浅谈温州市水利工程建设管理

对温州市水利工程建设管理存在的主要问题及原因进行分析,提出加强水利工程建设管理的对策和建议。图1幅。     

Large-scale synthesis of hierarchical alpha-FeOOH flowers by ultrasonic-assisted hydrothermal route

In this paper, we report a facile, an environmental friendly ultrasonic-assisted hydrothermal route for preparation of goethite flower structures using Fe nanopowders at low temperature (85°C). The flower structure consisted of tens of hundreds of nanowires and such structures can further self-assemble with the flake with micro size area. Structural, morphological, and elemental analysis revealed that the products consisted of flower-like structures with high structural uniformity, good crystal quality, and high yields. Influencing factors such as the reaction temperature, pH value, and the deposition time were systematically investigated. A possible formation mechanism was proposed on the basis of the experimental results. Magnetic measurements showed that the as-obtained goethite flowers exhibited weakly ferromagnetic characteristics at room temperature, which were quite different from those of the corresponding bulk materials.     

Innovative methodologies of circuit edit by focused ion beam (FIB) on wafer-level chip-scale-package (WLCSP) devices

As packaging technology advances to wafer level chip scale packaging (WLCSP) to enable reduced chip size and manufacturing cost, circuit edit has become a critical issue for the fully packaged integrated circuits (ICs). These advanced package types cannot be rebuilt on a single chip; therefore, function testing after circuit edit of WLCSP faces challenges. Furthermore, there are routings at the redistribution layer of WLCSP ICs. Circuit edit was applied on both the chip and the package level. In this paper the focused ion beam was applied to mill the organic material of the package structure to expose underlying ICs, instead of chemically destroying the packaging. Metal line cutting and conductive path deposition were also developed by a beam-based technique. These new approaches make the direct edit of electrical circuitry possible not only in ICs but also at package level. Therefore, for the debug process and for failure analysis, the WLCSP ICs have negligible damage and negligible signal integrity loss by retaining the original packaging structure.     

A study of the dynamic compressive behavior of Elk antler

Large mammalian antler is extremely tough and fracture resistant compared to other more brittle forms of skeletal bone. The ability of antler to resist fracture is associated with a decrease in material stiffness and yield strength and increased non-linear response, due in part to antler being fast growing, since they are typically shed and regrown annually. Since male Elk commonly engage in antler sparring as a means of making dominance displays, the ability to withstand large impacts suggest that antler may exhibit strain-rate dependent behavior even greater than skeletal bone. To evaluate this hypothesis, specimens of antler were tested in compression over a range of strain rates. Specimens were loaded either along or transversely to the osteonal growth direction, in wet and dry conditions. Results showed that antler exhibits higher compressive strengths at increased strain rates, and that strain rate and hydration are greater determinants of compressive strength than osteonal orientation. In addition, antler can sustain compressive strains a full order-of-magnitude greater than in mammalian long bone. Failed specimens showed that a hierarchical chain of deformation mechanisms sustains the large bulk strains supported by antler. These mechanisms appear to be less brittle and more fibrous than those seen previously in skeletal bone.     

Determination of volume fraction of bainite in low carbon steels using artificial neural networks

Artificial neural networks have been used to estimate the volume fraction of bainite in low carbon steels containing various alloying elements. The network predicts the volume fraction for a given composition, isothermal transformation temperature and isothermal transformation time. Additionally, the maximum transformation temperature at which bainite formation takes place is also provided as an input to the neural network. The network was trained using the experimental data from three low carbon steels and it was found to perform quite well in predicting the volume fraction of bainite. The impact of the composition of alloying elements on the volume fraction of bainite was also studied and the results were in agreement with the known metallurgical theory.     

Computational uncertainty analysis in multiresolution materials via stochastic constitutive theory

A stochastic constitutive theory is proposed in this work to propagate microstructure uncertainties in computational multiscale continuum models to bulk multiresolution material behavior. Ubiquitous fine resolution uncertainty sources influencing prediction of material properties based on their structures are categorized in detail, and this research transmits these uncertainties to coarser material resolutions by introducing a stochastic constitutive theory deduced from volume element simulations. To implement the stochastic upscaling process, two advanced uncertainty quantification methods are examined: statistical copula functions and random process polynomial chaos expansion. Both methods confront the mathematical difficulty in randomizing constitutive laws by capturing the marked correlation among constitutive parameters seen in complex materials, thus the results proffer a more accurate probabilistic estimation of constitutive material behavior. The contribution of this work is twofold: uncertainty is propagated from heterogeneous material “structure” to material “property” via the stochastic constitutive theory, and rigorous, data-driven mathematics are formalized to represent complicated dependence structures in multivariate statistical distributions. To the authors’ knowledge, this is the first work in multiresolution mechanics that presents an approach to computationally derive correlation functions from numerical experiments, as opposed, for instance, to assuming one a priori. The method put forth in this research, though quite general, is applied to a mathematical example and plastic, high strength steel alloy for demonstration. Results include stochastic constitutive curve confidence intervals for the material stress–strain response and qualitative comparisons of the two stochastic methods detailed herein.     

AES for multiscale localization modeling in granular media

This work presents a multiscale strong discontinuity approach to tackle key challenges in modeling localization behavior in granular media: accommodation of discontinuities in the kinematic fields, and direct linkage to the underlying grain-scale information. Assumed enhanced strain (AES) concepts are borrowed to enhance elements for post-localization analysis, but are reformulated within a recently-proposed hierarchical multiscale computational framework. Unlike classical AES methods, where material properties are usually constants or assumed to evolve with some arbitrary phenomenological laws, this framework provides a bridge to extract evolutions of key material parameters, such as friction and dilatancy, based on grain scale computational or experimental data. More importantly, the phenomenological softening modulus typically used in AES methods is no longer required. Numerical examples of plane strain compression tests are presented to illustrate the applicability of this method and to analyze its numerical performance.     

An approximation method of origin–destination flow traffic from link load counts

Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.     

Modeling stochastic live load for long-span bridge based on microscopic traffic flow simulation

A general framework of modeling the stochastic live load from traffic for a long-span bridge is developed. The cellular automaton (CA) traffic flow simulation technique is adopted to develop the analytical basis of the framework. Based on the traffic flow simulation results, the live load on a long-span bridge from the stochastic traffic is studied with a focus on the static component. Parametric studies of major variables of the framework, such as the length of the connecting roadways, the speed limit, and the vehicle combination, are conducted.     

本钢新1号高炉生产创新实践

对本钢新1号高炉2010年恢复炉况期间的一些生产经验和创新进行了总结。新1号高炉通过加强原燃料管理、合理的装料制度、调整送风制度及强化炉前管理等措施,高炉冶炼强度达到一个较高的水平,2010年12月份达到日产过万的良好成绩。