such simplifications. The paper offers a practical way of constructing approximations of nonlinearly parametrized functions by linearly parametrized ones. As the idea of such approximations goes back to Principal Component Analysis, we call the corresponding transformation Principal Component Transform. We show that this transform possesses the best individual fit property, in the sense that the corresponding approximations preserve most information (in some sense) about the original function. It is also demonstrated how one can estimate the error between the given function and its approximations. In addition, we apply the theory of tensor products of compact operators in Hilbert spaces to justify our method for the case of the products of parametrized functions. Finally, we provide several examples, which are of relevance for systems biology.

Dimensional Amplitude Response Analysis of Vibrations Produced by Defects in Rolling Contact Bearings

Usage of rolling contact bearings in variety of rotor-dynamic applications has put forth a need to develop a detailed and easy to implement techniques for the assessment of damage related features in these bearings so that before mechanical failure,maintenance actions can be planned well in advance.In accordance to this,a method based on dimensional amplitude response analysis and scaling laws is presented in this paper for the diagnosis of defects in different components of rolling contact bearings in a dimensionally scaled rotor-bearing system.Rotor,bearing,operating and defect parameters involved are detailed for dimensional analysis using frequency domain vibration data.A defect parameter for modeling all the three dimensions of the defect as well as the different shapes like square,circular,rectangular is put forth which takes into account the volume as well as the surface area of the defect.Experimental data set is generated for the‘model’bearing(designated as SKF30205J2/Q)using Box-Behnken design of response surface methodology for solution of the theoretical model by factorial regression approach.Obtained metamodel is then used for the prediction of the objective variable,i.e.,Vibration acceleration amplitude at the defect frequency component for other types of‘test’bearings(designated as SKF 30305C and SKF 22220 EK)using the developed scaling laws.Confirmation experiments showed that the computable relationship amongst objective variable and the dimensionless parameters can be forecast and correlated.

Research on Design Optimization Strategy in Virtual Product Development

Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.

Reverse Engineering Activity Diagrams from Object Oriented Code： An MDA-Based Approach

The success of system modernization depends on the existence of technical frameworks for information integration and tool interoperation like the Model Driven Architecture （MDA）. Reverse engineering techniques play a crucial role in system modernization. This paper describes how to reverse engineering activity diagrams from object oriented code in the MDA context focusing on transformations at model and metamodel levels. A framework to reverse engineering MDA models from object oriented code that distinguishes three different abstraction levels linked to models, metamodels and formal specifications, is described. At model level, transformations are based on static and dynamic analysis. At metamodel level, transformations are specified as 0CL （Object Constraint Language） contracts between M0F （Meta Object Facility） metamodels which control the consistency of these transformations. The level of formal specification includes algebraic specifications of MOF metamodels and metamodel-based transformations. This paper analyzes a recovery process of activity diagrams from Java code by applying static and dynamic analysis and shows a formalization of this process in terms of MOF metamodels. The authors validate their approach by using Eclipse Modeling Framework, Ecore metamodels and ATL （Atlas Transformation Language）.

Service-Oriented Semantic Interoperability Technologies and Standards

Service-Oriented Software Engineering （SOSE） presents new challenges; in particular, how to promote interoperability and cooperation among loosely-coupled service resources. This is critical for service resource sharing and for implementing on-demand services. This paper discusses key technologies of service virtualization, including encapsulation of service interoperability （for available resources）; ontology-based Role, Goal, Process, and Service （RGPS） metamodeling （for interoperable aggregation and organization of virtualization services）; registration and repository management of Metamodel Framework for Interoperability （MFI） （for virtualization service management）; and virtualization service ontology and its represented association with RGP＆ Latest progress of the MFI and ISQ standards is also discussed.