Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Conversion between solid and beam element solutions of finite element method based on meta-modeling theory:development and application to a ramp tunnel structure

In this study, a new method for conversion of solid finite element solution to beam finite element solution is developed based on the meta-modeling theory which constructs a model consistent with continuum mechanics. The proposed method is rigorous and efficient compared to a typical conversion method which merely computes surface integration of solid element nodal stresses to obtain cross-sectional forces. The meta-modeling theory ensures the rigorousness of proposed method by defining a proper distance between beam element and solid element solutions in a function space of continuum mechanics. Results of numerical verification test that is conducted with a simple cantilever beam are used to find the proper distance function for this conversion. Time history analysis of the main tunnel structure of a real ramp tunnel is considered as a numerical example for the proposed conversion method. It is shown that cross-sectional forces are readily computed for solid element solution of the main tunnel structure when it is converted to a beam element solution using the proposed method. Further, envelopes of resultant forces which are of primary importance for the purpose of design, are developed for a given ground motion at the end.

Dynamic Meta-Modeling Method to Assess Stochastic Flutter Behavior in Turbomachinery

With increasing design demands of turbomachinery,stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety.Stochastic flutter assessment is an effective measure to quantify the failure risk and improve aeroelastic stability.However,for complex turbomachinery with multiple dynamic influencing factors(i.e.,aeroengine compressor with time-variant loads),the stochastic flutter assessment is hard to be achieved effectively,since large deviations and inefficient computing will be incurred no matter considering influencing factors at a certain instant or the whole time domain.To improve the assessing efficiency and accuracy of stochastic flutter behavior,a dynamic meta-modeling approach(termed BA-DWTR)is presented with the integration of bat algorithm(BA)and dynamic wavelet tube regression(DWTR).The stochastic flutter assessment of a typical compressor blade is considered as one case to evaluate the proposed approach with respect to condition variabilities and load fluctuations.The evaluation results reveal that the compressor blade has 0.95% probability to induce flutter failure when operating 100% rotative rate at t=170 s.The total temperature at rotor inlet and dynamic operating loads(vibrating frequency and rotative rate)are the primary sensitive parameters on flutter failure probability.Bymethod comparisons,the presented approach is validated to possess high-accuracy and highefficiency in assessing the stochastic flutter behavior for turbomachinery.

Robust optimization of aircraft weapon delivery trajectory using probability collectives and meta-modeling

Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.

动态联盟中企业建模的Meta-Model

虚拟组织的两个主要组成 :1 )虚拟的办公环境 ,如个体之间不受地理位置限制的电子通讯交流方式和个体之间基于信息技术支持的文件存储、检索或数据处理等系统 .2 )功能虚拟化 ,发展自己的核心业务能力 ,弱项实行外包 ( outsourcing) .因此 ,信息技术和信息系统的有效支持是虚拟企业的重要特征之一 .如何在构建虚拟企业动态联盟的企业建模时 ,考虑整体的信息系统构架值得研究 .文章利用战略依赖 /原理模型等面向目标 ( goal-oriented,GO)的分析方法—OO( object-oriented)方法的延伸 ,研究了虚拟企业动态联盟中企业建模的 Meta-Model。

Performance Comparison of Two Meta-Model for the Application to Finite Element Model Updating of Structures

To investigate the application of meta-model for finite element( FE) model updating of structures,the performance of two popular meta-model,i. e.,Kriging model and response surface model( RSM),were compared in detail. Firstly,above two kinds of meta-model were introduced briefly. Secondly,some key issues of the application of meta-model to FE model updating of structures were proposed and discussed,and then some advices were presented in order to select a reasonable meta-model for the purpose of updating the FE model of structures. Finally,the procedure of FE model updating based on meta-model was implemented by updating the FE model of a truss bridge model with the measured modal parameters. The results showed that the Kriging model was more proper for FE model updating of complex structures.

