A Neural Network Approach to Analyse Cavitating Flow Regime in an Internal Orifice

2012 ◽  
Author(s):  
M. G. De Giorgi ◽  
D. Bello ◽  
A. Ficarella
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Regime ◽  
Cavitating Flow ◽  
Operating Conditions ◽  
Elman Neural Network ◽  
Hydraulic Machines ◽  
Wide Range ◽  
Artificial Neural ◽  
The Impact

The identification of the water cavitation regime is an important issue in a wide range of machines, as hydraulic machines and internal combustion engine. In the present work several experiments on a water cavitating flow were conducted in order to investigate the influence of pressures and temperature on flow regime transition. In some cases, as the injection of hot fluid or the cryogenic cavitation, the thermal effects could be important. The cavitating flow pattern was analyzed by the images acquired by the high-speed camera and by the pressure signals. Four water cavitation regimes were individuated by the visualizations: no-cavitation, developing, super and jet cavitation. As by image analysis, also by the frequency analysis of the pressure signals, different flow behaviours were identified at the different operating conditions. A useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon is the application of Artificial Neural Network (ANN). In the present study a three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. Results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence the different cavitation regimes and the impact of the temperature. A theoretical analysis has been also performed to justify the results of the experimental observations. In this approach the nonlinear dynamics of the bubbles growth have been used on an homogenous vapor-liquid mixture model, so to couple the effects of the internal dynamic bubble with the other flow parameters.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

Artificial neural network model for evaluation the effect of surface properties amendment on slurry erosion behavior of AISI 5117 steel

2016 ◽  
Vol 68 (6) ◽  
pp. 676-682 ◽  
Author(s):  
Bahaa Saleh ◽  
Ayman A. Aly
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Computational Technique ◽  
Slurry Erosion ◽  
Content Type ◽  
Erosion Behavior ◽  
Wide Range ◽  
Artificial Neural ◽  
Effect Of Surface

Purpose The aim of this paper is to evaluate the effect of surface treatment on slurry erosion behavior of AISI 5,117 steel using artificial neural network (ANN) technique. Design/methodology/approach The slurry erosion wear behavior of electroless nickel-phosphorus (Ni-P) coated, carburized and untreated AISI 5,117 alloy steel was investigated experimentally and theoretically using ANN technique based on error back propagation learning algorithm. Findings From the obtained results, it can be concluded that the proposed AAN model can be successfully used for evaluating slurry erosion behavior of the Ni-P coated, carburized and untreated AISI 5,117 steel for wide range of operating conditions and Ni-P coating and carburizing improve the slurry erosion resistance of AISI 5,117 steel; however, the coating is more efficient. Originality/value Slurry erosion is a serious problem for the performance, reliability and service life of engineering components used in many industrial applications. To improve the performance of these components, engineering surface technologies have been attracting a great deal of attention. The extent of slurry erosion is dependent on a wide range of variables. To account all variables that effect on erosion behavior, prediction of erosion behavior by soft computational technique is one of the most important requirements. ANN has the ability to tackle the problem of complex relationships among variables that cannot be accomplished by traditional analytical methods.

scholarly journals Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

2009 ◽  
Vol 15 (2) ◽  
pp. 103-117 ◽  
Author(s):  
S.K. Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Superior Performance ◽  
Slurry Flow ◽  
Wide Range ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Regime identification of slurry transport in pipelines: A novel modelling approach using ANN & differential evolution

2010 ◽  
Vol 16 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Sandip Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Slurry Transport ◽  
Pipeline Design

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and differential evolution technique (ANN - DE) for efficient tuning of ANN Meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN - DE method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters. .

Artificial Neural Network Model with Parameter Tuning Assisted by Differential Evolution Technique: Study of Pressure Drop of Slurry Flow in Pipeline

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
S.K. Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Drop ◽  
Differential Evolution ◽  
Operating Conditions ◽  
Superior Performance ◽  
Slurry Flow ◽  
Wide Range ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper describes a robust hybrid artificial neural network (ANN) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of pressure drops over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5362
Author(s):  
Antonio Rosato ◽  
Francesco Guarino ◽  
Sergio Sibilio ◽  
Evgueniy Entchev ◽  
Massimiliano Masullo ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Temporal Trends ◽  
Climatic Conditions ◽  
Fault Detection And Diagnosis ◽  
Hvac System ◽  
Wide Range ◽  
Artificial Neural ◽  
The Impact

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions.

Modeling of Thermal Cracking Furnaces Via Exergy Analysis Using Hybrid Artificial Neural Network–Genetic Algorithm

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
M. Alizadeh ◽  
S. M. Sadrameli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exergy Analysis ◽  
Calculated Data ◽  
Operating Conditions ◽  
Detailed Knowledge ◽  
Outlet Temperature ◽  
Exergetic Efficiency ◽  
Wide Range ◽  
Artificial Neural

In this study, we try to make an exergy analysis of an olefin cracking furnace more understandable by coupling it with the use of an artificial neural network–generic algorithm (ANN–GA) modeling. The presented method permits to provide an energy diagnosis of the process under a wide range of operating conditions. As a case study, one of the petrochemical complexes in Iran has been considered. The Petrosim process simulator software was used to obtain thermodynamic properties of the process streams and to perform exergy balances. The results are validated with industrial data obtained from the plant. The exergy destruction and exergetic efficiency for the main system components and the entire system were calculated. The simulation results reveal that the exergetic loss of the process increases with increasing steam ratio (SR) and decreases with coil outlet temperature (COT) and residence time (RT). The results show that the overall exergetic efficiency of the system is about 65%. The recorded and calculated data have been used as inputs for the neural network. The results show that ANN–GA is a highly effective method to optimize the performance of the neural networks, predicting the overall exergy efficiency. Comparing to phenomenological modeling based on the detailed knowledge of the furnace condition, the use of the introduced ANN–GA model saves significant amount of the time needed for the performance prediction of cracking furnaces.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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