次可加测度压的一个注记

利用非紧集上拓扑压的定义,对任意一个不变测度和一族紧度量空间上的次可加势函数,引进了一个新的次可加测度压的定义.在某些假设下,对任意一个遍历测度,证明了新定义的次可加测度压等于用生成集定义的次可加测度压.进一步得到了一个逆变分原理,即次可加测度压等于在某个非紧集上的拓扑压.

某个非紧集上的拓扑压的变分原理(英文)

任给一个遍历测度,我们考虑在该测度下Brin-Katok定理和Birkhoff遍历定理成立的点所组成的集合.我们证明了测度理论压等于这个集合上的拓扑压.

Prediction of compressive strength of cement mortars with fly ash and activated coal gangue

The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive strength of mortars made with ternary blends of cement, activated coal gangue and fly ash. Based on the results of a minimum of seven design points, three special cubic polynomial models are used to establish the strength predicating equations at different ages for mortars. Five experimental checkpoints were also designed to verify the precision of the equations. The most frequent errors of the predicted values are within 3%. A simple and practical way is provided for determining the optimal proportion of two admixtures when they are used in concrete.

Geophysical monitoring technology for CO_2 sequestration

Geophysical techniques play key roles in the measuring, monitoring, and verifying the safety of CO2 sequestration and in identifying the efficiency of CO2-enhanced oil recovery. Although geophysical monitoring techniques for CO2 sequestration have grown out of conventional oil and gas geophysical exploration techniques, it takes a long time to conduct geophysical monitoring, and there are many barriers and challenges. In this paper, with the initial objective of performing CO2 sequestration, we studied the geophysical tasks associated with evaluating geological storage sites and monitoring CO2 sequestration. Based on our review of the scope of geophysical monitoring techniques and our experience in domestic and international carbon capture and sequestration projects, we analyzed the inherent difficulties and our experiences in geophysical monitoring techniques, especially, with respect to 4D seismic acquisition, processing, and interpretation.

Establishment and application of logging saturation interpretation equation in vuggy reservoirs

Vuggy reservoirs are the most common, albeit important heterogeneous carbonate reservoirs in China. However, saturation calculations using logging data are not well developed, whereas Archie method is more common. In this study, electrical conduction in a vuggy reservoir is theoretically analyzed to establish a new saturation equation for vuggy reservoirs. We found that vugs have a greater effect on saturation than resistivity, which causes inflection in the rock-electricity curve. Using single-variable exPeriments, we evaluated the effects of rug size, vug number, and vug distribution on the rock-electricity relation. Based on the general saturation model, a saturation equation for vuggy reservoirs is derived, and the physical significance of the equation parameters is discussed based on the seepage-electricity similarity. The equation parameters depend on the pore structure, and vugs and matrix pore size distribution. Furthermore, a method for calculating the equation parameters is proposed, which uses nuclear magnetic resonance （NMR） data to calculate the capillary pressure curve. Field application of the proposed equation and parameter derivation method shows good match between calculated and experimental results, with an average absolute error of 5.8%.

Relationship between Rice Planthopper Occurrence Area in China and Atmospheric Circulation Indices

[Objective] This study aimed to establish models based on atmospheric cir- culation indices for forecasting the area attacked by rice planthopper every year, and to provide guide for preventing and controlling planthopper damage. [Method] The data related to rice planthopper occurrence and atmospheric circulation were collected and analyzed with the method of stepwise regression to establish the prediction models. [Result] The factors significantly related to the area attacked by rice plan-thopper were selected. Two types of prediction models were established. One was for Sogatella furcifera （Horvath）, based on Atlantic-Europe circulation pattern W in October in that year, Pacific polar vortex area index in October in that year, North America subtropical high index in August in that year, Atlantic-Europe circulation pattern W in June in that year, northern boundary of North America subtropical high in February in that year, Atlantic-Europe polar vortex intensity index in October in that year and Asia polar vortex intensity index in November in the last year; the other type of prediction models were for Nilaparvata lugens （Stal）, based on the Eastern Pacific subtropical high intensity index in July in that year, northern hemi- sphere polar vortex area index in October in the last year, Asia polar vortex strength index in November in the last year, north boundary of North America-At- lantic subtropical high in September in that year, north boundary of North Africa-At- lantic-North America subtropical high in January in that year, sunspot in September of the last year and eastern Pacific subtropical high area index in September in that year. [Conclusion] With the stepwise regression, the forecasting equations of the rice planthopper occurrence established based on the atmospheric circulation indices could be used for actual forecast.

