基于触头电弧侵蚀的交流接触器电寿命分布特征研究

电寿命是交流接触器的重要性能指标之一,受各种因素的影响,其中电弧电流对触头的侵蚀是最主要因素。该文首先提出将电弧电流作为交流接触器触头系统侵蚀的参量,建立了基于起弧相角的触头磨损数学模型;然后基于损伤累积效应建立了交流接触器电寿命分布特征的数学模型,分析了负荷连接方式对其电寿命分布的影响;最后基于Monte Carlo方法建立了交流接触器电寿命模拟试验模型,并对交流接触器在不同工作状态下的电寿命进行了仿真,得到电寿命分布特征,并与可靠性研究领域公认的耗损型产品的寿命服从威布尔分布结论一致。仿真结果表明,文中提出的交流接触器电寿命分布特征的数学模型可以很好地解释实际使用中电寿命呈现出的分散性,对其可靠性的提高具有重要意义。

Monte Carlo numerical simulation and its application in probability analysis of long span bridges

To get the probability of long span bridges under the influence of external random factors, the Monte Carlo method using Latin hypercube sampling is applied. Combined with the condition assessment system on Runyang Suspension Bridge, which is the longest suspension bridge in China, the structural probabilities in normal and damaged situations are calculated with the external random factors considered including environmental temperature, wind load, load of vehicles, etc. The main assessment items contain the maximal vertical displacement of girder, the maximal stress of cables, the maximal horizontal displacement of towers etc. Finally, the probabilities and their cumulative distribution functions are provided. The analysis results can be plotted on line in a clear and vivid way, so the efficiency of assessment is increased and the decision-making of maintenance is more objective and accurate.

Direct Tunneling Effect in Metal-Semiconductor Contacts Simulated with Monte Carlo Method

Considering the tunneling effect and the Schottky effect,the metal semiconductor contact is simulated by using self consistent ensemble Monte Carlo method.Under different biases or at different barrier heights,the investigation into the tunneling current indicates that the tunneling effect is of great importance under reverse biases.The Schottky barrier diode current due to Schottky effect is in agreement with the theoretical one.The barrier lowering is found a profound effect on the current transport at the metal semiconductor interface.

Application of the Monte Carlo Method in Simulative Generation Test Problems Simulative Generation Test Problems

An approach for generating test problems by a computer using the Monte Carlo method based upon user-given characterizations is described.A single point X~* is prespocified by the user to be a solution of the test problems.The approach is flex- ible enough to specify function values,gradients,Hesse,degeneracy degree and ill- conditioned degree at the point X~*.Other numerical features such as indefiniteness, convexity are also under user's control.

FNM method for estimating reliability of existing bridges

Combined with current specifications and stress characteristics of concrete filled steel tubular （CFST） arch bridges, the determination principle of safe-middle-failure threestage mode is given. Accordingly, damage probability and failure probability and the corresponding reliability indices are calculated; a direct relationship between reliability indices and three-stage working status is made. Based on the three-stage working mode, a combined FNM （finite element-neural network- Monte-Carlo simulation） method is put forward to estimate the reliability of existing bridges. According to time-dependent reliability theory, subsequent service time is divided into several stages; minimum samples required by the Monte-Carlo method are generated by random sampling; training samples are calculated by the finite element method, and the training samples are extended by the neural network; failure probability and damage probability are calculated by the Monte-Carlo method. Thus, time dependent reliability indices are obtained, and the working status is judged. A case study is investigated to estimate the reliability of an actual bridge by the FNM method. The bridge is a CFST arch bridge with an 83.6 m span and it has been in operation for 10 years. According to analysis results, in the tenth year, the example bridge is still in safe status. This conclusion is consistent with the facts, which proves the feasibility of the FNM method for estimating the reliability of existing bridges.

