This document describes an evaluation of natural draft wet cooling tower (NDWCT) performance using different packing fills in Iraq via artificial neural networks (ANN). Experimental tests were conducted on a NDWCT rig using honeycomb, splash, and trickle fills under varying conditions. An ANN with 10 hidden neurons was developed using the Levenberg-Marquardt backpropagation algorithm in MATLAB to predict the experimental results. The ANN predictions showed good agreement with the experiments based on correlation coefficients above 0.994, low root mean square errors below 6%, and mean relative errors below 8.4%.
2011 santiago marchi_souza_araki_cobem_2011CosmoSantiago
This document discusses the performance of the multigrid method for solving the Navier-Stokes equations using two alternative formulations: streamfunction-velocity and streamfunction-vorticity. The study examines the lid-driven cavity flow problem for different grid sizes, Reynolds numbers, numbers of grid levels, and inner iterations to determine optimal parameter values that minimize CPU time. Previous research has found that the multigrid method is less effective for the Navier-Stokes equations at higher Reynolds numbers. The document aims to investigate how formulation choice and multigrid parameters influence efficiency.
This document presents a comparative study of the dynamic optical filtering and temperature sensing capabilities of one-dimensional binary and ternary photonic crystals (PHCs). The study finds that:
1) The binary PHC shows four optical bandgaps compared to seven bandgaps in the ternary PHC.
2) For temperatures between 100-700K, both PHCs show improved temperature sensing effectiveness as temperature increases, except for the first bandgap of the binary and the first two bandgaps of the ternary.
3) The binary PHC performs better as a temperature sensor between 840-1100nm, but the ternary PHC outperforms at longer wavelengths.
4)
Numerical Experiments of Hydrogen-Air Premixed FlamesIJRES Journal
Numerical experiments have been carried out to study turbulent premixed flames of hydrogen-air mixtures in a small scale combustion chamber. Flow is calculated using the Large Eddy Simulation (LES) Technique for turbulent flow. The chemical reaction is modeled using a dynamic procedure for the calculation of the flame/flow interactions. Sensitivity of the results obtained to the computational grid, ignition source and different flow configurations have been carried out. Numerical results are validated against published experimental data. It was found that the grid resolution has very small effect on the results after a certain grid. Also, the ignition source has influenced only the time where the peak overpressure appears. Finally, the different configurations are reported to affect both the peak overpressure and flame position.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
Correlation Of Cbr Value With Properties Of Red SoilIRJET Journal
This document summarizes a study that aimed to establish relationships between the California Bearing Ratio (CBR) value and index properties of red soil, a type of soil locally available in Kerala, India. Three soil samples were collected from different locations and tested to determine their geotechnical properties, including CBR value, maximum dry density, optimum moisture content, liquid limit, plastic limit, and plasticity index. Simple and multiple linear regression analyses were used to develop correlations between CBR value and the index properties. The results showed that CBR had the highest correlation with optimum moisture content. Validation of the regression models found that predicted CBR values closely matched actual values, indicating a good relationship between CBR and soil properties for red soil
This document summarizes a study on the effect of parameters of a geometric multigrid method on CPU time for solving one-dimensional problems related to heat transfer and fluid flow. The parameters studied include coarsening ratio of grids, number of inner iterations, number of grid levels, and tolerances. Finite difference methods were used to discretize partial differential equations for problems involving Poisson, advection-diffusion, and heat transfer equations. Comparisons were made between multigrid and single grid methods like Gauss-Seidel and TDMA. Results confirmed some literature findings and presented some new results on the effect of parameters on CPU time.
This document presents a data-driven approach to establish relationships between the microstructure and hydraulic conductivity of packed soil particles. Soil particle packings were generated numerically and their microstructures characterized using 2-point statistics. Hydraulic conductivity was estimated using finite volume simulations. Principal component analysis was used to reduce the microstructural data, and regression analysis was employed to correlate hydraulic conductivity with the principal components, establishing a structure-property relationship. Leave-one-out cross validation was used to assess the regression models.
The document outlines a study characterizing various carbon samples for use as fuel cell catalyst supports. Raman spectroscopy and XRD were used to analyze crystallinity, while TEM provided visualization of crystallite size and structure. Additional techniques including TGA and gas adsorption were proposed but not completed due to time constraints. The results from Raman, XRD and preliminary TEM indicated some samples were mislabeled, highlighting the need for the characterization protocol to reliably evaluate carbon properties for fuel cell applications.
2011 santiago marchi_souza_araki_cobem_2011CosmoSantiago
This document discusses the performance of the multigrid method for solving the Navier-Stokes equations using two alternative formulations: streamfunction-velocity and streamfunction-vorticity. The study examines the lid-driven cavity flow problem for different grid sizes, Reynolds numbers, numbers of grid levels, and inner iterations to determine optimal parameter values that minimize CPU time. Previous research has found that the multigrid method is less effective for the Navier-Stokes equations at higher Reynolds numbers. The document aims to investigate how formulation choice and multigrid parameters influence efficiency.
This document presents a comparative study of the dynamic optical filtering and temperature sensing capabilities of one-dimensional binary and ternary photonic crystals (PHCs). The study finds that:
1) The binary PHC shows four optical bandgaps compared to seven bandgaps in the ternary PHC.
2) For temperatures between 100-700K, both PHCs show improved temperature sensing effectiveness as temperature increases, except for the first bandgap of the binary and the first two bandgaps of the ternary.
3) The binary PHC performs better as a temperature sensor between 840-1100nm, but the ternary PHC outperforms at longer wavelengths.
4)
Numerical Experiments of Hydrogen-Air Premixed FlamesIJRES Journal
Numerical experiments have been carried out to study turbulent premixed flames of hydrogen-air mixtures in a small scale combustion chamber. Flow is calculated using the Large Eddy Simulation (LES) Technique for turbulent flow. The chemical reaction is modeled using a dynamic procedure for the calculation of the flame/flow interactions. Sensitivity of the results obtained to the computational grid, ignition source and different flow configurations have been carried out. Numerical results are validated against published experimental data. It was found that the grid resolution has very small effect on the results after a certain grid. Also, the ignition source has influenced only the time where the peak overpressure appears. Finally, the different configurations are reported to affect both the peak overpressure and flame position.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
Correlation Of Cbr Value With Properties Of Red SoilIRJET Journal
This document summarizes a study that aimed to establish relationships between the California Bearing Ratio (CBR) value and index properties of red soil, a type of soil locally available in Kerala, India. Three soil samples were collected from different locations and tested to determine their geotechnical properties, including CBR value, maximum dry density, optimum moisture content, liquid limit, plastic limit, and plasticity index. Simple and multiple linear regression analyses were used to develop correlations between CBR value and the index properties. The results showed that CBR had the highest correlation with optimum moisture content. Validation of the regression models found that predicted CBR values closely matched actual values, indicating a good relationship between CBR and soil properties for red soil
This document summarizes a study on the effect of parameters of a geometric multigrid method on CPU time for solving one-dimensional problems related to heat transfer and fluid flow. The parameters studied include coarsening ratio of grids, number of inner iterations, number of grid levels, and tolerances. Finite difference methods were used to discretize partial differential equations for problems involving Poisson, advection-diffusion, and heat transfer equations. Comparisons were made between multigrid and single grid methods like Gauss-Seidel and TDMA. Results confirmed some literature findings and presented some new results on the effect of parameters on CPU time.
This document presents a data-driven approach to establish relationships between the microstructure and hydraulic conductivity of packed soil particles. Soil particle packings were generated numerically and their microstructures characterized using 2-point statistics. Hydraulic conductivity was estimated using finite volume simulations. Principal component analysis was used to reduce the microstructural data, and regression analysis was employed to correlate hydraulic conductivity with the principal components, establishing a structure-property relationship. Leave-one-out cross validation was used to assess the regression models.
