Experimental and Artificial Neural Network (Ann) Simulation of a Solar Cavity Collector

Solar cavity collector (SCC) is an improvised version of flat plate solar collector (FPC). A SCC of outer radius 16mm positioned concentrically and placed in a 50 mm metal box. Five numbers of such cavities with a provision of inlet and outlet water pipes has been fabricated and experimented for its optimal performance. This experimental gadget is used to heat the water. As the physical dimensions of solar cavity collector influence the performances of the cavity collector, it includes the comparison of 5 numbers of cavities and 7 numbers of cavities, effect of aperture entry have been taken as investigation parameters in the present study. Inclination angle of the collector, water mass flow rates and mode of flow are the other parameters taken for the present study. Experimental data are trained and tested using Artificial Neural Network (ANN) tool of MATLAB software and ANN simulation results have been validated and verified with the available experimental data. Simulations for other set of variables have been predicted with the developed ANN mode

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A Prediction Model of Deflection of Fixing Thin Parts

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.217-219.1526 ◽

2012 ◽

Vol 217-219 ◽

pp. 1526-1529

Author(s):

Yu Mei Liu ◽

Wen Ping Liu ◽

Zhao Liang Jiang ◽

Zhi Li

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Prediction Model ◽

Simulated Data ◽

Artificial Neural ◽

Predicted Values ◽

Artificial Neural Network Ann ◽

Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

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

Journal of Nanotechnology in Engineering and Medicine ◽

10.1115/1.4003500 ◽

2011 ◽

Vol 2 (1) ◽

Cited By ~ 5

Author(s):

Hadi Salehi ◽

Mosayyeb Amiri ◽

Morteza Esfandyari

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Energy Gain ◽

Operating Conditions ◽

Maximum Relative Error ◽

Ann Model ◽

Wide Range ◽

Artificial Neural ◽

Artificial Neural Network Ann

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

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Inverse Analysis of Material Parameters of Multiple Foam Layers Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.3313 ◽

2011 ◽

Vol 189-193 ◽

pp. 3313-3316

Author(s):

Xiao Xiang Su ◽

Yao Dong Gu ◽

J.P. Finlay ◽

I.D. Jenkinson ◽

Xue Jun Ren

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Nonlinear Material ◽

Fe Model ◽

Ann Model ◽

Nonlinear Parameters ◽

Closed Cell ◽

Artificial Neural ◽

Artificial Neural Network Ann

Closed cell polymeric foams are widely used in sport and medical equipments. In this study, an artificial neural network (ANN) based inverse finite element (FE) program has been developed and used to predict the nonlinear material properties of EVA foams with multiple layers. A 2-D parametric FE model was developed and validated against experimental data. Systematic data from FE simulations was used to train and validate the ANN model. The accuracy and validity of the ANN method were assessed based on both blind tests and experimental data. Results showed that the proposed artificial neural network model is robust and efficient in predicating the nonlinear parameters of foam materials.

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The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs

Petroleum Science and Technology ◽

10.1080/10916466.2010.519749 ◽

2012 ◽

Vol 30 (23) ◽

pp. 2450-2459 ◽

Cited By ~ 3

Author(s):

A. Khaksar Manshad ◽

M. Khaksar Manshad ◽

S. Ashoori

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Genetic Programming ◽

Crude Oil ◽

Oil Reservoirs ◽

Permeability Reduction ◽

Asphaltene Precipitation ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Developing optimal artificial neural network (ANN) to predict the specific heat of water based yttrium oxide (Y2O3) nano?uid according to the experimental data and proposing

Heat Transfer Research ◽

10.1615/heattransres.2020034724 ◽

2020 ◽

Author(s):

Andaç Batur Çolak

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Specific Heat ◽

Yttrium Oxide ◽

Water Based ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network

Materials ◽

10.3390/ma12111752 ◽

2019 ◽

Vol 12 (11) ◽

pp. 1752 ◽

Cited By ~ 3

Author(s):

Hasan Sh. Majdi ◽

Amir N. Saud ◽

Safaa N. Saud

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Physical Properties ◽

Chemical Properties ◽

Strongly Correlated ◽

Catalyst Carrier ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Yeast Concentration

Porous γ-alumina is widely used as a catalyst carrier due to its chemical properties. These properties are strongly correlated with the physical properties of the material, such as porosity, density, shrinkage, and surface area. This study presents a technique that is less time consuming than other techniques to predict the values of the above-mentioned physical properties of porous γ-alumina via an artificial neural network (ANN) numerical model. The experimental data that was implemented was determined based on 30 samples that varied in terms of sintering temperature, yeast concentration, and socking time. Of the 30 experimental samples, 25 samples were used for training purposes, while the other five samples were used for the execution of the experimental procedure. The results showed that the prediction and experimental data were in good agreement, and it was concluded that the proposed model is proficient at providing high accuracy estimation data derived from any complex analytical equation.

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Modeling of a natural lipstick formulation using an artificial neural network

RSC Advances ◽

10.1039/c5ra12749a ◽

2015 ◽

Vol 5 (84) ◽

pp. 68632-68638 ◽

Cited By ~ 2

Author(s):

Norsuhaili Kamairudin ◽

Siti Salwa Abd Gani ◽

Hamid Reza Fard Masoumi ◽

Mahiran Basri ◽

Puziah Hashim ◽

...

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Melting Point ◽

Experimental Designs ◽

Artificial Neural ◽

Artificial Neural Network Ann

An artificial neural network (ANN) was applied in conjunction with experimental data from a mixture of experimental designs to predict the melting point of a lipstick formulation.

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