Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
For the nondeterministic factors of an aeroengine blisk, including both factors with sufficient and insufficient statistical data,\nbased on the dynamic substructural method of determinate analysis, the extremum response surface method of probabilistic\nanalysis, and the interval method of nonprobabilistic analysis, a methodology called the probabilistic and nonprobabilistic hybrid\nreliability analysis based on dynamic substructural extremum response surface decoupling method (P-NP-HRA-DS-ERSDM) is\nproposed. The model includes randomvariables and interval variables to determine the interval failure probability and the interval\nreliability index. The extremum response surface function and its flow chart of mixed reliability analysis are given. The interval\nanalysis is embedded in the most likely failure point in the iterative process. The probabilistic analysis and nonprobabilistic analysis\nare investigated alternately. Tuned and mistuned blisks are studied in a complicated environment, and the results are compared\nwith the Monte Carlo method (MCM) and the multilevel nested algorithm (MLNA) to verify that the hybrid model can better\nhandle reliability problems concurrently containing random variables and interval variables; meanwhile, it manifests that the\ncomputational efficiency of this method is superior and more reasonable for analysing and designing a mistuned blisk. Therefore,\nthis methodology has very important practical significance....
As a vehicle moves on roads, a complex vibration system of the running vehicle is formed under the collective excitations of\nrandom crosswinds and road surface roughness, together with the artificial handing by the drivers. Several numerical models\nin deterministic way to assess the safety of running road vehicles under crosswinds were proposed. Actually, the natural wind\nis a random process in time domain due to turbulence, and the surface roughness of a road is also a random process but in\nspatial domain. The nature of a running vehicle therefore is an extension of dynamic reliability excited by random processes. This\nstudy tries to explore the dynamic reliability of a road vehicle subjected to turbulent crosswinds. Based on a nonlinear vibration\nsystem, the dynamic responses of a road vehicle are simulated to obtain the dynamic reliability.Monte Carlo Simulation with Latin\nHypercube Sampling is then applied on the possible random variables including the vehicle weight, road friction coefficient, and\ndriver parameter to look at their effects. Finally, a distribution model of the dynamic reliability and a corresponding index for the\nwind-induced vehicle accident considering these random processes and variables is proposed and employed to evaluate the safety\nof the running vehicle....
Large-demand customers, generally high-density dwellings and buildings, have dedicated ground or elevated water tanks to\nconsistently supply drinking water to residents. Online field measurement for Nonsan-2 district meter area demonstrated that\nintermittent replenishment from large-demand customers could disrupt the normal operation of a water distribution system by\ntaking large quantities of water in short times when filling the tanks from distribution mains. Based on the previous results of\nfield measurement for hydraulic and water quality parameters, statistical analysis is performed for measured data in terms of\nautocorrelation, power spectral density, and cross-correlation. The statistical results show that the intermittent filling interval of\n6.7 h and diurnal demand pattern of 23.3 h are detected through autocorrelation analyses, the similarities of the flow-pressure and\nthe turbidity-particle count data are confirmed as a function of frequency through power spectral density analyses, and a strong\ncross-correlation is observed in the flow-pressure and turbidity-particle count analyses. In addition, physicochemical results show\nthat the intermittent refill of storage tank fromlarge-demand customers induces abnormal flowand pressure fluctuations and results\nin transient-induced turbid flow mainly composed of fine particles ranging within 2ââ?¬â??4 ...
Bridges are vulnerable to the fatigue damage accumulation caused by traffic loading over the service period. A continuous\ngrowth in both the vehicle weight and the traffic volume may cause a safety hazard to existing bridges. This study\npresented a computational framework for probabilistic modeling of the fatigue damage accumulation of short to medium\nspan bridges under actual traffic loading. Stochastic truck-load models were simulated based on site-specific weigh-inmotion\nmeasurements. A response surface method was utilized to substitute the time-consuming finite element model\nfor an efficient computation. A case study of a simply supported bridge demonstrated the effectiveness of the computational\nframework. Numerical results show that the simulated fatigue stress spectrum captures the probability density\nfunctions of the heavy traffic loading. The equivalent fatigue stress range increases mostly linearly in the good road\nroughness condition with the growth of the gross vehicle weight. The vehicle type and the road roughness condition\naffect the stress range. The influence of the driving speed on the equivalent stress range is non-monotonic. The bridge\nfatigue reliability has a considerable increase even under a relatively high overload limit. It is anticipated that the proposed\ncomputational framework can be applied for more types of bridges....
The conventional form of statistical simulation proceeds by selecting a few\nmodels and generating hundreds or thousands of data sets from each model.\nThis article investigates a different approach, called BayesSim, that generates\nhundreds or thousands of models from a prior distribution, but only one (or a\nfew) data sets from each model. Suppose that the performance of estimators in\na parametric model is of interest. Smoothing methods can be applied to\nBayesSim output to investigate how estimation error varies as a function of\nthe parameters. In this way inferences about the relative merits of the estimators\ncan be made over essentially the entire parameter space , as opposed to a\nfew parameter configurations as in the conventional approach. Two examples\nillustrate the methodology: One involving the skew-normal distribution and\nthe other nonparametric goodness-of-fit tests....
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