The document outlines a study characterizing various carbon samples for use as fuel cell catalyst supports. Raman spectroscopy and XRD were used to analyze crystallinity, while TEM provided visualization of crystallite size and structure. Additional techniques including TGA and gas adsorption were proposed but not completed due to time constraints. The results from Raman, XRD and preliminary TEM indicated some samples were mislabeled, highlighting the need for the characterization protocol to reliably evaluate carbon properties for fuel cell applications.
The document describes a study that uses fuzzy logic to predict porosity from well log data. It discusses (1) normalizing the input data, (2) using subtractive clustering to identify clusters and membership functions, and (3) developing fuzzy rules with Gaussian membership functions to relate inputs like density, sonic, and neutron logs to the output of porosity. The results showed fuzzy logic predictions of porosity were more accurate than those from multiple linear regression on the same well log data.
This document presents a chain sampling plan for truncated life tests when product lifetime follows a log-logistic distribution. It provides the minimum sample size needed to ensure a specified acceptance probability while satisfying producer and consumer risks, for various quality levels. Tables 1 and 2 show the minimum sample sizes and operating characteristic functions for the proposed sampling plan for different confidence levels, acceptance numbers, and ratios of test time to scale parameter. For example, a sample size of 10 is required for a confidence level of 0.99, acceptance number of 2, and time-to-scale ratio of 0.942.
This document proposes an improved particle swarm optimization (PSO) algorithm for data clustering that incorporates Gauss chaotic map. PSO is often prone to premature convergence, so the proposed method uses Gauss chaotic map to generate random sequences that substitute the random parameters in PSO, providing more exploration of the search space. The algorithm is tested on six real-world datasets and shown to outperform K-means, standard PSO, and other hybrid clustering algorithms. The key aspects of the proposed GaussPSO method and experimental results demonstrating its effectiveness are described.
This document summarizes work using molecular dynamics simulations to calculate the viscosity of liquid nickel. The researchers used a modified embedded atom method potential to simulate liquid nickel across a range of temperatures. Preliminary results for the viscosity fell within the range of available experimental data. Future work involves further testing and developing optimized potentials for nickel alloys and calculating other physically relevant parameters for larger scale simulations.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Parallel Guided Local Search and Some Preliminary Experimental Results for Co...csandit
This document proposes a Parallel Guided Local Search (PGLS) algorithm for continuous optimization problems. PGLS runs multiple Guided Local Search agents in parallel that periodically exchange information. The agents use local search and crossover to explore the search space. Preliminary experiments on benchmark functions show PGLS performs better than single-agent Guided Local Search by efficiently utilizing parallel computing resources and information exchange between agents.
Carrier kinetics analysis with two-defect level model in a CdS/CIGSe junction...Ashwin Hariharan
This document discusses using a two-defect kinetic model and time-resolved photoluminescence spectroscopy (TRPL) to analyze carrier kinetics in a CdS/CIGSe junction. The model considers trapping and transport effects and is used to understand how the space charge region (SCR) affects bulk carrier dynamics. Key findings include: (1) the bulk shows a bi-exponential decay, with the SCR causing the initial decay time constant to change; (2) transport-limited recombination in the quasi-neutral region accounts for this changed time constant; (3) intensity- and wavelength-dependent TRPL measurements provide information about SCR and bulk carrier dynamics that can assess absorber quality.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
The document summarizes research on simulating the surface tension of liquid nickel through molecular dynamics and density functional theory calculations. It describes using embedded atom method interatomic potentials to model nickel, compares calculated density-temperature data to experimental values, and presents a molecular dynamics simulation of a molten nickel nanoparticle in good agreement with experiment. Future work is outlined on modeling nickel-aluminum alloys, oxidation effects, and temperature-dependent interdiffusion at metal-metal interfaces.
Mathematical Calculation toFindtheBest Chamber andDetector Radii Used for Mea...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
This paper relates the best practices in Log preprocessing in Petrophysics which
are necessary to have a good model for Facies and Permeability. The well-logs which
were used for the Electrofacies modeling and permeability modeling consist of
Gamma-Ray(GR), Bulk Density Porosity(RHOB) Neutron porosity(NPHI).
Meanwhile, the model distinct type of facies consists of sand, Shaly sand, and shale.
Precise Electrofacies sorting was accomplished by the Multi-Resolution Graph-based
Clustering (MRGC). The improvement in the Logs from the Well-X1 after undergoing
pre-processing like Log Normalization, Compaction Effect Removal, Fluid Effect
Removal returned the logs to their natural states and were used as input into Multi-
Resolution Graph-based Clustering (MRGC) model to produce better output Facies
and Permeability when compared to the Output which did not undergo preprocessing.
These practices can be utilized to validate very good Facies and
Permeability Models
Advantages of the self organizing controller for high-pressure sterilization ...ISA Interchange
A study of a self-organizing controller is implemented in a way that response to controlled system follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in sterilization equipment in order to verify the capacity of the response to any change in the pressure or temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive training and one-dimension arrays.
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENE...ecij
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem.
Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a
limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA)
to overcome this limitation, by starting with random solutions within the search space and narrowing
down the search space by considering the minimum and maximum errors of the population members.
Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to
converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro
thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
11.on the solution of incompressible fluid flow equationsAlexander Decker
This document summarizes a study comparing the performance of three iterative methods - Gauss-Seidel, Point Successive Over-relaxation, and Generalized Minimal Residual - for solving large sparse linear systems arising from numerical computations of incompressible Navier-Stokes equations. The study finds that as the mesh size increases, the Generalized Minimal Residual method converges faster and requires less CPU time than the other two methods.
On the solution of incompressible fluid flow equationsAlexander Decker
This document compares the performance of three iterative methods - Gauss-Seidel, Point Successive Over-Relaxation, and Generalized Minimal Residual - for solving large sparse linear systems arising from computations of incompressible Navier-Stokes equations. The methods are applied to solve the lid-driven cavity benchmark problem. It is found that as the mesh size increases, GMRES converges faster and requires less CPU time than the other two methods.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
The document discusses calculating the binding energy of a donor atom located within a spherical quantum dot made of GaAs. It presents two cases - where the donor is at the center of the dot and where it is on the surface. The binding energy is computed using an effective mass approximation and variational approach. The results show the binding energy decreases with increasing dot size and is highest when the donor is at the center. The document also examines how an external electric field affects the binding energy and polarizability of the donor atom.
GPR Probing of Smoothly Layered Subsurface Medium: 3D Analytical ModelLeonid Krinitsky
An analytical approach to GPR probing of a
horizontally layered subsurface medium is developed, based on the coupled-wave WKB approximation. An empirical model of current in dipole transmitter antenna is used.
This document summarizes interference avoidance techniques for OFDM-based cellular networks. It discusses how OFDM is used to reduce interference and improve capacity. It then categorizes interference coordination techniques into interference mitigation and interference avoidance. Various static interference avoidance techniques are described, including conventional frequency planning, fractional frequency reuse, partial frequency reuse, and soft frequency reuse. These techniques aim to reduce inter-cell interference by controlling frequency usage and power levels in different network channels and regions.
The document summarizes lightning phenomenon and describes various types of lightning strikes and their effects. It discusses lightning protection methods for structures. Key points:
1. Lightning is a sudden discharge between clouds or between clouds and the ground. It can cause significant damage to structures and injury.
2. There are different types of ground lightning strikes depending on the direction of charge. The most common is negative cloud-to-ground lightning.
3. Effects of lightning include thermal, acoustic, luminous, electrodynamic, and indirect effects from surges. Protection methods aim to safely conduct lightning currents to ground.
The document describes a study that uses fuzzy logic to predict porosity from well log data. It discusses (1) normalizing the input data, (2) using subtractive clustering to identify clusters and membership functions, and (3) developing fuzzy rules with Gaussian membership functions to relate inputs like density, sonic, and neutron logs to the output of porosity. The results showed fuzzy logic predictions of porosity were more accurate than those from multiple linear regression on the same well log data.
