scholarly journals A COMBINATION OF COMPUTATIONAL FLUID DYNAMICS, ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTORS MACHINES MODEL TO PREDICT FLOW VARIABLES IN CURVED CHANNEL

2017 ◽  
Vol 0 (0) ◽  
pp. 0-0 ◽  
Author(s):  
Azadeh Gholami ◽  
Hossein Bonakdari ◽  
Ali Akbar Akhtari ◽  
Isa Ebtehaj
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Curved Channel ◽  
Support Vectors ◽  
Artificial Neural ◽  
Flow Variables

The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment

2016 ◽  
Vol 26 (3) ◽  
pp. 347-354 ◽  
Author(s):  
Tian-hu Zhang ◽  
Xue-yi You
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
The Self ◽  
Inverse Design ◽  
Artificial Neural ◽  
The Impact

The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.

Supply Air Temperature Prediction in an Air-Handling Unit Using Artificial Neural Network

2018 ◽  
Author(s):  
Nikita Sukthankar ◽  
Abhishek Walekar ◽  
Dereje Agonafer
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Data Center ◽  
Operating Conditions ◽  
Cfd Analysis ◽  
Ann Model ◽  
Air Handling Unit

Continuous provision of quality supply air to data center’s IT pod room is a key parameter in ensuring effective data center operation without any down time. Due to number of possible operating conditions and non-linear relations between operating parameters make the working mechanism of data center difficult to optimize energy use. At present industries are using computational fluid dynamics (CFD) to simulate thermal behaviour for all types of operating conditions. The focus of this study is to predict Supply Air Temperature using Artificial Neural Network (ANN) which can overcome limitations of CFD such as high cost, need of an expertise and large computation time. For developing ANN, input parameters, number of neurons and hidden layers, activation function and the period of training data set were studied. A commercial CFD software package 6sigma room is used to develop a modular data center consisting of an IT pod room and an air-handling unit. CFD analysis is carried out for different outside air conditions. Historical weather data of 1 year was considered as an input for CFD analysis. The ANN model is “trained” using data generated from these CFD results. The predictions of ANN model and the results of CFD analysis for a set of example scenarios were compared to measure the agreement between the two. The results show that the prediction of ANN model is much faster than full computational fluid dynamics simulations with good prediction accuracy. This demonstrates that ANN is an effective way for predicting the performance of an air handling unit.

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