Application of experimental design techniques to structural simulation meta-model building using neural network

Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural network models.In this paper,some existing main sampling techniques are evaluated,including techniques based on experimental design theory, random selection,and rotating sampling.First,advantages and disadvantages of each technique are reviewed.Then,seven techniques are used to generate samples for training radial neural networks models for two benchmarks:an antenna model and an aircraft model.Results show that the uniform design,in which the number of samples and mean square error network models are considered,is the best sampling technique for neural network based meta-model building.

Creation of a Meta-Model for the Generation of a Webmapping Application

Our research focuses on creating a meta-model for generating a web mapping application. It was difficult for non-geomatics developers to implement a webmapping application. Indeed, this type of application uses geospatial data that require geomatics skills. For this reason, in order to help non-geomatics developers to set up a webmapping application, we have designed a meta-model that automatically generates a webmapping application using model-driven engineering. The created meta-model is used by non-geomatics developers to explicitly write the concrete syntax specific to the webmapping application using the xtext tool. This concrete syntax is automatically converted into source code using the xtend tool without the intervention of the non-geomatics developers.

An Integrated Approach for Decision Support through Multi-objective Optimization with Application to an Ill-posed Design Problem

In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective optimization has been proposed as an efficient method for human-centered manufacturing. However, previous vast researches on optimization have been mainly focused on optimization theory and optimization techniques and paid little interests on the process of problem formulation itself. In this paper, therefore, the authors present a total framework for supporting multi-objective decision making. Then, the authors try to solve the formulated multi-objective optimization problem that involves both qualitative and quantitative performance measures as a general consequence from the above procedure. Taking especially quality as a qualitative measure, the authors gave a new idea to evaluate the quality quantitatively. Additionally, to facilitate the portability of the proposed method in multidisciplinary decision-making environments, the authors implement the proposal algorithm in an Excel spreadsheet and validate the effectiveness of the approach through a case study.

Variable-stiffness composite cylinder design under combined loadings by using the improved Kriging model

The large design freedom of variable-stiffness (VS) composite material presupposes its potential for wide engineering application. Previous research indicates that the design of VS cylindrical structures helps to increase the buckling load as compared to quasi-isotropic (QI) cylindrical structures. This paper focuses on the anti-buckling performance of VS cylindrical structures under combined loads and the efficient optimization design method. Two kinds of conditions, bending moment and internal pressure, and bending moment and torque are considered. Influences of the geometrical defects, ovality, on the cylinder's performances are also investigated. To increase the computational efficiency, an adaptive Kriging meta-model is proposed to approximate the structural response of the cylinders. In this improved Kriging model, a mixed updating rule is used in constructing the meta-model. A genetic algorithm (GA) is implemented in the optimization design. The optimal results show that the buckling load of VS cylinders in all cases is greatly increased as compared with a QI cylinder.

An approach to measuring business-IT alignment maturity via DoDAF2.0

Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities of enterprise architecture(EA)in interrelating different business/IT viewpoints and elements,the development of EA is superior to support BITA measurement.Extant BITA measurement literature is sparse when it concerns EA.The literature tends to explain how EA viewpoints or models correlate with BITA,without discussing where to collect and integrate EA data.To address this gap,this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework:DoD Architectural Framework 2.0(DoDAF2.0).The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0.An illustrative ArchiSurance case is conducted to explain the measurement process.Systematically,this paper explores the process of BITA measurement from the viewpoint of EA,which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.

Ontology mapping approach based on set & relation theory and OCL

An ontology mapping approach based on set & relation theory and OCL is introduced,then an ontology mapping meta-model is established which is composed of ontology related elements,mapping related elements and definition rule related elements.This ontology mapping meta-model can be regarded as a unified mechanism to realize different kinds of ontology mappings.The powerful computation capability of set and relation theory and the flexible expressive capability of OCL can be used in the computation of ontology mapping meta-model to realize the unified mapping among different ontology models.Based on the mapping meta-model,a general mapping management framework is developed to provide a common mapping storage mechanism,some mapping APIs and mapping rule APIs.