This document presents a chain sampling plan for truncated life tests when product lifetime follows a log-logistic distribution. It provides the minimum sample size needed to ensure a specified acceptance probability while satisfying producer and consumer risks, for various quality levels. Tables 1 and 2 show the minimum sample sizes and operating characteristic functions for the proposed sampling plan for different confidence levels, acceptance numbers, and ratios of test time to scale parameter. For example, a sample size of 10 is required for a confidence level of 0.99, acceptance number of 2, and time-to-scale ratio of 0.942.
This document proposes an improved particle swarm optimization (PSO) algorithm for data clustering that incorporates Gauss chaotic map. PSO is often prone to premature convergence, so the proposed method uses Gauss chaotic map to generate random sequences that substitute the random parameters in PSO, providing more exploration of the search space. The algorithm is tested on six real-world datasets and shown to outperform K-means, standard PSO, and other hybrid clustering algorithms. The key aspects of the proposed GaussPSO method and experimental results demonstrating its effectiveness are described.
This document summarizes work using molecular dynamics simulations to calculate the viscosity of liquid nickel. The researchers used a modified embedded atom method potential to simulate liquid nickel across a range of temperatures. Preliminary results for the viscosity fell within the range of available experimental data. Future work involves further testing and developing optimized potentials for nickel alloys and calculating other physically relevant parameters for larger scale simulations.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Parallel Guided Local Search and Some Preliminary Experimental Results for Co...csandit
This document proposes a Parallel Guided Local Search (PGLS) algorithm for continuous optimization problems. PGLS runs multiple Guided Local Search agents in parallel that periodically exchange information. The agents use local search and crossover to explore the search space. Preliminary experiments on benchmark functions show PGLS performs better than single-agent Guided Local Search by efficiently utilizing parallel computing resources and information exchange between agents.
Carrier kinetics analysis with two-defect level model in a CdS/CIGSe junction...Ashwin Hariharan
This document discusses using a two-defect kinetic model and time-resolved photoluminescence spectroscopy (TRPL) to analyze carrier kinetics in a CdS/CIGSe junction. The model considers trapping and transport effects and is used to understand how the space charge region (SCR) affects bulk carrier dynamics. Key findings include: (1) the bulk shows a bi-exponential decay, with the SCR causing the initial decay time constant to change; (2) transport-limited recombination in the quasi-neutral region accounts for this changed time constant; (3) intensity- and wavelength-dependent TRPL measurements provide information about SCR and bulk carrier dynamics that can assess absorber quality.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
The document summarizes research on simulating the surface tension of liquid nickel through molecular dynamics and density functional theory calculations. It describes using embedded atom method interatomic potentials to model nickel, compares calculated density-temperature data to experimental values, and presents a molecular dynamics simulation of a molten nickel nanoparticle in good agreement with experiment. Future work is outlined on modeling nickel-aluminum alloys, oxidation effects, and temperature-dependent interdiffusion at metal-metal interfaces.
Mathematical Calculation toFindtheBest Chamber andDetector Radii Used for Mea...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
This paper relates the best practices in Log preprocessing in Petrophysics which
are necessary to have a good model for Facies and Permeability. The well-logs which
were used for the Electrofacies modeling and permeability modeling consist of
Gamma-Ray(GR), Bulk Density Porosity(RHOB) Neutron porosity(NPHI).
Meanwhile, the model distinct type of facies consists of sand, Shaly sand, and shale.
Precise Electrofacies sorting was accomplished by the Multi-Resolution Graph-based
Clustering (MRGC). The improvement in the Logs from the Well-X1 after undergoing
pre-processing like Log Normalization, Compaction Effect Removal, Fluid Effect
Removal returned the logs to their natural states and were used as input into Multi-
Resolution Graph-based Clustering (MRGC) model to produce better output Facies
and Permeability when compared to the Output which did not undergo preprocessing.
These practices can be utilized to validate very good Facies and
Permeability Models
Advantages of the self organizing controller for high-pressure sterilization ...ISA Interchange
A study of a self-organizing controller is implemented in a way that response to controlled system follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in sterilization equipment in order to verify the capacity of the response to any change in the pressure or temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive training and one-dimension arrays.
HYDROTHERMAL COORDINATION FOR SHORT RANGE FIXED HEAD STATIONS USING FAST GENE...ecij
This paper presents a Fast genetic algorithm for solving Hydrothermal coordination (HTC) problem.
Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a
limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA)
to overcome this limitation, by starting with random solutions within the search space and narrowing
down the search space by considering the minimum and maximum errors of the population members.
Since the search space is restricted to a small region within the available search space the algorithm
works very fast. This algorithm reduces the computational burden and number of generations to
converge. The proposed algorithm has been demonstrated for HTC of various combinations of Hydro
thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using
Fast GA are compared with simple (conventional) GA and found to be encouraging.
11.on the solution of incompressible fluid flow equationsAlexander Decker
This document summarizes a study comparing the performance of three iterative methods - Gauss-Seidel, Point Successive Over-relaxation, and Generalized Minimal Residual - for solving large sparse linear systems arising from numerical computations of incompressible Navier-Stokes equations. The study finds that as the mesh size increases, the Generalized Minimal Residual method converges faster and requires less CPU time than the other two methods.
On the solution of incompressible fluid flow equationsAlexander Decker
This document compares the performance of three iterative methods - Gauss-Seidel, Point Successive Over-Relaxation, and Generalized Minimal Residual - for solving large sparse linear systems arising from computations of incompressible Navier-Stokes equations. The methods are applied to solve the lid-driven cavity benchmark problem. It is found that as the mesh size increases, GMRES converges faster and requires less CPU time than the other two methods.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
The document discusses calculating the binding energy of a donor atom located within a spherical quantum dot made of GaAs. It presents two cases - where the donor is at the center of the dot and where it is on the surface. The binding energy is computed using an effective mass approximation and variational approach. The results show the binding energy decreases with increasing dot size and is highest when the donor is at the center. The document also examines how an external electric field affects the binding energy and polarizability of the donor atom.
GPR Probing of Smoothly Layered Subsurface Medium: 3D Analytical ModelLeonid Krinitsky
An analytical approach to GPR probing of a
horizontally layered subsurface medium is developed, based on the coupled-wave WKB approximation. An empirical model of current in dipole transmitter antenna is used.
This document summarizes interference avoidance techniques for OFDM-based cellular networks. It discusses how OFDM is used to reduce interference and improve capacity. It then categorizes interference coordination techniques into interference mitigation and interference avoidance. Various static interference avoidance techniques are described, including conventional frequency planning, fractional frequency reuse, partial frequency reuse, and soft frequency reuse. These techniques aim to reduce inter-cell interference by controlling frequency usage and power levels in different network channels and regions.
The document summarizes lightning phenomenon and describes various types of lightning strikes and their effects. It discusses lightning protection methods for structures. Key points:
1. Lightning is a sudden discharge between clouds or between clouds and the ground. It can cause significant damage to structures and injury.
2. There are different types of ground lightning strikes depending on the direction of charge. The most common is negative cloud-to-ground lightning.
3. Effects of lightning include thermal, acoustic, luminous, electrodynamic, and indirect effects from surges. Protection methods aim to safely conduct lightning currents to ground.
This document summarizes an article about India's energy policy and the need to promote renewable energy sources. It discusses how India has vast renewable energy resources and the government has implemented various policies and incentives to promote greater renewable energy deployment. The key challenges are India's limited fossil fuel reserves, high fuel transportation costs, aging conventional power plants, need to rationalize power tariffs, and reduce transmission and distribution losses in the power sector. The government is aiming to source 10% of additional grid power capacity from renewable sources by 2012 to help address these issues through its renewable energy policies.
This document compares the performance of passive and semi-active suspension systems using MATLAB/Simulink. It presents a quarter car model with both passive and semi-active suspensions. The semi-active system uses a magneto-rheological (MR) damper modeled by the Bingham model. Simulation results show the sprung mass acceleration is reduced by 93.9% and 63.7% for step and half-sine road inputs respectively with the semi-active system compared to passive. Therefore, the semi-active MR damper provides better ride comfort than the conventional passive damper.
