scholarly journals Forecasting of Wind Induced Pressure on Setback Building Using Artificial Neural Network

2020 ◽  
Author(s):  
Amlan Kumar Bairagi ◽  
Sujit Kumar Dalui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cfd Simulation ◽  
Incidence Angle ◽  
Pressure Coefficient ◽  
Tall Building ◽  
The Face ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Target Data

The wind load on an irregular plan shape tall building is quite different compared to a conventional plan shape tall building. Especially the aerodynamic parameters have extreme change due to the variety of setbacks at one or more the disparity of level. This paper highlights the prediction of pressure coefficient on square, single (20 %) setback and double (10 %) setback buildings for any wind incidence angle by CFD simulation and validated with Artificial Neural Network (ANN) and fast Fourier transform. The ANN is a widely used and efficient tool for different types of analyses. The 0° to 180° wind incidence angles (WIAs) considered as input data and respective face wise pressure coefficient (Cp) used as target data. The Levenberg-Marquardt training function and Mean Square Error (MSE) performance function used to train the target data. The face wise graphs of CFD, ANN and FFT are plotted in a single graph and the Cp of the surface checked by any random WIAs. Amazingly, the Cp of random WIA by ANN is almost similar to CFD. Furthermore, the error of ANN is 0.6 % to 2.5 %, which is negligible. According to this predicted graph, the design Cp of any WIA can be easily calculated and implement directly in the design.

An Artificial Neural Network Approach to Cooling Analysis of Electronic Components in Enclosures Filled With Nanofluids

2011 ◽  
Vol 133 (1) ◽  
Author(s):  
A. Kargar ◽  
B. Ghasemi ◽  
S. M. Aminossadati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cfd Simulation ◽  
Electronic Components ◽  
Neural Network Approach ◽  
Cooling Performance ◽  
Computational Fluid Dynamics Cfd ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Lower Temperature

Computational fluid dynamics (CFD) and artificial neural network (ANN) are used to examine the cooling performance of two electronic components in an enclosure filled with a Cu-water nanofluid. The heat transfer within the enclosure is due to laminar natural convection between the heated electronic components mounted on the left and right vertical walls with a relatively lower temperature. The results of a CFD simulation are used to train and validate a series of ANN architectures, which are developed to quickly and accurately carry out this analysis. A comparison study between the results from the CFD simulation and the ANN analysis indicates that the ANN accurately predicts the cooling performance of electronic components within the given range of data.

PREDICTION OF PRESSURE COEFFICIENT ON SETBACK BUILDING BY ARTIFICIAL NEURAL NETWORK

2020 ◽  
Author(s):  
Amlan Kumar Bairagi ◽  
Sujit Kumar Dalui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Coefficient ◽  
Aerodynamic Coefficients ◽  
Building Models ◽  
Single Side ◽  
Artificial Neural ◽  
Drag And Lift ◽  
Artificial Neural Network Ann ◽  
Cfd Method

The present study predicted the pressure (Cp), drag (Cfy), and lift (Cfx) coefficients on square shape and setback building models. The study considered a conventional (1:1:2) square model, a single side single-setback, and single side double-setback models. It is very much challenging to measure the different aerodynamic coefficients on the setback building models for the intermediate wind incidence angles (WIAs). At first, the study calculated the different aerodynamic coefficients by Computational Fluid Dynamics (CFD) method and then predicted the Cp of intermediate wind angles by the artificial neural network (ANN) method. The Cp for different WIAs directly exports from the Cp versus WIAs graph. The study found the double setback model is 4.26% and 0.6% more efficient to resist the drag and lift force compared to the single setback building. Finally, suggested the setback number takes an important role to control the frequency due to pressure and velocity.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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

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

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

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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