应用SPOM方法辅助获取与分析面向服务需求(英文)

In order to design and provide good services,it is necessary for students to describe and understand the customer requirements for services rapidly,to design and plan the services and their behavior,and to schedule service processes effectively.This paper proposes a new service-oriented requirement elicitation and analysis method based on answer set semantic,sketches requirement Meta-model and correlative proofs and algorithms,which can help students represent requirement more declarative and precise.Based on the Meta-model,Subject-Predicate-Object requirement method and specification is presented,by which students can constitute a smooth,small-step,incremental and iterative development process cut in by forms.By applying this method to teach and help students develop service-oriented application,it is clear that it can help students eliminate the miscommunication between developers and users,assure the correctness of the artifacts and make the users active in development cycle.

An Efficient Multi-Scale Modelling Approach for ssDNA Motion in Fluid Flow

The paper presents a multi-scale modelling approach for simulating macromolecules in fluid flows. Macromolecule transport at low number densities is frequently encountered in biomedical devices, such as separators, detection and analysis systems. Accurate modelling of this process is challenging due to the wide range of physical scales involved. The continuum approach is not valid for low solute concentrations, but the large timescales of the fluid flow make purely molecular simulations prohibitively expensive. A promising multi-scale modelling strategy is provided by the meta-modelling approach considered in this paper. Meta-models are based on the coupled solution of fluid flow equations and equations of motion for a simplified mechanical model of macromolecules. The approach enables simulation of individual macromolecules at macroscopic time scales. Meta-models often rely on particle-corrector algorithms, which impose length constraints on the mechanical model. Lack of robustness of the particle-corrector algorithm employed can lead to slow convergence and numerical instability. A new FAst Linear COrrector （FALCO） algorithm is introduced in this paper, which significantly improves computational efficiency in comparison with the widely used SHAKE algorithm. Validation of the new particle corrector against a simple analytic solution is performed and improved convergence is demonstrated for ssDNA motion in a lid-driven micro-cavity.

Component versioning for protocol configuration management

Classical software configuration management which deals with source code versioning becomes insufficient when most components are distributed in binary form. As an important aspect of software configuration, protocol configuration also encounters those problems. This paper focuses on solving protocol component versioning issues for protocol configuration man- agement on embedded system, incorporating the following versioning issues: version identification, version description and protocol component archiving and retrieving based on the version library.

Modeling the dynamic alignment of business and information systems via the lens of human-centered architecture evolution

The complexity of business and information systems(IS)alignment is a growing concern for researchers and practitioners alike.The extant research on alignment architecture fails to consider the human viewpoint,which makes it difficult to embrace emergent complexity.This paper contributes to the extant literature in the following ways.First,we combine an enterprise architecture(EA)framework with a human viewpoint to address alignment issues in the architecture design phase;second,we describe a dynamic alignment model by developing a humancentered meta-model that explains first-and second-order changes and their effects on alignment evolution.This paper provides better support for the theoretical research and the practical application of dynamic alignment.

Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised.The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design.Multiple analysis methods are known and available to gain insight into existing models.In this contribution,selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process.The selection of introduced methods comprises techniques of machine learning and data mining,in which the utilization is aiming at a decreased numerical effort.The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown.An incremental knowledge gain is provided by a step-bystep application of the numerical methods,whereas resulting consequences for further applications are highlighted.Furthermore,a visualisation method aiming at an easy design guideline is proposed.These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design.

Model Interpretation Development: Analysis Design of Automatic Control System

Currently the development of automatic control system is mainly based on manual design. This has made the develop-ment process complicated and has made it difficult to guarantee system requirement. This paper presents a Model in-terpretation development architecture built on meta-models and model interpretation. In this modeling and developing process, different meta-models or domain models may be constructed in terms of various system requirements. Inter-preters are used to transform the meta-model into relevant domain model and generate some other formats from do-main models, typically with different semantic domains. An interpretation extension interface is introduced, which can be accelerated to develop the model interpreter. This development architecture can improve system reusability and en-hance development efficiency. Finally, an example is introduced to explain the advantage of method.

An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems

Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive en- gineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization （E-HAM） is first proposed in this work. Important region update method （IRU） and different sampling size strategies are proposed in the opti- mization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be up- dated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson＇s ratio （NPR） crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive prob- lems and can be widely used in engineering industry.