OSPCV: Off-line Signature Verification using Principal Component VariancesIOSR Journals
This document presents an offline signature verification system called OSPCV (Offline Signature Verification using Principal Component Variances) that analyzes two features - pixel density and center of gravity distance. It describes the related work in signature verification, the proposed OSPCV algorithm, and experimental results showing it provides a notable improvement over existing systems. The OSPCV system overcomes intra-signature and inter-signature variations to produce a better equal error rate for differentiating genuine versus forged signatures.
This document discusses weight optimization of the vertical tail in-board box structure of an aircraft through stress analysis. The vertical tail structure is modeled in CATIA and imported into MSC Patran for finite element analysis. The structure is meshed and material properties are applied. Boundary conditions representing the fixed root and free top surface are applied. Stress analysis is performed and high stress regions are identified. An iterative approach is used to introduce lightening cutouts in the spar and rib webs to reduce weight. Two iterations are performed, reducing the total weight by 1.66kg while maintaining similar deformation levels and stresses below material yield strength, demonstrating an effective weight optimization approach.
This document summarizes a paper that presents a novel method for determining the optimal location of Flexible AC Transmission System (FACTS) controllers in a multi-machine power system using a Fuzzy Controlled Genetic Algorithm (FCGA). The proposed algorithm aims to simultaneously optimize the location, type, and rated values of FACTS controllers while minimizing the overall system cost, which includes generation and investment costs. The algorithm is tested on IEEE 14-bus and 30-bus test systems, incorporating thyristor-controlled series compensator (TCSC) and unified power flow controller (UPFC) devices. Simulation results show the obtained solution is feasible and accurate for solving the optimal power flow problem.
Comparative Analysis of the Different Brassica OleraceaVarieties Grown on Jos...IOSR Journals
This study was carried out to determine and compare the phytochemical, anti-nutrients, proximate composition and the effects of Brassica oleracea varieties on hepatic and erythropoietic parameters such as liver enzymes and packed cell volume (PCV) respectively. Fresh samples of the different varieties of Brassica oleracea namely: Brassica oleracearepa(Chinese cabbage), Brassica oleracearupetris(red cabbage) and Brassica oleraceapeviridis(green cabbage) were collected from Kasa in Plateau state, Nigeria, and were identified. After the authentication of these samples, the effect of gastric inturbation (oral administration) of the aqueous extracts on Male White Albino rats was observed for 14days. Each of the three (3) varieties were analysed for proximate composition, phytochemicals and anti-nutrients. It was observed that Brassica olereceais an important source of nutrients, particularly minerals. However, the high content of anti-nutritional factors such as cyanides, tannins, oxalates and phytic acids make these minerals bio-unavailable due to the process of chelation. It was also observed that the 3 varieties could have possible effects in the reduction of packed cell volume (PCV)/ Haemoglobin (Hb) levels and in the elevation of liver enzymes activity (Alkaline phosphate, ALT and AST). One could therefore conclude that there is a change in PCV/Hb levels and liver enzymes activity of extract-fed subjects from Brassica oleraceavarieties to the control subjects from normal diet
Vitality of Physics In Nanoscience and NanotechnologyIOSR Journals
This document discusses the vital role of physics in nanoscience and nanotechnology. It explains that at the nanoscale, physics is different due to quantum effects and a high surface area to volume ratio. Properties like band structure and optical properties can be altered at the nanoscale. The document also discusses manufacturing approaches like top-down and bottom-up methods and how they apply physics principles to create nanomaterials. Finally, it notes that nanomaterials can have significantly different properties than bulk materials of the same composition due to their small size and large surface area.
1) The document discusses the effect of replacing soil with more competent granular fill materials like mixtures of 6mm metal and sand.
2) Direct shear tests were conducted on mixtures of white metal, black metal, and sand in various proportions. The tests showed that angle of internal friction increases with higher percentages of metal, with the 2:1 and 3:1 metal to sand ratios providing the best strength.
3) One dimensional compression tests were also conducted by replacing soil with the granular fill materials. The tests showed the granular materials have a much higher initial coefficient of subgrade reaction, indicating greater stiffness compared to the soils tested.
Study the effect of alpha particle fluences on the morphology and optical pro...IOSR Journals
Poly-aniline is one of the most important conducting polymers. The poly-aniline has many applications in the electronic fields such as batteries, sensors, controlling systems and organic displays. It is good environmental stability, easy conductivity control and cheap production in large quantities. In this study poly-aniline samples in nan-structure were irradiated with α- particles with different fluences (1.16 x 108- 1.20 x 109 alphas/ cm2) and constant energy (5.32±0.23 MeV). The damage is almost regular along the path length of alpha particles in poly-aniline samples. The modifications in the morphology and optical properties induced by the radiation were measured. It was found a strong correlation between absorbance and the alpha particle fluences at wavelength 600 nm for the samples after irradiations. Also, the results showed increase the number of carbon atoms per cluster in the poly-aniline samples after irradiations.
Subsurface 2D Image Analyses of the Uyangha Basement Area, South-Eastern NigeriaIOSR Journals
Geo-electric soundings were made in Stella Maris Secondary School, in Uyangha, Nigeria to image
the subsurface and obtain thicknesses and resistivities of different layers. A quantitative interpretation of the
data obtained clearly reveals the presence of four (4) geo-electric sections which are interpreted to be dry
laterite, moist laterite, weathered basement, and saturated basement. The depth probed is about 100m. The
saturated basement is the aquifer unit. Depth to aquifer unit in the area is at about 65m to 80m.The thickness of
the aquifer unit ranges from 20m to 35m. For ground water exploitation, boreholes in the area should therefore
be drilled to the depth of 91m, for reasonable groundwater yield. The lateritic layer makes the study area
suitable for building construction in the area.
Molecular characterization of pea (Pisum sativum L.) using microsatellite mar...IOSR Journals
Nineteen pea (Pisum sativum L.) accessions have been characterized using Simple Sequence Repeats (SSRs). The mains objectives of this study were to examine SSR polymorphism among cultivars and to assess genetic diversity among them. Eight microsatellites, from the Pisum microsatellite consortium (Agrogene ®, France) have been used. Five of the eight SSRs studied gave good electrophoretic profiles and helped us to amplify a number of alleles per locus varying from 3 (PSMPA5 and PSMPA6) to 13 (PSMPSAD126) with a total of 34 and an average number of 6.8 alleles per locus. The Polymorphism Information Content (PIC) varied from 0.18 for PSMPSAD134 to 0.85 for PSMPSAD126, with an average value of 0.62. The five microsatellites analyzed allowed us to separate 18 out of the 19 genotypes studied, and only the two most polymorphic markers (PSMPSAA205 and PSMPSAD126), permit to discriminate among the same genotypes (18) separated using the 5 SSRs. Genetic distances computed have been used to draw the corresponding dendrogram and to distribute genotypes according to their genetic relationship. The genotypes classified within the same group share several agro-morphological characters. Finally, the present study attests that SSR microsatellites are good tools for identifying genotypes and for the assessment of genetic diversity in pea.
Effective Waste Management and Environmental ControlIOSR Journals
There is wide spread interest in the world today in the methods that enable the re-use of waste.
According to Webster’s Mew Practical Dictionary, ‘Waste’ means “Thrown away as worthless after being used.
i.e. of no further use to a person, animal or plant; contrary to this opinion, it has been discovered that what is
regarded as waste or worthless, when worked upon can be manipulated to generated or produce materials that
are beneficial for the use of man.