Growth Mechanism of Microcrystalline Silicon Films by Scaling Theory and Monte Carlo Simulation

Hydrogenated microcrystalline silicon （~c-Si：H） films with a high deposition rate of 1.2nm/s were prepared by hot-wire chemical vapor deposition （HWCVD）. The growth-front roughening processes of the μc-Si..H films were investi- gated by atomic force microscopy. According to the scaling theory, the growth exponent β≈0.67, the roughness exponent α≈0.80,and the dynamic exponent 1/z = 0.40 are obtained. These scaling exponents cannot be explained well by the known growth models. An attempt at Monte Carlo simulation has been made to describe the growth process of μc-Si： H film using a particle reemission model where the incident flux distribution,the type and concentration of growth radical, and sticking,reemission,shadowing mechanisms all contributed to the growing morphology.

Diffusion Monte Carlo Study of Bond Dissociation Energies for BH2, B（OH）2, BCl2, and BCI

On basis of bond dissociation energies （BDEs） for BH2, B（OH）2, BCl2, and BCl, the diffusion Monte Carlo （DMC） method is applied to explore the BDEs of HB-H, HOB-OH, ClB-Cl, and B-Cl. The effect of the choice of orbitals, as well as the backflow transformation, is studied. The Slater-Jastrow DMC algorithm gives BDEs of 359.1±0.12 kJ/mol for HB-H, 410.5±0.50 kJ/mol for HOB-OH, 357.8±1.46 kJ/mol for ClB-Cl, and 504.5±0.96 kJ/mol for B-Cl using B3PW91 orbitals and similar BDEs when B3LYP orbitals are used. DMC with backflow corrections （BF-DMC） gives a HB-H BDE of 369.9±0.12 kJ/mol which is close to one of the available experimental value （375.8 kJ/mol）. In the case of HOB-OH BDE, the BF-DMC calculation is 446.04-1.84 k J/mol that is closer to the experimental BDE. The BF-DMC BDE for ClB-Cl is 343.2±2.34 kJ/mol and the BF-DMC B-Cl BDE is 523.3±0.33 kJ/mol, which are close to the experimental BDEs, 341.9 and 530.0 kJ/mol, respectively.

Extremum response surface method of reliability analysis on two-link flexible robot manipulator

In order to present a new method for analyzing the reliability of a two-link flexible robot manipulator,Lagrange dynamics differential equations of the two-link flexible robot manipulator were established by using the integrated modal method and the multi-body system dynamics method.By using the Monte Carlo method,the random sample values of the dynamic parameters were obtained and Lagrange dynamics differential equations were solved for each random sample value which revealed their displacement,speed and acceleration.On this basis,dynamic stresses and deformations were obtained.By taking the maximum values of the stresses and the deformations as output responses and the random sample values of dynamic parameters as input quantities,extremum response surface functions were established.A number of random samples were then obtained by using the Monte Carlo method and then the reliability was analyzed by using the extremum response surface method.The results show that the extremum response surface method is an efficient and fast reliability analysis method with high-accuracy for the two-link flexible robot manipulator.

Hybrid LEAP modeling method for long-term energy demand forecasting of regions with limited statistical data

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.

Time-variant reliability analysis of three-dimensional slopes based on Support Vector Machine method

In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

Elbow precision machining technology by abrasive flow based on direct Monte Carlo method

The investigation was carried out on the technical problems of finishing the inner surface of elbow parts and the action mechanism of particles in elbow precision machining by abrasive flow.This work was analyzed and researched by combining theory,numerical and experimental methods.The direct simulation Monte Carlo(DSMC)method and the finite element analysis method were combined to reveal the random collision of particles during the precision machining of abrasive flow.Under different inlet velocity,volume fraction and abrasive particle size,the dynamic pressure and turbulence flow energy of abrasive flow in elbow were analyzed,and the machining mechanism of particles on the wall and the influence of different machining parameters on the precision machining quality of abrasive flow were obtained.The test results show the order of the influence of different parameters on the quality of abrasive flow precision machining and establish the optimal process parameters.The results of the surface morphology before and after the precision machining of the inner surface of the elbow are discussed,and the surface roughness Ra value is reduced from 1.125μm to 0.295μm after the precision machining of the abrasive flow.The application of DSMC method provides special insights for the development of abrasive flow technology.

