Major and trace element geochemistry of the mid-Bay of Bengal surface sediments: implications for provenance

The major and trace elements in 110 surface sediment samples collected from the middle of the Bay of Bengal（mid-Bay of Bengal） are analyzed to investigate provenance. Si levels are highest, followed by Al, and the distributions of these two elements are identical. The average CIA＊（chemical index of alteration） value is 72.07,indicating that the degree of weathering of the sediments in the study area is intermediate between those of sediments of the Himalayan and Indian rivers. Factor analyses and discrimination function analyses imply that the two main provenances are the Himalayan and the Indian continent. The inverse model calculation of the Tinormalized element ratios of the Bay of Bengal sediments indicate an estimated average contribution of 83.5%and 16.5% from the Himalayan and peninsular Indian rivers to the study area, respectively. The Himalayan source contributes more sediment to the eastern part of the study area, whereas the western part receives more sediment from the Indian Peninsula than did the eastern part. The primary mechanisms for deposition of sediments in the study area are the transport of Himalayan matter by turbidity currents and river-diluted water and the transport of Indian matter to the study area by a surface circulation in the Bay of Bengal, particularly the East India Coastal Current.

新集煤矿地质构造发育特征及充水因素分析

本文以永春县新集煤矿为例,针对其水患现状,在分析研究区地质、水文地质特征、构造特征的基础上,用分维理论定量分析了矿井断层构造的空间发育特征,分析了矿井充水因素及潜在的水害威胁,并给出了相应的建议,对于矿井安全生产具有一定的理论和现实意义。

Human factors quantification via boundary identification of flight performance margin

A systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled： information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo（MC） simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor（k-NN） classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors.

Inter-cluster voltage balancing control of a delta connected modular multilevel cascaded converter under unbalanced grid voltage

A new inter-cluster DC capacitor voltage balancing scheme for a delta connected modular multilevel cascaded converter (MMCC)-based static synchronous compensator (STATCOM) is presented. A detailed power flow analysis of applying negative sequence current (NSC) and zero-sequence current (ZSC) injection methods in addressing the issue of inter-cluster DC voltage imbalance under unbalance grid voltage is carried out. A control scheme is proposed which integrates both inter-cluster methods using a quantification factor QF. This is used to achieve the sharing of the inter-cluster active power between the NSC and ZSC injection methods. An accurate method of determining the quantification factor is also presented. The proposed method offers better sub-module DC capacitor voltage balancing and prevents converter overcurrent. The influence of unbalanced grid voltage on the delta connected MMCC-based STATCOM rating using this integrated cluster balancing technique is investigated. The control scheme is verified with a 5 kV 1.2MVA MMCC-STATCOM using 3-level bridge sub-modules, and the results show the advantages of the proposed method over other inter-cluster methods.

Inter-cluster Voltage Balancing Control of Modular Multilevel Cascaded Converter Under Unbalanced Grid Voltage

This paper presents a novel inter-cluster direct current(DC)capacitor voltage balancing control scheme for the single-star configured modular multilevel cascaded converter(MMCC)-based static synchronous compensator(STATCOM)under unbalanced grid voltage.The negative-sequence component of grid voltage at the point of common connection(PCC)causes unbalanced active power flow in the phase limbs of converter.This leads to the imbalance of DC voltages of the sub-module capacitors across the MMCC phases,and consequently,the malfunction of converter.The proposed solution is to inject both negative-sequence current(NSC)and zero-sequence voltage(ZSV)into the phase limbs of MMCC.A quantification factor Qf is used to achieve the sharing of inter-cluster active pow-er between the NSC and ZSV injection methods.Accurate determination of the quantification factor has been presented.In addition to maintaining the DC voltages of sub-module capacitor across the MMCC phases balanced,it also prevents the overcurrent and overvoltage of converter by injecting NSC and ZSV with the right proportion.The control scheme is validated on a 3.54 kV 1.2 MVA power system using MMCC-based STATCOM with 3-level bridge cells as sub-modules.The results show that the proposed scheme provides superior effectiveness in eliminating the voltage imbalance of DC capacitor in the phase limb while maintaining low voltage and current ratings.

Quantitative evaluation of model consistency evolution in compositional service-oriented simulation using a connected hyper-digraph

Appropriate maintenance technologies that facilitate model consistency in distributed simulation systems are relevant but generally unavailable.To resolve this problem,we analyze the main factors that cause model inconsistency.The analysis methods used for traditional distributed simulations are mostly empirical and qualitative,and disregard the dynamic characteristics of factor evolution in model operational running.Furthermore,distributed simulation applications(DSAs)are rapidly evolving in terms of large-scale,distributed,service-oriented,compositional,and dynamic features.Such developments present difficulty in the use of traditional analysis methods in DSAs,for the analysis of factorial effects on simulation models.To solve these problems,we construct a dynamic evolution mechanism of model consistency,called the connected model hyper-digraph(CMH).CMH is developed using formal methods that accurately specify the evolutional processes and activities of models(i.e.,self-evolution,interoperability,compositionality,and authenticity).We also develop an algorithm of model consistency evolution(AMCE)based on CMH to quantitatively and dynamically evaluate influencing factors.Experimental results demonstrate that non-combination(33.7%on average)is the most influential factor,non-single-directed understanding(26.6%)is the second most influential,and non-double-directed understanding(5.0%)is the least influential.Unlike previous analysis methods,AMCE provides good feasibility and effectiveness.This research can serve as guidance for designers of consistency maintenance technologies toward achieving a high level of consistency in future DSAs.