This paper throw light into how waste resources can be control by analysis the theories of waste
management, recycling, re-use disposal and compositing from organic wastes and ways by which farm and
municipal waste can be worked upon to produce materials that are beneficial for the use of man
This document summarizes a research paper that proposes and compares fuzzy and Naive Bayes models for detecting obfuscated plagiarism in Marathi language texts. It first provides background on plagiarism detection and describes different types of plagiarism, including obfuscated plagiarism. It then presents the fuzzy semantic similarity model, which uses fuzzy logic rules and semantic relatedness between words to calculate similarity scores between texts. Next, it describes the Naive Bayes model for plagiarism detection using Bayes' theorem. The paper compares the performance of the fuzzy and Naive Bayes models on precision, recall, F-measure and granularity. It finds that the Naive Bayes model provides more accurate detection of obfuscated plagiar
This document presents an effective method for hiding text within an image using bit plane extraction. It discusses how a data (text) can be hidden in an image and then extracted using Matlab. Both the encryption and decryption processes are described. In encryption, the data and reference images are converted to grayscale and sliced into 8 bit planes. The 3rd bit plane of the data is then resized and replaced with the 3rd bit plane of the reference image. In decryption, the 3rd bit plane is extracted from the encrypted image and processed using morphological operations like erosion to improve readability of the extracted data. The method was tested on multiple images and provided a readable output while maintaining the cover image quality.
Analysis of Pattern Transformation Algorithms for Sensitive Knowledge Protect...IOSR Journals
The document analyzes pattern transformation algorithms for sensitive knowledge protection in data mining. It discusses:
1) Three main privacy preserving techniques - heuristic, cryptography, and reconstruction-based. The proposed algorithms use heuristic-based techniques.
2) Four proposed heuristic-based algorithms - item-based Maxcover (IMA), pattern-based Maxcover (PMA), transaction-based Maxcover (TMA), and Sensitivity Cost Sanitization (SCS) - that modify sensitive transactions to decrease support of restrictive patterns.
3) Performance improvements including parallel and incremental approaches to handle large, dynamic databases while balancing privacy and utility.
This document analyzes the capacity-based performance of optimal antenna selection for an 8x8 MIMO system. It simulates optimal antenna selection and finds that selecting 4 antennas provides channel capacity close to using all 8 antennas. The highest capacity of 44bps is achieved with 8 antennas selected. The capacity remains constant up to 18dB, 16dB and 12dB for antenna selection factors of 7, 6 and 5, respectively. For factors 1-4, capacity increases with SNR. For fading channels, 4 antenna selection authorizes channel capacity similar to using all 8 antennas in 8x8 MIMO.
This document analyzes the time complexities and accuracy of various algorithms used for depression filling in digital elevation models (DEMs). It discusses conventional algorithms like Jenson and Domingue (O(n^2) time complexity) and Planchon and Darboux (O(nlogn) time complexity on average), as well as more recent approaches like the priority-flood algorithm (O(nlogn) time complexity) and quantile classification method (thousand times faster than Jenson and Domingue). The document analyzes the performance of these algorithms based on processing time and number of DEM cell modifications required, concluding that approaches like priority-flood and quantile classification are more suitable for large, high-resolution
The document proposes a modified H-shaped microstrip patch antenna with a periodic ground structure to achieve broadband capabilities. Simulation results show the proposed antenna design offers a bandwidth of 52.43% ranging from 3.165-5.565 GHz. The antenna also exhibits stable radiation patterns and a gain of over 2 dB across the entire operating bandwidth, making it suitable for applications such as WLAN, WiMAX, and Bluetooth. Parameter sweeps were performed to optimize the antenna geometry and achieve the desired impedance bandwidth and performance.
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...IJDKP
Total column water vapor is an important factor for the weather and climate. This study apply
deep learning based multiple regression to map the TCWV with elements that can improve
spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the
fifth generation ECMWF atmospheric reanalysis of the global climate. We use an appropriate
deep learning based multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface
pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component,
10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2
metre temperature.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
The document summarizes a study that used artificial neural networks (ANN) to predict chemical oxygen demand (COD) levels in an anaerobic wastewater treatment system. Four ANN backpropagation training algorithms - Levenberg-Marquardt, gradient descent with adaptive learning, gradient descent with momentum, and resilient backpropagation - were tested on a model using COD input data. The Levenberg-Marquardt algorithm produced the best results with the lowest mean squared error of 0.533 and highest regression value of 0.991, accurately predicting COD levels. The study demonstrates ANNs can effectively model and predict values in nonlinear wastewater treatment processes.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
PERFORMANCE PREDICTION OF AN ADIABATIC SOLAR LIQUID DESICCANT REGENERATOR USI...IAEME Publication
The document describes the development and training of an artificial neural network model to predict the performance of an adiabatic packed tower regenerator using lithium bromide as a desiccant. The neural network was trained using input parameters like temperature, flow rates, and humidity ratios of air and desiccant. The output parameters used for evaluation were moisture removal rate and regenerator effectiveness. The neural network was trained using an error backpropagation algorithm and was found to accurately predict the experimental moisture removal rate and effectiveness values from tests of the regenerator, with average differences between predicted and measured values being well below 5%.
The document describes a study that used artificial neural networks (ANN) to predict chemical oxygen demand (COD) levels in wastewater from an anaerobic reactor. Four different backpropagation algorithms - Levenberg-Marquardt, gradient descent with adaptive learning rate, gradient descent with momentum, and resilient backpropagation - were used to train a three-layer feedforward ANN model. The model trained with the Levenberg-Marquardt algorithm performed best with a mean squared error of 0.533 and regression coefficient of 0.991, accurately predicting COD levels. The Levenberg-Marquardt algorithm provided the most accurate ANN model for predicting COD in effluent from the ana
This document summarizes a study that used artificial neural networks to model and identify dynamic indoor thermal comfort based on the PMV index. The study developed equations to model thermal comfort based on factors like air temperature, humidity, clothing insulation, and metabolism. An artificial neural network was then trained using these equations to approximate the nonlinear relationship between inputs like temperature and outputs like predicted mean vote. Simulation results showed the neural network model could accurately track desired thermal sensations and matched existing fuzzy logic models of human thermal comfort. The neural network approach provides a practical method for real-time identification of thermal comfort that is better than traditional manual calculations.
This document proposes a holistic approach to reconstruct data in ocean sensor networks using compression sensing. It involves two key aspects:
1) A node reordering scheme is developed to improve the sparsity of signals in the discrete cosine transform or Fourier transform domain, reducing the number of measurements needed for accurate reconstruction.
2) An improved sparse adaptive tracking algorithm is adopted to estimate the sparse degree and then reconstruct the signal in a step-by-step manner, gradually converging on an accurate reconstruction even with unknown sparsity.
Simulation results show the proposed method can effectively reduce signal sparsity and accurately reconstruct signals, especially in cases of unknown sparsity.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...ijaia
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...gerogepatton
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m
The prediction of moisture through the use of neural networks MLP typeIOSR Journals
This document describes a study that uses artificial neural networks (ANN) to predict moisture levels in the Chefchaouen region of Morocco. Specifically:
- ANNs with a multilayer perceptron (MLP) architecture were applied to predict moisture based on climatic variables like temperature, pressure, etc. collected over 1248 days.
- The optimal network structure was determined to have 4 neurons in a single hidden layer, with inputs normalized and outputs between 0-1.
- When trained on 70% of data, the ANN model achieved a correlation of 0.98 and mean squared error of 0.19%. On validation and test data, the correlation was 0.97 and error around 0.16
Neural Network Model Development with Soft Computing Techniques for Membrane ...IJECEIAES
Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
This document summarizes a study that used artificial neural networks to estimate soil moisture levels from cosmic ray sensor neutron count data. Specifically:
- Five neural networks (FFBPN, MLPN, RBFN, Elman, PNN) were tested on cosmic ray sensor data from two Australian sites to estimate soil moisture levels from the Australian Water Availability Project database.
- The Elman neural network achieved the best performance, estimating soil moisture levels with 94% accuracy for one site and 91% accuracy for the other.
- This study demonstrated that neural networks can effectively estimate continuous soil moisture levels remotely using cosmic ray sensor neutron count time series data as input.
