A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

Modeling and analysis of actively Q-switched Fe:ZnSe laser pumped by a 2.8μm fiber laser

A theoretical model concerning active Q-switching of an Fe:ZnSe laser pumped by a continuous-wave(CW)2.8μm fiber laser is developed.Calculations are compared with the recently reported experiment results,and good agreement is achieved.Effects of principal parameters,including pump power,output reflectivity,ion concentration and temperature of crystal,on the laser output performance are investigated and analyzed.Numerical results demonstrate that similar to highly efficient CWFe:ZnSe laser,low temperature of the crystal is significant to obtain high peak power Q-switched pulses.The numerical simulation results are useful for optimizing the design of actively Q-switched Fe:ZnSe laser.

A novel approach of testability modeling and analysis for PHM systems based on failure evolution mechanism

Prognostics and health management （PHM） significantly improves system availability and reliability, and reduces the cost of system operations. Design for testability （DFT） developed concurrently with system design is an important way to improve PHM capability. Testability modeling and analysis are the foundation of DFT. This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms. At the component level, the fault progression-related information of each unit under test （UUT） in a system is obtained by means of failure modes, evolution mechanisms, effects and criticality analysis （FMEMECA）, and then the failure-symptom dependency can be generated. At the system level, the dynamic attributes of UUTs are assigned by using the bond graph methodology, and then the symptom-test dependency can be obtained by means of the functional flow method. Based on the failure-symptom and symptom-test dependencies, testability analysis for PHM systems can be realized. A shunt motor is used to verify the application of the approach proposed in this paper. Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well, and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.

Multiscale Characterization of Automotive Surface Coating Formation for Sustainable Manufacturing

Automotive surface coating manufacturing is one of the most sophisticated and expensive steps in automotive assembly. This step involves generating multiple thin layers of polymeric coatings on the vehicle surface through paint spray and curing in a multistage, dynamically changing environment. Traditionally, the quality control is solely post-process inspection based, and process operational adjustment is only experience based, thus the manufacturing may not be （highly） sustainable. In this article, a multiscale system modeling and analysis methodology is introduced for achieving a sustainable application of polymeric materials through paint spray and film curing in automotive surface coating manufacturing. By this methodology, the correlations among paint material, application processes and coating performance can be identified. The model-based analysis allows a comprehensive and deep study of the dynamic behaviors of the material, process, and product in a wide spectrum of length and time. Case studies illustrate the efficacy of the methodology for sustainable manufacturing.

Preface

Micromechanics aims mainly at establishing the quantitative relation between the macroscopic mechanical behavior and the microstructure of heterogeneous materials.

Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.

A PRELIMINARY VALIDATION STUDY OF THE SEASONAL FORECAST OF CCCMA MODEL OVER CHINA

In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,the model was able to produce seasonal anomalies that have properties that are reasonably close to those that are observed.This anomaly is the quantity of interest when forecasting seasonal climatic conditions.The root mean squared difference(RMSD) between the forecast and observed anomaly leads us to be modestly optimistic about the prospects for using dynamical models to forecast the interannual variability of some meteorological elements. The correlation analysis of the forecast and observation also supports the result given by the RMSD analysis and provides a tool for identify the forecast confidence level in various regions,

Trust-Based Personalized Service Recommendation: A Network Perspective

Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.