Measurement and prediction of pressure drop in two-phase flow

Experimental data for air–water two-phase co-current flow in two different pipe diameters were used to test the prediction of pressure drop by a number of existing theories and correlations. Several models are shown to be useful for prediction, particularly with the stratified regimes which have proved difficult to handle in the past. The model suggested by Olujic proved to be of particular value.     

A NEW ALGORITHM FOR ADAPTIVE LATTICE FILTERAND ITS APPLICATION IN THE SPEECH LINEARPREDICTIVE SYNTHESIS

In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.     

动态模糊神经网络在大坝变形预报中的应用

针对静态模糊神经网络的局限性,提出了在线动态建模的模糊神经网络方法.当新增样本进入训练集之后,根据新样本对模型的贡献大小,在已有模型的基础上进行动态修正,这样可以减少建模的计算时间.新方法实现了增加样本而矩阵阶数不增加,避免了矩阵求逆运算,理论上可以提高计算效率.实例表明动态模糊神经网络方法是可行的,可实现持久预报,具有较强的适应能力和较高的预报精度,可应用于在线实时变形预报及相关领域.     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.     

多属性神经网络地震反演在NB油田水平井钻探中的应用

NB油田位于渤海海域,为新近系河流相稠油油田,储层横向分布不稳定,砂体厚度薄、连通性较差,油水关系复杂,开发设计部署了大量水平井。选择神经网络反演方法,建立地震属性与储层参数的非线性关系,进而根据其关系反演得到储层参数数据体,利用该数据体沿层切片提取储层预测信息,研究储层发育规律,指导该油田水平井的部署和钻探,取得了较好的效果,降低了钻探风险。用神经网络地震反演方法进行储层预测研究,指导水平井钻探,在渤海海域尚属首次。     

疲劳裂纹的灰色残差预测

借助灰色理论，建立了预测疲劳裂纹扩展的灰色残差预测模型，并应用此模型预测了某不锈钢构件腐蚀疲劳裂纹的扩展，得到了较高精度的预测结果，为疲劳裂纹预测提供了一种简易而可靠的新途径。     

低频天波传播预测与低电离层模式参数的测定

在理想化的均匀球面通过进一步地与具有突变边界的均匀各向同性同心球面电离层条件下的LF天波全波解的基础上，考虑由海陆之差引起的地面导电率的变化、大气和低电离层的不均匀性以及由地磁场引起的各向异性特性等因素的影响，推导出了较严格预测实际地与低电离层条件下场强与传播相延精确预测的完善公式。 给出了一些低电离层模式参数的实测结果。这些模式参数为参考高度h0、参考浓度N0与梯度参数α。由罗兰－C天波信号场强与相位的精细测量数据求得的。     

一种适用于有限长度混合相位未知脉冲的最小平方反褶积新方法

普通的最小平方反褶积只有在子波为最小相位脉冲和反射序列为白噪声的假设下才是成立的,而对于混合相位未知脉冲来说,这种最小平方反褶积就不再适用了。本文提出一种适合于有限长度混合相位未知脉冲的最小平方反褶积方法。运用此法只需预先估计一个混合相位子波的长度。合成数据的试验结果表明,该方法有较好的效果,且对预先估计出的子波长度不敏感。文中还证明混合相位未知脉冲最小平方反褶积等效于一种有间隙的平滑误差滤波器的作用,而这种有间隙的平滑误差滤波又可分解为一个前向多步预测误差滤波与一个后向多步预测误差滤波之和。可以预计这种新的反滤积方法将在地震数据处理中得到有效的应用。     

TBM地区低孔渗高产气储层预测

 本文从TBM地区目的层的地质和地球物理特征分析出发，以三维地震资料为基础，综合利用人工和神经网络波形分类等定性储层预测方法和利用反演及敏感地震属性分析，对TBM地区低孔渗砂岩分布区开展了砂体展布特征分析和天然气高产区预测研究。根据研究结果部署了10口钻井，已完钻的4口井中有3口获得高产气流（其中DK27井日产天然气36.8×10⁴m³）。值得指出的是，目前所用的预测方法还不能预测厚度小于6m的单砂层或砂层组构成的储层。