Pressure and Temperature Feasibility of NCEP/NCAR Reanalysis Data at Mt.Everest

Mt.Everest (27°54' N,86°54' E),the highest peak,is often referred to as the earth's 'third' pole,at an elevation of 8844.43 m. Due to the difficult logistics in the extreme high elevation regions over the Himalayas,observational meteorological data are very few on Mt. Everest. In 2005,an automatic weather station was operated at the East Rongbuk glacier Col of Mt. Everest over the Himalayas. The observational data have been compared with the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),and the reliability of NCEP/NCAR reanalysis data has been investigated in the Himalayan region,after the reanalyzed data were interpolated in the horizontal to the location of Mt. Everest and in the vertical to the height of the observed sites. The reanalysis data can capture much of the synoptic-scale variability in temperature and pressure,although the reanalysis values are systematically lower than the observation. Furthermore,most of the variability magnitude is,to some degree,underestimated. In addition,the variation extracted from the NCEP/NCAR reanalyzed pressure and temperature prominently appears one-day lead to that from the observational data,which is more important from the standpoint of improving the safety of climbers who attempt to climb Mt. Everest peak.

Estimating Average Reservoir Pressure： A Neural Network Approach with Limited Data

Insight into average oil pressure in gas reservoirs and changes in production （time）, play a critical role in reservoir and production performance, economic evaluation and reservoir management. In all practicality, average reservoir pressure can be conducted only when producing wells are shut in. This is regarded as a pressure build-up test. During the test, the wellbore pressure is recorded as a function of time. Currently, the only available method with which to obtain average reservoir pressure is to conduct an extended build-up test. It must then be evaluated using Homer or MDH （Miller, Dyes and Huchinson） valuation procedures. During production, average reservoir pressure declines due to fluid withdrawal from the wells and therefore, the average reservoirpressure is updated, periodically. A significant economic loss occurs during the entire pressure build-up test when producing wells are shut in. In this study, a neural network model has been established to map a nonlinear time-varying relationship which controls reservoir production history in order to predict and interpolate average reservoir pressure without closing the producing wells. This technique is suitable for constant and variable flow rates.

MECHANICS ANALYSIS ON PRECISE FORMING PROCESS OF EXTERNAL SPLINE COLD ROLLING

According to the suitable assumption, the deformation process of external spline cold rolling is analyzed. By the graphing method, the slip-line field of plastically deforming area in process of external spline cold rolling is set up. Different friction-conditions are used in different contact areas in order to realistically reflect the actual situation. The unit average pressure on contact surface of the rolling process is solved according to the stress filed theory of slip-line. And the formulae of the rolling-force and rolling-moment are established. The theoretical result is well consistent with the finite element analysis. A theoretical basis is provided for the precise forming process of spline cold rolling and the production of external splined shafts.

On the ratio of expectation crossings of random-excited dielectric elastomer balloon

The ratio of expectation crossings of dielectric elastomer balloon excited by random pressure is analytically evaluated in this letter. The Mooney-Rivlin model is adopted to describe the constitutive relation while the random pressure is described by Gaussian white noise. Through a specific transformation, the stochastic differential equations for the total energy and phase are derived. With the application of the stochastic averaging, the system total energy is then approximated by a one-dimensional diffusion pro-cess. Solving the associated Fokker-Planck-Kolmogorov （FPK） equation yields the stationary probability density of the system total energy. The ratio of expectation crossings is then derived based on the joint stationary probability density of stretch ratio and its ratio of change. The efficacy and accuracy of the proposed procedure are verified by comparing with the results from Monte Carlo simulation （MCS）.