Use of casing and its effect on pressure cells

Although pressure cells have been produced and installed successfully for decades,the accuracy of measured pressure is often inadequate.Due to large differences between the stiffness of pressure cells and the surrounding media,there is a considerable difference between applied pressure and that measured from pressure cells.It is often difficult and expensive to make a pressure cell with stiffness（modulus of elasticity） similar to the surrounding material in which it will be embedded.In order to improve this situation,a casing material with proportional dimensions is recommended as a means to obtain reliable results.In our study,the effect of using casing in the installation of pressure cells is investigated,providing the characteristics of casing.Some practical recommendations are presented to improve the accuracy of the results using casing.

Measurement of Walnut Properties and Analysis on the Compressive Stiffness of the Critical Value of Shell-cracking

Based on the measurement of walnut properties, a computational analysis was conducted on the compressive stiffness of the critical val-ue of shell-cracking. As indicated by the results, to crack the walnuts shell from its longitudinal or edge directions could save lots of efforts. The study provided theoretical basis for the design and improvement of walnut shell-cracking machine as well as theoretical references for the design, mak ng and use of some spec a crack ng-a d ng too s such as nutcracker.

Viscosity profile prediction of a heavy crude oil during lifting in two deep artesian wells

It has been known that the productivity of artesian wells is strongly dependent on the rheological properties of crude oils. This work targets two deep artesian wells(>5000 m) that are producing heavy crude oil. The impacts of well conditions including temperature, pressure and shear rate, on the crude oil rheology were comprehensively investigated and correlated using several empirical rheological models. The experimental data indicate that this heavy oil is very sensitive to temperature as result of microstructure change caused by hydrogen bonding. The rheological behavior of the heavy oil is also significantly impacted by the imposed pressure, i.e., the viscosity flow activation energy(Eμ) gently increases with the increasing pressure. The viscosity–shear rate data are well fitted to the power law model at low temperature. However, due to the transition of fluid feature at high temperature(Newtonian fluid), the measured viscosity was found to slightly deviate from the fitting data. Combining the evaluated correlations, the viscosity profile of the heavy crude oil in these two deep artesian wells as a function of well depth was predicted using the oilfield producing data.

Determining areas in an inclined coal seam floor prone to water-inrush by micro-seismic monitoring

The failure depth of the coal seam floor is one important consideration that must be kept in mind when mining is carried out above a confined aquifer.Determining the floor failure depth is the essential precondition for predicting the water-resisting ability of the floor.We have used a high-precision microseismic monitoring technique to overcome the limited amount of data available from field measurements. The failure depth of a coal seam floor,especially an inclined coal seam floor,may be more accurately estimated by monitoring the continuous,dynamic failure of the floor.The monitoring results indicate the failure depth of the coal seam floor near the workface conveyance roadway（the lower crossheading） is deeper and that the failure range is wider here compared to the coal seam floor near the return airway（the upper crossheading）.The results of micro-seismic monitoring show that the dangerous area for water-inrush from the coal seam floor may be identified.This provides an important field measurement that helps ensure safe and highly efficient mining of the inclined coal seam above the confined aquifer at the Taoyuan Coal Mine.

Inversion of thicknesses of multi-layered structures from eddy current testing measurements

Luquire et al. ' s impedance change model of a rectangular cross section probe coil above a structure with an arbitrary number of parallel layers was used to study the principle of measuring thicknesses of multi-layered structures in terms of eddy current testing voltage measurements. An experimental system for multi-layered thickness measurement was developed and several fitting models to formulate the relationships between detected impedance/voltage measurements and thickness are put forward using least square method. The determination of multi-layered thicknesses was investigated after inversing the voltage outputs of the detecting system. The best fitting and inversion models are presented.

Experimental study on the compressive strength of grouted concrete block masonry based on nondestructive detection methods

Existing nondestructive detection methods were adopted to test the compressive strength of grouted concrete block masonry,i.e.the rebound method,pulling-out method and core drilling method were employed to test the strength of block,mortar and grouted concrete,respectively.The suitability of these methods for the testing of strength of grouted concrete block masonry was discussed,and the comprehensive strength of block masonry was appraised by combining existing nondestructive or micro-destructive detection methods.The nondestructive detection test on 25 grouted concrete block masonry specimens was carried out.Experimental results show that these methods mentioned above are applicable for the strength detection of grouted concrete block masonry.Moreover,the formulas of compressive strength,detection methods and proposals are given as well.

Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill

To reduce the difficulty of obtaining the unconfined compressive strength(UCS) value of fiber-reinforced cemented paste backfill(CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm(salp swarm algorithm, SSA) and extreme learning machine(ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA-ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA-ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.

Rock burst prediction based on genetic algorithms and extreme learning machine

Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.

Prediction of Pressure Gradient and Holdup in Small Eoetvoes Number Liquid-Liquid Segregated Flow

The segregated flow pattern, which occurs in a 26.1 mm diameter, horizontal, stainless steel test section, is investigated. Pressure gradient and in situ phase distribution data were obtained for different combinations of phase superficial velocities ranging from 0.05 m.s^-1 to 0,96 m.s^-1. For the current small Eoetvoes number liquid-liquid system （EOD=4.77）, the dominant effect of interfacial tension and wall-wetting properties of the liquids over the gravity is considered. The approach introduces the closure relationship for the case of turbulent flow m a rough pipe, and attempts to modify the two-fluid model to account for the curved interface. In present flow rates range, wave amplitudes were found small, while interfacial mixing was observed. An adjustable definition for hydraulic diame- ters of two fluids and interfacial friction factor is adopted. The predicted pressure gradient and in situ phase distribution data have been compared with present experimental data and those reported in the literature.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Solubilities of CO2 in some glycol ethers under high pressure by experimental determination and correlation

The binary vapor–liquid equilibrium data of CO_2 in diethylene glycol(monomethyl,monoethyl,monobutyl,dimethyl,diethyl,dibutyl)ether were determined from 288.15 to 318.15 K at pressure up to 6 MPa based on the constant-volume method.It was found by contrast that the ether group in solvents can promote the CO_2 absorption,but the hydroxyl group will inhibit the CO_2 absorption.Furthermore,the solubilities of CO_2 showed an upward trend with the increasing molecular lengths of absorbents.The experimental data were also correlated with a modified Patel–Teja equation of state(PT EOS)combined with the traditional van der Waals one-fluid mixing rules and the results showed a satisfactory agreement between the model and the experimental data.

NOVEL SENSORLESS VECTOR CONTROL SYSTEM OF INDUCTION MACHINE BASED ON FLUX OBSERVER IN FIELD WEAKENING

A speed-sensorless vector control system for induction machines （IMs）is presented, According to the vector control theory of IMs, the rotor flux is estimated based on a flux observer,and the speed is estimated through the method of q-axis rotor flux converging on zero with proportional integral regulator, A 0.75 kW,50 Hz,two-pole induction machine was used in the simulation and experimental verification, The simulation model was constructed in Matlab. A series of tests were performed in the field weakening region, for both no-load and loaded operation. The estimated speed tracks the actual speed well in the based speed region and field weakening region （ 1 per unit value to 4 per unit value）. The small estimation error of residual speed is due to the existence of slip.

Study of Sandy Soil Compaction

In this paper, a study of sandy soil compaction with different granulometry and moisture content has been performed, and soil mechanical property variations in moisture and granulometry have been investigated. Investigations were performed to compare hydrostatic compression test （HCT） responses and evaluate the compression index, Cc, which is an indicator of the soil＇s susceptibility to compaction-induced damage. The experiments have been performed on 24 soil samples typologies. Each sample has been obtained by combining three types of soil granulometry （types A, B and C） with a relative content varying from 0% to 100% in 20% increments. Soil type A had a granulometry ranging between 0.5 mm and 1 mm, type B between 0.25 mm and 0.5 mm, and type C less than 0.25 mm. These samples were representative of a sandy soil, chemically inactive and had various granulometries and initial moisture contents. A cell for HCT has been set up to allow the initial volume measurement of the test pieces and the subsequent changes during HCT with an estimated error less than 0.1 cm3. All samples were pre-compacted and prepared in agreement with the actual standards. The experimental data are reported in diagrams, the data allowed comparison of the mechanical behaviors between the considered unsaturated soils and underlined how soil moisture and granulometry affect soil response during HCT. Furthermore, because of the methodology used, the equipment was very economical.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.