scholarly journals Neural network estimation of the scour cone geometry around outlet in the pressure flushing

2013 ◽  
Vol 14 (4) ◽  
pp. 540-549
Keyword(s):  
Neural Network ◽  
Flow Velocity ◽  
Three Dimensional ◽  
Accurate Estimation ◽  
Mean Flow ◽  
Data Set ◽  
Network Estimation ◽  
Artificial Neural Network Ann ◽  
Bp Model ◽  
Mean Flow Velocity

When flushing carry out as pressure condition, a scour cone is performed around the outlet. As the flow around the outlet in the pressure flushing is three dimensional, therefore that it is difficult to establish a general empirical model to provide accurate estimation for scour cone volume and length. In this study artificial neural network (ANN) with multi-layer perception which using backpropagation algorithm (MLP/BP) was used. The scour cone volume (Vf) and length (Lf) were modeled as a function of three variables; water depth (HW), mean flow velocity through outlet (uf) and mean grain diameter (D50). For training and testing model, experimental data in two forms of original and non-dimensional are selected. The results of this research indicate that MLP/BP model can predict the scour cone volume and length. Finally, sensitivity analysis with original and nondimensional data set show that mean flow velocity through outlet (uf) and uf / √(g (Gs-1)D50) have a greater influence on scour cone volume and length rather than other parameters.

scholarly journals Discharge Estimation Using Tsallis and Shannon Entropy Theory in Natural Channels

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1786
Author(s):  
Jitendra Kumar Vyas ◽  
Muthiah Perumal ◽  
Tommaso Moramarco
Keyword(s):  
Flow Velocity ◽  
Surface Flow ◽  
Tsallis Entropy ◽  
Accurate Estimation ◽  
Mean Flow ◽  
Flow Area ◽  
Cross Sectional ◽  
The Cross ◽  
Discharge Estimation ◽  
Mean Flow Velocity

Streamflow measurements during high floods is a challenge for which the World Meteorological Organization fosters the development of innovative technologies for achieving an accurate estimation of the discharge. The use of non-contact sensors for monitoring surface flow velocities is of interest to turn these observed values into a cross-sectional mean flow velocity, and subsequently, into discharge if bathymetry is given. In this context, several techniques are available for the estimation of mean flow velocity, starting from observed surface velocities. Among them, the entropy-based methodology for river discharge assessment is often applied by leveraging the theoretical entropic principles of Shannon and Tsallis, both of which link the maximum flow velocity measured at a vertical of the flow area, named the y-axis, and the cross-sectional mean flow velocity at a river site. This study investigates the performance of the two different entropic approaches in estimating the mean flow velocity, starting from the maximum surface flow velocity sampled at the y-axis. A velocity dataset consisting of 70 events of measurements collected at two gauged stations with different geometric and hydraulic characteristics on the Po and Tiber Rivers in Italy was used for the analysis. The comparative evaluation of the velocity distribution observed at the y-axis of all 70 events of measurement was closely reproduced using both the Shannon and Tsallis entropy approaches. Accurate values in terms of the cross-sectional mean flow velocity and discharge were obtained with average errors not exceeding 10%, demonstrating that the Shannon and Tsallis entropy concepts were equally efficient for discharge estimation in any flow conditions.

scholarly journals Three-dimensional spatial structure of the macro-pores and flow simulation in anthracite coal based on X-ray μ-CT scanning data

2020 ◽  
Vol 17 (5) ◽  
pp. 1221-1236
Author(s):  
Hui-Huang Fang ◽  
Shu-Xun Sang ◽  
Shi-Qi Liu
Keyword(s):  
Spatial Structure ◽  
Flow Velocity ◽  
Single Channel ◽  
Three Dimensional ◽  
Bedding Plane ◽  
Mean Flow ◽  
Equivalent Radius ◽  
Flow Pressure ◽  
The Mean ◽  
Mean Flow Velocity

Abstract The three-dimensional (3D) structures of pores directly affect the CH4 flow. Therefore, it is very important to analyze the 3D spatial structure of pores and to simulate the CH4 flow with the connected pores as the carrier. The result shows that the equivalent radius of pores and throats are 1–16 μm and 1.03–8.9 μm, respectively, and the throat length is 3.28–231.25 μm. The coordination number of pores concentrates around three, and the intersection point between the connectivity function and the X-axis is 3–4 μm, which indicate the macro-pores have good connectivity. During the single-channel flow, the pressure decreases along the direction of CH4 flow, and the flow velocity of CH4 decreases from the pore center to the wall. Under the dual-channel and the multi-channel flows, the pressure also decreases along the CH4 flow direction, while the velocity increases. The mean flow pressure gradually decreases with the increase of the distance from the inlet slice. The change of mean flow pressure is relatively stable in the direction horizontal to the bedding plane, while it is relatively large in the direction perpendicular to the bedding plane. The mean flow velocity in the direction horizontal to the bedding plane (Y-axis) is the largest, followed by that in the direction horizontal to the bedding plane (X-axis), and the mean flow velocity in the direction perpendicular to the bedding plane is the smallest.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Experimental Investigation of the Acoustic Reflection Coefficient of a Modeled Gas Turbine Impingement Cooling Section

2012 ◽  
Author(s):  
Christoph Jörg ◽  
Michael Wagner ◽  
Thomas Sattelmayer
Keyword(s):  
Reflection Coefficient ◽  
Flow Velocity ◽  
Gas Turbines ◽  
Acoustic Energy ◽  
Quality Data ◽  
Mean Flow ◽  
Impingement Cooling ◽  
Acoustic Reflection ◽  
The Mean ◽  
Mean Flow Velocity

