Measurement and simulation of the two-phase velocity correlation in sudden-expansion gas-particle flows

In this paper the present authors measured the gas-particle two-phase velocity correlation in sudden expansion gas-particle flows with a phase Doppler particle anemometer （PDPA） and simulated the system behavior by using both a Reynolds-averaged Navier-Stokes （RANS） model and a large-eddy simulation （LES）. The results of the measurements yield the axial and radial time-averaged velocities as well as the fluctuation velocities of gas and three particle-size groups （30μm, 50μm, and 95μm） and the gasparticle velocity correlation for 30μm and 50μm particles. From the measurements, theoretical analysis, and simulation, it is found that the two-phase velocity correlation of sudden-expansion flows, like that of jet flows, is less than the gas and particle Reynolds stresses. What distinguishes the two-phase velocity correlations of sudden-expansion flow from those of jet and channel flows is the absence of a clear relationship between the two-phase velocity correlation and particle size in sudden-expansion flows. The measurements, theoretical analysis, and numerical simulation all lead to the above-stated conclusions. Quantitatively, the results of the LES are better than those of the RANS model.

The Correlation Coefficient of Hesitancy Fuzzy Graphs in Decision Making

The hesitancy fuzzy graphs(HFGs),an extension of fuzzy graphs,are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making(DM).This research implements a correlation coefficient measure(CCM)to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data.The CCM that is proposed between the HFGs has better qualities than the existing ones.It lowers restrictions on the hesitant fuzzy elements’length and may be used to establish whether the HFGs are connected negatively or favorably.Additionally,a CCMbased attribute DM approach is built into a hesitant fuzzy environment.This article suggests the use of weighted correlation coefficient measures(WCCMs)using the CCM concept to quantify the correlation between two HFGs.The decisionmaking problems of hesitancy fuzzy preference relations(HFPRs)are considered.This research proposes a new technique for assessing the relative weights of experts based on the uncertainty of HFPRs and the correlation coefficient degree of each HFPR.This paper determines the ranking order of all alternatives and the best one by using the CCMs between each option and the ideal choice.In the meantime,the appropriate example is given to demonstrate the viability of the new strategies.

Subset-based local vs.finite element-based global digital image correlation:A comparison study

Being the two primary approaches for full-field kinematics measurements, both subset-based local digital image correlation （DIC） and finite element-based global DIC have been extensively studied. Nowadays, most commercial DIC systems employ local DIC algorithm because of its advantages of straight forward principle and higher efficiency. However, several researchers argue that global DIC can provide better displacement results due to the displacement continuity constraint among adjacent elements. As such, thoroughly examining the performance of these two different DIC methods seems to be highly necessary. Here, the random errors associated with local DIC and two global DIC methods are theoretically analyzed at first. Subsequently, based on the same algorithmic details and parameters during analyses of numerical and real experiments, the performance of the different DIC approaches is fairly compared. Theoretical and experimental results reveal that local DIC outperforms its global counterpart in terms of both displacement results and computational efficiency when element （subset） size is no less than 11 pixels.

Employment of predictive search algorithm in digital image correlation

A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.

ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

Efficient Set-Correlation Operator Inside Databases

Large scale of short text records are now prevalent, such as news highlights, scientific paper citations, and posted messages in a discussion forum, and are often stored as set records in hidden-Web databases. Many interesting information retrieval tasks are correspondingly raised on the correlation query over these short text records, such as finding hot topics over news highlights and searching related scientific papers on a certain topic. However, current relational database management systems （RDBMS） do not directly provide support on set correlation query. Thus, in this paper, we address both the effectiveness and the efficiency issues of set correlation query over set records in databases. First, we present a framework of set correlation query inside databases. To the best of our knowledge, only the Pearson＇s correlation can be implemented to construct token correlations by using RDBMS facilities. Thereby, we propose a novel correlation coefficient to extend Pearson＇s correlation, and provide a pure-SQL implementation inside databases. We further propose optimal strategies to set up correlation filtering threshold, which can greatly reduce the query time. Our theoretical analysis proves that with a proper setting of filtering threshold, we can improve the query efficiency with a little effectiveness loss. Finally, we conduct extensive experiments to show the effectiveness and the efficiency of proposed correlation query and optimization strategies.

Correlation-induced coherence and its use in detecting quantum phase transitions

The past two decades have witnessed a surge of interest in exploring correlation and coherence measures to investigate quantum phase transitions(QPTs). Here, motivated by the continued push along this direction, we propose a measure which is built upon the so-called degree of coherence, and advocate using the susceptibility of our measure to detect QPTs. We show that our measure can capture both the notions of coherence and correlations exhibited in bipartite states and therefore represents a hybrid of these two notions. Through examining the XXZ model and the Kitaev honeycomb model, we demonstrate that our measure is favorable for detecting QPTs in comparison to many previous proposals.

STOCHASTIC APPROXIMATION UNDER CORRELATED MEASUREMENT ERRORS

In this paper the Kiefer-Wolfowitz (KW) procedure for searching the extremum of the regression function as well as the Robbins-Monro (RM) procedure for solving the regression equation are modified in order that they can be applied to the case when the measurement errors form an ARMA process. Simple conditions are given to guarantee their convergence to the extremum and the root of regression function respectively by using a new approach combining both the probabilistic method and the ordinary differential equation (ODE) method. The results given here are better than the well-known ones even if the measurement error is the martingale difference sequence.

Experimental and numerical study on structural performance of reinforced concrete box sewer with localized extreme defect

An experimental and numerical investigation into the structural performance of reinforced concrete box sewers with typical corrosion-related extreme defects localized at the ceiling was conducted.Firstly,during the large-scale laboratory test,some key struc-tural responses were captured and evaluated,including the crack width development process(via digital image correlation measurement),ceiling deflection,and material strains of both complete and typical defective boxes.The failure modes and load-carrying mechanism throughout the specimen loading phases were analyzed.Furthermore,the specimen failure process was simulated using a damage-basedfinite element method,and a related parameter sensitivity analysis was performed.The results indicate that the defective ceiling cracked at mid-span under a low load value,but the bending capacity loss can be substituted by two shoulders and carry three tofive times more load before completely collapsing.The simulation matched the lab test qualitatively,and with the suggested set strategy of material parameters,the load-deflection feature curve could provide a practical prediction of the ultimate bearing capacity of the defec-tive sewers,with a 10–15%error on the safe side.

Pseudo-Randomness of Certain Sequences of k Symbols with Length pq

The theory of finite pseudo-random binary sequences was built by C. Mauduit and A. Sarkozy and later extended to sequences of k symbols （or k-ary sequences）. Certain constructions of pseudo-random sequences of k symbols were presented over finite fields in the literature. In this paper, two families of sequences of k symbols are constructed by using the integers modulo pq for distinct odd primes p and q. The upper bounds on the well-distribution measure and the correlation measure of the families sequences are presented in terms of certain character sums over modulo pq residue class rings. And low bounds on the linear complexity profile are also estimated.