Supervised machine learning based dynamic estimation of bulk soil moisture us...eSAT Journals
Abstract In this paper artificial neural network based sensor informatics architecture has been investigated; including proposed continuous daily estimation of area wise surface soil moisture using cosmic ray sensor’s neutron count time series. Study was conducted based on cosmic ray data available from two Australian locations. The main focus of this study was to develop a data driven approach to convert neutron counts into area wise ground surface soil moisture estimates. Independent surface soil moisture data from the Australian Water Availability Project (AWAP) was used as ground truth. A comparative study using five different types of neural networks, namely, Feed Forward Back Propagation (FFBPN), Multi-Layer Perceptron (MLPN), Radial Basis Function (RBFN), Elman (EN), and Probabilistic networks (PNN) was conducted to evaluate the overall soil moisture estimation accuracy. Best performance from the Elman network outperformed all other neural networks with 94% accuracy with 92% sensitivity and 97% specificity based on Tullochgorum data. Overall high accuracy proved the effectiveness of the Elman neural network to estimate surface soil moisture continuously using cosmic ray sensors. Index Terms: Artificial Neural Network, Surface Soil Moisture, Cosmic Ray Sensors, Neutron Counts.
Neural Networks for High Performance Time-Delay Estimation and Acoustic Sourc...cscpconf
Time-delay estimation is an essential building block of many signal processing applications.
This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or
underwater; it presents unprecedented high performance results obtained with supervised
training of neural networks which challenge the state of the art and compares its performance
to that of well-known methods such as the Generalized Cross-Correlation or Adaptive
Eigenvalue Decomposition.
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
Predict the Average Temperatures of Baghdad City by Used Artificial Neural Ne...IJERA Editor
This paper utilizes artificial neural networks (ANN) technique to improve temperature forecast performance of
Baghdad city. Our study based on Feed Forward Backpropagation Artificial Neural Networks (BPANN)
algorithm of which trained and tested by used a real world daily average temperatures of Bagdad city for ten
years past for months of January and July. Aimed at providing forecasts in a schedule, for all Days of the month
to help the meteorologist to foresee future weather temperature accurately and easily. Forecasts by ANN model
has been compared with the actual results and the realistic output (with IMOS). The results has been Compared
to the practical temperature prediction results, and shows that the BPANN forecasts have accuracy that gave
reasonably very good result and can be considered as a good method for temperature predicting..
New Microsoft PowerPoint Presentation (2).pptxpraveen kumar
The document discusses using ANN models to predict surface roughness in electrochemical machining of EN 31 tool steel. Experimental data from 31 runs using four process parameters as inputs is used to train and test different ANN architectures with LM, GDX, and SCG algorithms. The 4-5-1 architecture trained with LM has the best performance, predicting surface roughness with 96% accuracy and good generalization to new data. The developed ANN model can accurately predict surface roughness in electrochemical machining.
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
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1. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 12, Issue 2 Ver. V (Mar - Apr. 2015), PP 27-36
www.iosrjournals.org
DOI: 10.9790/1684-12252736 www.iosrjournals.org 27 | Page
Evaluation for NDWCT Performance with Different Types of
Packing Fills in Iraq Using ANN
Qasim Saleh Mahdi1
and Muwafaq Rahi Al-Hachami2
1Professor, Mechanical Engineering Dept., Al-Mustansiriyah University, Iraq.
2PhD Student, Mechanical Engineering Dept., Al-Mustansiriyah University, Iraq.
Abstract: Artificial Neural Network (ANN) is used to predicate experimental results for Natural Draft Wet
Cooling Tower (NDWCT) rig using Levenberg-Marquardt back propagation algorithm in MATLAP. The
experimental tests are done in hot and dry weather (Iraqi weather as an example). ANN results show good
agreements with experimental results where average correlation coefficient (R) for all results is (0.994),
average root mean square errors (RMSE) are (5.99, 0.91, 0.24, 0.51, 0.49, 0.2, and 5.46), and average of mean
ratio between the errors and the network output values (MRE) are (1.72%, 1.32%, 3.93%, 1.78%, 3.77%, 8.4%,
and 1.05%) for relative humidity change, tower range, water to air mass flow ratio, cooling capacity, heat
rejected to air, effectiveness, and air enthalpy change respectively.
Keywords: Cooling Tower, Packing Fill, Natural Draft, Artificial Neural Network, Back Propagation.
I. Introduction
ANN recently growing areas of artificial intelligence and it is started to be used in cooling tower area
because of it is ability to deal with many inlet and outlet parameters while their relations are linear or nonlinear.
ANN usually used as a part of Excel or MATLAP programs depend on complicity and programmers
experiences. One of the main important features of ANN is its Ability to learn. Learning or training algorithms
can be categorized into supervised training and unsupervised training. Supervised training uses pairing of both
input vector with a target vector which represents the desired output. So, this training required a teacher.
Unsupervised training is employed in self-organizing neural nets. Unsupervised training does not require a
teacher, Sivanandam, [1]. Figure (1) shows the supervised and unsupervised training. Seven learning rules are
tabulated and compared in terms of the single weight adjustment formulas, supervised versus unsupervised
learning mode, weight initialization, and required neuron activation function. Learning rules are: Delta,
Perceptron, Hebbian, Widro-Hoff, Correlation, Winner-take-all, and Outstar, Zurada, [2].
ANN is widely used in many engineering fields, in this survey will focus on its use in NDWCT only.
Gao et al., [3], experimentally studied the performance of natural draft counter-flow wet cooling in terms of
heat transfer for cases with cross-wind conditions. It is concluded experimentally that ∆T and η are influenced
by the cross-wind, and ∆T and η can decrease by 6% and 5%, respectively. When the critical (Fr) number is less
than 0.174 (wind velocity = 0.45 m/s), ∆T and η decrease with increasing cross-wind velocity, and when it is
greater than 0.174, ∆T and η increase with increasing cross-wind velocity, ANN is used in this research to
predicate experimental results. Gao et al., [4], applied and developed ANN model for prediction of thermal
performance on natural draft wet cooling towers using five, six, three nodes at input, hidden , and output layers.
The nodes were dry bulb temperature of inlet air, wet-bulb temperature, circulating water inlet temperature,
circulating water inlet mass flow rate and inlet wind velocity and output layer included circulating water outlet
temperature, temperature difference and cooling efficiency coefficient. The correlation coefficient (R) and mean
square error (MSE) are used to measure the performance of ANN model where the correlation coefficient in the
range of 0.993–0.999, and the MSE values for the ANN training and predictions were very low relative to the
range of the experiments. Jiasheng et al., [5], used artificial neural network (ANN) technique. Huge data
required for training and predication so extensive field experimental work has been carried out. Tangent sigmoid
transfer function at hidden layer used with ANN model where eleven nodes and a linear transfer function at
output layer with back-propagation (BP) training technique. The predictions have good agreement with the
experimental values with a satisfactory correlation coefficient in the range of (0.9249–0.9988), the absolute
fraction of variance in the range of (0.8753–0.9976), and the mean relative error in the range of (0.0008–0.54%).
Gao et al., [6]. Developed a three-layer back propagation (BP) network model which has one hidden layer
based on the level Froude number (Frl), and four, eight, six nodes at input, hidden, and output layer respectively.
The results were MRE and R in the range of (0.48%-3.92%) and (0.992-0.999), respectively, and RMSE values
for the ANN training and predictions were very low relative to the range of the experiments.
In this research, Levenberg-Marquardt back propagation used to predicate NDWCT experimental
results which are validated firstly by direct comparison and secondly using R, MRE, and RMSE. A software
package for Artificial Neural Network using MATLAP is used to exam experimental results with Delta learning
2. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 28 | Page
rule. ANN structure including (I×H×O) which represent input, hidden, and output layer respectively. Eight
neurons are used as input and seven neurons as output where hidden neurons are varied according different
theories
II. Experimental and Artificial Neural Network
[7], describes experimental work and results recorded from experimental test rig which is shown in
figure (2) to evaluate and compare three types of packing fills namely splash, honey cell, and trickle fill where
results show that trickle fill has better heat performance that other fills.