The thermoacoustic stability of gas turbines depends on a balance of acoustic energy inside the engine. While the flames produce acoustic energy, other areas like the impingement cooling system contribute to damping. In this paper, we investigate the damping potential of an annular impingement sleeve geometry embedded into a realistic environment. A cold flow test rig was designed to represent real engine conditions in terms of geometry, and flow situation. High quality data was delivered by six piezoelectric dynamic pressure sensors. Experiments were carried out for different mean flow velocities through the cooling holes. The acoustic reflection coefficient of the impingement sleeve was evaluated at a downstream reference location. Further parameters investigated were the number of cooling holes, and the geometry of the chamber surrounding the impingement sleeve. Experimental results show that the determining parameter for the reflection coefficient is the mean flow velocity through the impingement holes. An increase of the mean flow velocity leads to significantly increased damping, and to low values of the reflection coefficient.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

scholarly journals A SELF-ADAPTABLE DISTRIBUTED CBR VERSION OF THE EQUIVOX SYSTEM

2016 ◽  
Vol 28 (04) ◽  
pp. 1650028
Author(s):  
Julien Henriet ◽  
Christophe Lang ◽  
Ronnie Muthada Pottayya ◽  
Karla Breschi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Three Dimensional ◽  
Multi Agent System ◽  
Accidental Exposure ◽  
Interpolation Accuracy ◽  
Multi Agent ◽  
Artificial Neural Network Ann ◽  
Numerical Representations ◽  
Voxel Phantoms

Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.

Abstract WP432: Non-invasive Respiratory Impedance Enhances Cerebral Perfusion

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Christopher G Favilla ◽  
Ashwin B Parthasarathy ◽  
John A Detre ◽  
Michael T Mullen ◽  
Scott E Kasner ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cardiac Output ◽  
Flow Velocity ◽  
Cerebral Perfusion ◽  
Respiratory Impedance ◽  
Mean Flow ◽  
Non Invasive ◽  
End Tidal ◽  
High Level ◽  
Mean Flow Velocity

Background: Optimization of cerebral blood flow is the cornerstone of clinical management in a number of neurologic diseases, most notably ischemic stroke. Intra-thoracic pressure influences cardiac output and has the potential to impact cerebral blood flow (CBF). Here we aim to quantify cerebral hemodynamic changes in response to increased respiratory impedance using a non-invasive respiratory device. Methods: Cerebral perfusion was measured under varying levels of respiratory impedance (6cm H 2 0, 9cm H 2 0, and 12 cm H 2 0) in 20 healthy volunteers. Simultaneous measurements of microvascular CBF and middle cerebral artery mean flow velocity (MFV), respectively, were performed with optical diffuse correlation spectroscopy (DCS) and transcranial Doppler ultrasound (TCD). Results: At the high level of respiratory impedance, mean flow velocity increased by 6.4% compared to baseline (p=0.004), but changes in cortical CBF were smaller and non-significant (Figure). Heart rate, cardiac output, respiratory rate, and end tidal CO 2 remained stable during all levels of respiratory impedance. There was small increase in mean arterial blood pressure, 1.7% (p=0.006), at the high level of respiratory impedance. In a multivariable linear regression model accounting for end tidal CO 2 and individual variability, respiratory impedance was associated with increases in both mean flow velocity (coefficient: 0.49, p<0.001) and cortical CBF (coefficient: 0.13, p<0.001). Conclusions: Manipulating intrathoracic pressure via non-invasive respiratory impedance was well tolerated and produced a small but measurable increase in cerebral perfusion in healthy individuals. Future studies in acute ischemic stroke patients with impaired cerebral autoregulation is warranted in order to assess whether respiratory impedance is feasible as a novel non-invasive therapy for stroke.

scholarly journals A new approach to estimating Mean Flow in the UK

2002 ◽  
Vol 6 (4) ◽  
pp. 709-720 ◽  
Author(s):  
M. G. R. Holmes ◽  
A. R. Young ◽  
A. Gustard ◽  
R. Grew
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Accurate Estimation ◽  
Mean Flow ◽  
Time Step ◽  
Runoff Water ◽  
Data Set ◽  
Water Balance Models ◽  
The Uk ◽  
Drying Curve

Abstract. Traditionally, the estimation of Mean Flow (MF) in ungauged catchments has been approached using conceptual water balance models or empirical formulae relating climatic inputs to stream flow. In the UK, these types of models have difficulty in predicting MF in low rainfall areas because the conceptualisation of soil moisture behaviour and its relationship with evaporation rates used is rather simplistic. However, it is in these dry regions where the accurate estimation of flows is most critical to effective management of a scarce resource. A novel approach to estimating MF, specifically designed to improve estimation of runoff in dry catchments, has been developed using a regionalisation of the Penman drying curve theory. The dynamic water balance style Daily Soil Moisture Accounting (DSMA) model operates at a daily time step, using inputs of precipitation and potential evaporation and simulates the development of soil moisture deficits explicitly. The model has been calibrated using measured MFs from a large data set of catchments in the United Kingdom. The performance of the DSMA model is superior to existing established steady state and dynamic water-balance models over the entire data set considered and the largest improvement is observed in very low rainfall catchments. It is concluded that the performance of all models in high rainfall areas is likely to be limited by the spatial representation of rainfall. Keywords: hydrological models, regionalisation, water resources, mean flow, runoff, water balance, Penman drying curve, soil moisture model

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