STATISTICAL PROCESSING OF LAMINAR WATER FLOW DATA - NONLINEAR REGRESSION BASED APPROACH

2013 ◽  
Author(s):  
Burian Jaroslav
Keyword(s):  
Water Flow ◽  
Nonlinear Regression ◽  
Statistical Processing ◽  
Flow Data

Control of Vortex Shedding of Circular Cylinder in Shallow Water Flow Using an Attached Splitter Plate

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Huseyin Akilli ◽  
Cuma Karakus ◽  
Atakan Akar ◽  
Besir Sahin ◽  
N. Filiz Tumen
Keyword(s):  
Circular Cylinder ◽  
Shallow Water ◽  
Water Flow ◽  
Vortex Shedding ◽  
Flow Characteristics ◽  
Splitter Plate ◽  
Wake Flow ◽  
Flow Data ◽  
Shallow Water Flow ◽  
Plate Length

In the present work, passive control of vortex shedding behind a circular cylinder by splitter plates of various lengths attached on the cylinder base is experimentally investigated in shallow water flow. Detailed measurements of instantaneous and time-averaged flow data of wake flow region at a Reynolds number of Re=6300 were obtained by particle image velocimetry technique. The length of the splitter plate was varied from L∕D=0.2 to L∕D=2.4 in order to see the effect of the splitter plate length on the flow characteristics. Instantaneous and time-averaged flow data clearly indicate that the length of the splitter plate has a substantial effect on the flow characteristics. The flow characteristics in the wake region of the circular cylinder sharply change up to the splitter plate length of L∕D=1.0. Above this plate length, small changes occur in the flow characteristics.

A New Comprehensive Model for Predicting the Pressure Drop of Flow in the Horizontal Wellbore

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Quan Zhang ◽  
Zhiming Wang ◽  
Xiaoqiu Wang ◽  
Jiankang Yang
Keyword(s):  
Pressure Drop ◽  
Water Flow ◽  
Single Phase ◽  
Variable Mass ◽  
Data Sets ◽  
Comprehensive Model ◽  
Flow Data ◽  
Experimental Apparatus ◽  
Oil Flow ◽  
Horizontal Wellbore

Over the past two decades, the modeling of flow in a perforated pipe with influx through wall openings has been recognized as a key topic especially in the field of horizontal wells. In this paper, based on the theoretical analysis and previous research achievements, combining with the new measured data sets stemming from the large-scale experimental apparatus designed and constructed recently at China University of Petroleum (CUP), a new comprehensive model has been developed for the prediction of pressure drop regarding single-phase flow in horizontal perforated pipes with wall influx, in which new correlations for calculating the hydraulic friction factor and momentum correction factor of variable mass flow are given. The presented model is then implemented using the visual basic.net package and validated against two data sets obtained on single-phase water flow and single-phase oil flow. Predictions of the new model and frequently used Ouyang model are also compared based on the new experimental data. Results show that the model given in this article can not only properly represent the complex mechanisms of flow in the horizontal wellbore, such as the resistance caused by wall perforations and the drag reduction or so-called lubrication effect caused by wall injection, but also has a preferable prediction accuracy. Compared with the water flow data and the oil flow data, the absolute average percentage errors of the proposed model are, respectively, 4.5% and 5.0%, which demonstrates better performance and wider application range than Ouyang model.

scholarly journals Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1691 ◽  
Author(s):  
Cristina Vega-Garcia ◽  
Mathieu Decuyper ◽  
Jorge Alcázar
Keyword(s):  
Neural Networks ◽  
Water Resources ◽  
Water Flow ◽  
General Rule ◽  
Hydrological Regime ◽  
Weather Data ◽  
Data Series ◽  
Flow Data ◽  
Cascade Correlation ◽  
Management Flow

The analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8).

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