Levenberg-Marquardt back propagation algorithm is used to predicate experimental results because it
has better convergence properties than the conventional back propagation method but required higher storage
capacity. ANN with three layers constructed from eight, ten, and seven neurons at input, output, and hidden
layers respectively as shown in figures (3) and (4). The total operation will early stop if error reaches to (1*10-
7) or it will continue till (1000 iterations) which represent an optimum number using Levenberg-Marquardt back
propagation algorithm. Data divided automatically and randomly into (70%) for training ANN, (15%) for
validation, and the rest (15%) for testing samples. Working by ANN required many tests and huge number of
data to achieve network training. Huge experimental results are recorded from rig by changing water mass flow
rate six times, cross wind velocity five times, [8], three type of fills, and four different thicknesses. These results
are divided as followed.
1 Data collected for different thickness of honey cell fill to study increasing water flow rate effects.
2 Data collected for different thickness of honey cell fill to study effect of cross wind velocity.
3 Data collected for different thickness of splash fill to study increasing water flow rate effects.
4 Data collected for different thickness of splash fill to study effect of cross wind velocity.
5 Data collected for (5) cm thickness of honey cell, splash, and trickle fills to study increasing water flow rate
effects.
6 Data collected for (5) cm thickness of honey cell, splash, and trickle fills to study effect of cross wind
velocity.
7 Data collected for (10) cm thickness of honey cell, splash, and trickle fills to study increasing water flow
rate effects.
8 Data collected for (10) cm thickness of honey cell, splash, and trickle fills to study effect of cross wind
velocity.
Validation of all results can be shown by comparison between experimental results and predicated results
using ANN by direct comparison between exact and predicated results or by checking R, MRE, RMSE values
where exact solution if (R=1) and good agreeing if approach to (1) while approaching to (-1) means that results
are not valid, The less MRE is the better fit predicted results are, and (RMSE) better fit when its value
approaches to zero. First path needs to draw experimental with predicated results together as in figures (5) to
(24). Second path can give all results together in one package as in figure (25).
III. Results validation
Number of hidden nodes in hidden layer are determined by many theories mentioned in detail at [2]
which include different theories submitted by Hecht-Nielson, [9], Xin, [10], Ding, [11], Xie, [12], Yao and
Wang, [13]. Applying these theories, a range of hidden numbers are found so eight to fifteen nodes are tested to
find the best suitable number. Table (1) list correlation coefficient (R) values using different number of hidden
nodes used to predicate (120) experimental results for honey cell type when water flow is changed from (0.8) to
(2.4) gpm and it is found that best value is (Rall =0.99653) when 10 hidden nodes are used. Up on that ten
hidden nodes will be used to predicate all results in this research.
Validation of predicated and experimental results will be determined by the use of MRE, RMSE, and R where:
N
i i
ii
a
ba
N
MRE
1
100
1
(%)
MRE, shows the mean ratio between the error and the network output values, Hosoz et al., [14].
(ai and bi) represent experimental and network output values, respectively. (N) Represents the sample number.
N
i
ii ba
N
RMSE
1
2
)(
1
Root mean square error (RMSE), better fit when its value approaches to zero. Gao et al., [1, 2].
b)a).cov(b,cov(a,
b)cov(a,
R
…………….. (1)
…………….. (2)
…………….. (3)
3. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 29 | Page
IV. Results predication using ANN
Eight inputs are used {water flow rate (Liter/min), air inlet temperature (˚C), inlet air relative humidity
(%), inlet water temperature (˚C), fill thickness (cm), wind velocity (m/s), air velocity at outlet (m/s), and
pressure difference (mm water)} and seven outlets {change in relative humidity (%), range (˚C), water to air
mass flow rate ratio, cooling capacity (kW), heat transfer to air (kW), effectiveness (%), and air enthalpy change
(kJ/kg)}.
Table (2) list RMSE and MRE for each case as mentioned before. Table shows that maximum RMSE
between predicated and experimental results are (15.25, 4.203, 0.475, 1.508, 0.801, 0.458, and 9.663) found at
cases (5, 5, 1, 5, 4, 8, and 3) and minimum values are (2.288, 0.0183, 0.002, 0.014, 0.272, 0.016, and 0.846)
found at cases (2, 7, 2, 7, 3, 3, and 5) for relative humidity change, tower range, water to air mass flow rate ratio,
cooling capacity, heat rejected to air, effectiveness, and enthalpy change respectively. Maximum and minimum
values of MRE as followed (8.010%, 5.953%, 22.642%, 5.687%, 11.503%, 28.714% and 1.847%) for cases (5,
5, 4, 5, 8, 8, and 3) and (0.235%, 0.005%, 0.108%, 0.457%, 0.332%, 0.352%, and 0.320%) for cases (1, 8, 2, 6,
3, 3, and 5) respectively. Correlation coefficient (R) Values for 8 cases are listed in table (3) where (Rall) values
(all means total predicated results for each case including results used for training, validation, and test) are
(0.99653, 0.99562, 0.99397, 0.99548, 0.99201, 0.994, 0.9907, and 0.99445). Remembering that R approach to
(+1) means agreed relation between predicated and experimental results where (R) approach to (-1) means
reverse relation and R approach to zero mean no relation between them. Better results (R= 0.99653) found in
case one and less one is found at case seven (R=0.9907). Figure (25) shows relations between experimental
(target) and predicated (output) results using ANN for eight cases. Best relation between experimental and
predicated results are listed in table (4).
V. Conclusions
1 Using Artificial Neural Network shows a very good matching with experimental results which can be
expressed by direct comparison or using R, MRE, and RMSE.
2 Best R, MRE, and RMSE are (0.99653, 0.005%, and 0.002) recorded at cases 1, 8, and 2 respectively.
3 ANN can be used to find best fit relation for predicated (output) and experimental (target) results, the best
relation is (output= target+0.019) found at case (8).
References
[1]. Sivanandam, S.N. Paulraj,M, ״Introduction to Artificial Neural Networks .״ Vikas Publishing House PVT Ltd, 2003.
[2]. Zurada J., “Introduction to Artificial Neural Systems”, West Publishing Company, 1996.
[3]. Gao M., Sun F. , Wang K., Shi Y. and Zhao Y., “Experimental Research of Heat Transfer Performance on Natural Draft Counter
Flow Wet Cooling Tower under Cross-Wind Conditions”, International Journal of Thermal Sciences, vol.47, pp. 935–941, 2008.
[4]. Gao M., Sun F., Wang K., Shi Y. and Zhao Y., “Performance Prediction of Wet Cooling Tower Using Artificial Neural Network
Under Cross-Wind Conditions”, International Journal of Thermal Sciences, vol.48, pp.583–589, 2009.
[5]. Jiasheng Wu, Guoqiang Zhang, Quan Zhang, Jin Zhou, Yu Wang, “Artificial neural network analysis of the performance
characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter”, Journal of
Energy and Buildings vol.43, pp.1685–1693, 2011.
[6]. Gao Ming, Yue-tao Shi , Ni-ni Wang, Yuan-bin Zhao, Feng-zhong Sun, “Artificial neural network model research on effects of
cross-wind to performance parameters of wet cooling tower based on level Froude number”, Journal of Applied Thermal
Engineering, vol.51, pp. 1226-1234, 2013.
[7]. Qasim Saleh Mahdi and Muwafaq Rahi Al- Hachami, “Experimental Evaluation for NDWCT Performance Using Different Types
of Packing Fills in Iraq”, IPASJ International Journal of Mechanical Engineering (IIJME), Volume 3, Issue 3, pp. 1-10, 2015.
[8]. Qasim Saleh Mahdi and Muwafaq Rahi Al- Hachami, “Experimental Analyses for NDWCT Performance Using Trickle Fill Under
the Effect of Cross Wind”, International Journal Of Scientific Research And Education, Volume 3, Issue 3, pp 2969-2977, 2015.