Reliability analysis on bolt support structure of coal roadway under the influence of mining

Based on the features of the serious deformation of coal roadway,many random variables of the mechanics of the surrounding rocks and the influence of mining, the reliability analysis model of the support structure of coal roadway under the influence of mining is established,and the calculating formulas of reliability of the support structure is obtained with the engineering structure reliability theory. And the reliability is calculated based on the method of Monte Carlo to the coal roadway which is exampled on the influence of mining or not. The relationship between support parameters and reliability, the mining influence coefficients and reliability is established, which provides theory foundations for the design of the coal roadway bolt support.

One-step random-walk process of nanoparticles in cement-based materials

Efficient modelling approaches capable of predicting the behavior and effects of nanoparticles in cement-based materials are required for conducting relevant experiments.From the microstructural characterization of a cement-nanoparticle system,this paper investigates the potential of cell-based weighted random-walk method to establish statistically significant relationships between chemical bonding and diffusion processes of nanoparticles within cement matrix.LaSr_(0.5)C_(0.5)O_(3)(LSCO)nanoparticles were employed to develop a discrete event system that accounts for the behavior of individual cells where nanoparticles and cement components were expected to interact.The stochastic model is based on annihilation(loss)and creation(gain)of a bond in the cell.The model considers both chemical reactions and transport mechanism of nanoparticles from cementitious cells,along with cement hydration process.This approach may be useful for simulating nanoparticle transport in complex 2D cement-based materials systems.

Blind receiver for OFDM systems via sequential Monte Carlo in factor graphs

Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.

Regional Clustering and Synchronization of Provincial Business Fluctuations in China

In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China(31 Chinese provinces are considered except Hong Kong, Macao, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the ‘four-trillion economic stimulus plan'.

Study on slope reliability method Response Surface Method based on Morgenstern-Price Method

The combination of Morgenstern-Price Method （MPM） with Response Surface Method （RSM） improves the eonventional RSM by replacing Finite-Element Method （FEM） with MPM, which is a type of reliability analysis method-RSM based on MPM, and makes the computing process simpler. The stability of the slope was re-evaluated by use of Monte Carlo method （MCM） and the result contrasted with the fore is the conclusion. The reliability analysis of the artificial slope in Donggang Power Station was carried on, and obtained the reliable charaeteristics, which provided basis for the project.

Reliability analysis of supporting pressure in tunnels based on three-dimensional failure mechanism

Based on nonlinear failure criterion,a three-dimensional failure mechanism of the possible collapse of deep tunnel is presented with limit analysis theory.Support pressure is taken into consideration in the virtual work equation performed under the upper bound theorem.It is necessary to point out that the properties of surrounding rock mass plays a vital role in the shape of collapsing rock mass.The first order reliability method and Monte Carlo simulation method are then employed to analyze the stability of presented mechanism.Different rock parameters are considered random variables to value the corresponding reliability index with an increasing applied support pressure.The reliability indexes calculated by two methods are in good agreement.Sensitivity analysis was performed and the influence of coefficient variation of rock parameters was discussed.It is shown that the tensile strength plays a much more important role in reliability index than dimensionless parameter,and that small changes occurring in the coefficient of variation would make great influence of reliability index.Thus,significant attention should be paid to the properties of surrounding rock mass and the applied support pressure to maintain the stability of tunnel can be determined for a given reliability index.

Comparison of GUF and Monte Carlo methods to evaluate task-specific uncertainty in laser tracker measurement

Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.

Natural ventilation performance of single room building with fluctuating wind speed and thermal mass

Natural ventilation is driven by either buoyancy forces or wind pressure forces or their combinations that inherit stochastic variation into ventilation rates. Since the ventilation rate is a nonlinear function of multiple variable factors including wind speed, wind direction, internal heat source and building structural thermal mass, the conventional methods for quantifying ventilation rate simply using dominant wind direction and average wind speed may not accurately describe the characteristic performance of natural ventilation. From a new point of view, the natural ventilation performance of a single room building under fluctuating wind speed condition using the Monte-Carlo simulation approach was investigated by incorporating building facade thermal mass effect. Given a same hourly turbulence intensity distribution, the wind speeds with 1 rain frequency fluctuations were generated using a stochastic model, the modified GARCH model. Comparisons of natural ventilation profiles, effective ventilation rates, and air conditioning electricity use for a three-month period show statistically significant differences （for 80% confidence interval） between the new calculations and the traditional methods based on hourly average wind speed.