The Propagation of Inertia-Gravity Waves and Their Influence on Mean Zonal Flow, Part One: the Propagation of Inertia-Gravity Waves

Inertia-gravity waves play an increasingly important role in the middle atmosphere dynamics. As a result, more attention has been paid to the study of inertia-gravity waves, especially to the middle atmosphere gravity waves. This paper presents some aspects of inertia-gravity waves with emphasis on the propagation. Two methods are used here, namely, geometric optical method and physical optical method. We can see from the study that inertia-gravity waves are similar to planetary waves in some respects and they are different from planetary waves in others.

The Propagation of Inertia-Gravity Waves and Their Influence on Zonal Mean Flow Part Two:Wave Breaking and Critical Levels

The gravity wave breaking is crucial to the large-scale circulation of middle atmosphere. In this paper, we follow Lindzen (1981) to draw out the parameterization of two-dimensional gravity wave breaking including inertial effect. Also we present some properties of critical levels and inertial critical levels.

THE INFLUENCE OF DEFORMATIONCONDITIONS ON THE FLOW STRESS ANDMICROSTRUCTURE OF ALLOY GH169

In this paper the dependence of flow stress in alloy GH169 on the deformation temperature, strain and strain rate based on the data of hot working simulation test is defined for the first time. Experimental results indicate that for the alloy GH169 the values of flow stress at high temperatures are much higher than those of alloy steel and the work hardening is remarkable. The deformation conditions have significant influence on the flow stress and microstructure of alloy GH169. It is found that under certain conditions fine grain structure can be obtained even though the deformation processes are carried out at high temperatures. This finding is beneficial to the raise of productivity of forming processes. Increasing the cooling rate after deformation is also favourable to the development of fine grain structure. To sum up, the microstructure of alloy GH169 can be controlled effectively through thermomechanical treatment.

INFLUENCE OF THE BASIC FLOW IN THE TROPICS ON THE STATIONARY PLANETARY WAVES AT MIDDLE AND HIGH LATITUDES DURING THE NORTHERN HEMISPHERE WINTER

The relationship between the quasi-stationary planetary waves forced by topography and heat source during the Northern Hemisphere winter is investigated by means of a quasi-geostrophic,34-level,spherical coordinate model with the Rayleigh friction,the Newtonian cooling and the horizontal eddy thermal diffu- sion. The calculated results show that when the basic flow is the westerly in the tropical stratosphere,the amplitude of quasi-stationary planetary wave for zonal wavenumber 2 at middle and high latitudes is larger during the Northern Hemispheric winter;while when the basic flow is the easterly,it is smaller.This is in agreement with the observed results. The calculated results also show that influence of the basic flow in the tropical troposphere on the quasi- stationary planetary waves is larger than that of the basic flow in the tropical stratosphere on the quasi- stationary planetary waves.

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead chokes.It is useful to understand the relationship between flow rate through orifice meters(Qv)and the five fluid-flow influencing input variables:pressure(P),temperature(T),viscosity(μ),square root of differential pressure(ΔP^0.5),and oil specific gravity(SG).Here we evaluate these relationships using a range of machine-learning algorithms applied to orifice meter data from a pipeline flowing from the Cheshmeh Khosh Iranian oil field.Correlation coefficients indicate that(Qv)has weak to moderate positive correlations with T,P,andμ,a strong positive correlation with theΔP^0.5,and a weak negative correlation with oil specific gravity.In order to predict the flow rate with reliable accuracy,five machine-learning algorithms are applied to a dataset of 1037 data records(830 used for algorithm training;207 used for testing)with the full input variable values for the data set provided.The algorithms evaluated are:Adaptive Neuro Fuzzy Inference System(ANFIS),Least Squares Support Vector Machine(LSSVM),Radial Basis Function(RBF),Multilayer Perceptron(MLP),and Gene expression programming(GEP).The prediction performance analysis reveals that all of the applied methods provide predictions at acceptable levels of accuracy.The MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset studied.GEP and RBF also achieve high levels of accuracy.ANFIS and LSSVM perform less well,particularly in the lower flow rate range(i.e.,<40,000 stb/day).Some machine learning algorithms have the potential to overcome the limitations of idealized streamline analysis applying the Bernoulli equation when predicting flow rate across an orifice meter,particularly at low flow rates and in turbulent flow conditions.Further studies on additional datasets are required to confirm this.