Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
Transmission belt is one of the most likely to fail parts of combine harvester,\nwhich affecting the machine reliability seriously. Dynamic strength occurs along with\nvibration during the operation and must be taken into account when calculating\nreliability, especially in harsh working environment like harvesting. However, the\nexisting calculation method of reliability on combine harvester transmission belt\ndidn�t take the dynamic strength into account. In this research, a reliability calculation\nmethod was proposed based on the dynamic analysis of transmission belt. The\nnonlinear dynamic equation was built using string and beam model. Through the\nequation, relationship between belt speed and dynamic stress was deduced.\nConsidering dynamic stress and regarding uncertain parameters as random uncertain\nparameters, reliability calculation model was built. Finally, an example was presented\nand the above mentioned dynamic reliability calculation method was simulated to\nverify the theoretical analysis in this paper and tested by the Monte-Carlo method....
To more accurately calculate the dynamic reliability for the center gear of power\nassisting cycle with various failure modes, the multi-extremum response surface method is\nadopted. The first, the gear�s torque, material density, elastic modulus and poisson's ratio are\nsampled in small batches as the random input samples, the dynamic extremum of deformation,\nstress and strain are obtained as output response by finite element model. Then, the multiextremum\nresponse surface equation is established. Finally, multitudinous sample points are\nobtained by using Monte Carlo method and linkage sampling to multi-extremum response\nsurface equation, which can used to calculate the reliability of the deformation, stress and strain\nof the gear in the comprehensive failure mode. The results show that the comprehensive\nreliability degree of gear is 0.9949 when the allowable deformation, stress and strain are 0.47\nmm, 540 Mpa and 0.003 , respectively....
The temperature distribution in real-world industrial environments is often in a three-dimensional space, and developing a reliable\nmethod to predict such volumetric information is beneficial for the combustion diagnosis, the understandings of the complicated\nphysical and chemical mechanisms behind the combustion process, the increase of the system efficiency, and the reduction of\nthe pollutant emission. In accordance with the machine learning theory, in this paper, a new methodology is proposed to predict\nthree-dimensional temperature distribution from the limited number of the scattered measurement data. The proposed prediction\nmethod includes two key phases. In the first phase, traditional technologies are employed tomeasure the scattered temperature data\nin a large-scale three-dimensional area. In the second phase, the Gaussian process regression method, with obvious superiorities,\nincluding satisfactory generalization ability, high robustness, and low computational complexity, is developed to predict threedimensional\ntemperature distributions. Numerical simulations and experimental results from a real-world three-dimensional\ncombustion process indicate that the proposed prediction method is effective and robust, holds a good adaptability to cope with\ncomplicated, nonlinear, and high-dimensional problems, and can accurately predict three-dimensional temperature distributions\nunder a relatively lowsampling ratio.As a result, a practicable and effective method is introduced for three-dimensional temperature\ndistribution....
The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the\npast decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is\napplied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the\nnetwork-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel\ntime rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density).\nThe weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted\naverage travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with\ntime-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time\nrate increase monotonically with network density.The empirical findings of the relation between network travel time reliability and\nnetwork traffic characteristics can be possibly applied to assess trafficmanagement and controlmeasures to improve network travel\ntime reliability....
The numerical simulation of multiphysics problems has grown steadily in recent years.\nThis development is due to both the permanent increase of IT resources and the considerable progress\nmade in modeling, mathematical and numerical analysis of many problems in fluid and solid\nmechanics. The phenomena related to fluid/structure mechanical coupling occurs in many industrial\nsituations, and the influence it may have on the dynamic behavior of mechanical systems is often\nsignificant. In this paper, a numerical vibratory study is conducted on a three-dimensional aircraftââ?¬â?¢s\nwing subjected to aerodynamic loads. Finite volume method (FVM) is used for the discretization of\nthe fluid problem, and finite element method (FEM) is used for the structureââ?¬â?¢s approximation. In this\ncontext, a deterministic model has been proposed in our study, then stochastic analysis has been\ndeveloped to deal with the statistical nature of fluidââ?¬â??structure interaction parameters. Moreover,\nprobabilistic-based reliability analysis intends to find safe and cost-effective projects....
Loading....