[9]. Hecht R. - Nielson, “Theory of back propagation neural networks”, Proceedings of the International Joint Conference on Neural
Networks. Washington vol.1, pp.593–605, 1989.
[10]. Xin F., “Basic Theory and Method of Neural Net Intelligence”. Chengdu, Southwest Jiaotong University Press, 2000.
[11]. Ding E., “Air Cooling Techniques in Power Plants, Water and Electric Power Press, Beijing, 1992.
[12]. Xie Q.S., “Neural Net Method in Mechanical Engineering”, China Machine Press, Beijing, 2003.
[13]. Yao Y.B. and J.L. Wang, “Research on raising BP network training speed”, Information Technology 1, pp. 4–6, 2002.
[14]. Hosoz M., Ertunc H.M., Bulgurcu H., “Performance prediction of a cooling tower using artificial neural network”, Energy
Conversion and Management, vol. 48, pp.1349-1359, 2007.
[15]. Qasim Saleh Mahdi and Muwafaq Rahi Al- Hachami, “Performance Comparison for NDWCT Using Trickle Fill at Different
Weather Conditions”, International Journal of Engineering Trends and Technology, Volume 19 Number 3 – Jan 2015, pp 134-139.
4. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 30 | Page
Table (1) List regressions values using different hidden number of nodes to predicate results using honey cell
fill.
No. of hidden nodes
R
Training
R
Validation
R
Tests
R
All
8 0.99397 0.99021 0.98514 0.99179
9 0.99687 0.98871 0.98682 0.99412
10 0.99866 0.9923 0.9896 0.99653
11 0.99775 0.99442 0.99269 0.99626
12 0.9989 0.99199 0.99014 0.99592
13 0.99333 0.98677 0.98906 0.99151
14 0.99529 0.99058 0.99457 0.99379
15 0.99622 0.99296 0.99461 0.99533
Table (2) List RMSE and MRE for eight cases and seven parameters using ANN
Parameter type ∆RH
%
∆ Tw
˚C aw mm
/
∆ Qw
kW
∆Qa
kW
η
%
∆i
kJ/kg
Result type
1
RMSE 4.179 0.021 0.475 0.255 0.317 0.178 4.760
MRE 0.235% 0.466% 1.723% 1.958% 2.890% 5.401% 0.517%
2
RMSE 2.289 1.149 0.002 0.693 0.747 0.038 6.930
MRE 0.365% 1.424% 0.108% 2.276% 2.137% 7.850% 1.720%
3
RMSE 11.400 0.120 0.078 0.354 0.272 0.016 9.664
MRE 2.198% 0.024% 0.567% 0.629% 0.332% 0.352% 1.847%
4
RMSE 5.458 1.543 0.346 0.137 0.801 0.162 8.703
MRE 1.227% 2.341% 22.64% 1.382% 7.181% 5.820% 1.652%
5
RMSE 15.256 4.203 0.007 1.508 0.741 0.394 0.846
MRE 8.010% 5.953% 0.358% 5.687% 2.869% 3.809% 0.320%
6
RMSE 2.395 0.055 0.423 0.524 0.406 0.214 2.356
MRE 0.666% 0.320% 2.362% 0.457% 0.457% 2.060% 0.475%
7
RMSE 3.459 0.018 0.402 0.014 0.311 0.173 3.816
MRE 0.794% 0.023% 2.393% 1.159% 2.806% 13.15% 1.140%
8
RMSE 3.487 0.152 0.193 0.605 0.344 0.458 6.604
MRE 0.274% 0.005% 1.310% 0.706% 11.50% 28.71% 0.710%
Table (3) List correlation coefficient values for eight cases.
Correlation
coefficient R
Training
R
Validation
R
Tests
R
All
Case number
1 0.99866 0.9923 0.9896 0.99653
2 0.99853 0.99221 0.98705 0.99562
3 0.99782 0.99153 0.9771 0.99397
4 0.99642 0.99177 0.99597 0.99548
5 0.9918 0.99665 0.98836 0.99201
6 0.99823 0.99378 0.97748 0.994
7 0.99916 0.99446 0.94695 0.9907
8 0.9969 0.99469 0.98963 0.99445
Table (4) List best relation between experimental and predicated values
Case number Fit relation for all results
1 Output= 0.98*target+0.12
2 Output= 0.99*target+0.16
3 Output= 0.97*target+0.24
4 Output= 0.98*target+0.078
5 Output= 0.94*target+0.32
6 Output= 0.99*target+0.18
7 Output= 0.99*target+0.28
8 Output= target+0.019
5. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 31 | Page
Fig. (1) Block diagram of basic learning modes, [6]. Fig. (2) Experimental rig, [15].
Fig. (3) Schematic diagram for sigmoid hidden neurons
and linear output neurons using 10, 7, and 8 neurons at
input, hidden, and output layers respectively.
Fig. (4) Structure of ANN used to model
experimental tests.
Fig. (5) Tower range due to water mass flow rate
change for different fill types, (u=0 m/s),
(experimental and ANN).
Fig. (6) Tower range due to water mass flow rate
change for different fill types, (u=0.4 m/s),
(experimental and ANN).
6. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 32 | Page
Fig. (10) Effectiveness due to water mass flow rate
change for different fill types, (u=0 m/s),
(experimental and ANN).
Fig. (11) Effectiveness due to water mass flow rate
change for different fill types, (u=0.4 m/s),
(experimental and ANN).
Fig. (7) Tower range due to water mass flow rate
change for different fill types, (u=0.6 m/s),
(experimental and ANN).
Fig. (8) Tower range due to water mass flow rate
change for different fill types, (u=0.8 m/s),
(experimental and ANN).
Fig. (9) Tower range due to water mass flow rate change for
different fill types, (u=1 m/s), (experimental and ANN).
7. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 33 | Page
Fig. (14) Effectiveness due to water mass flow rate change for different
fill types, (u=1 m/s), (experimental and ANN).
Fig. (12) Effectiveness due to water mass flow rate
change for different fill types, (u=0.6 m/s),
(experimental and ANN).
Fig. (13) Effectiveness due to water mass flow
rate change for different fill types, (u=0.8 m/s),
(experimental and ANN).
Fig. (18) Air relative humidity change due to water
mass flow rate change for different fill types, (u=0.8
m/s), (experimental and ANN).
Fig. (15) Air relative humidity change due to water
mass flow rate change for different fill types, (u=0
m/s), (experimental and ANN).
Fig. (16) Air relative humidity change due to water
mass flow rate change for different fill types, (u=0.4
m/s), (experimental and ANN).
Fig. (17) Air relative humidity change due to water
mass flow rate change for different fill types, (u=0.6
m/s), (experimental and ANN).
8. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 34 | Page
Fig. (19) Air relative humidity change due to water mass flow rate change for
different fill types, (u=1 m/s), (experimental and ANN).
Fig. (24) Cooling capacity due to water mass flow rate change for
different fill types, (u=1 m/s), (experimental and ANN).
Fig. (22) Cooling capacity due to water mass flow
rate change for different fill types, (u=0.6 m/s),
(experimental and ANN).
Fig. (23) Cooling capacity due to water mass flow
rate change for different fill types, (u=0.8 m/s),
(experimental and ANN).
Fig. (20) Cooling capacity due to water mass flow
rate change for different fill types, (u=0 m/s),
(experimental and ANN).
Fig. (21) Cooling capacity due to water mass flow
rate change for different fill types, (u=0.4 m/s),
(experimental and ANN).
9. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 35 | Page
Case (1). Case (2).
Case (3). Case (4).
10. Evaluation for NDWCT Performance with Different Types of Packing Fills in Iraq Using ANN
DOI: 10.9790/1684-12252736 www.iosrjournals.org 36 | Page
Case (5). Case (6).
Case (7). Case (8).
Figure (25) Experimental (target) and predicated (output) results using ANN for eight cases.