scholarly journals Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

2016 ◽  
Vol 20 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Hassan Pouraria ◽  
Seyed Kia ◽  
Warn-Gyu Park ◽  
Bahman Mehdizadeh
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Vortex Tube ◽  
Temperature Drop ◽  
Cooling Performance ◽  
Artificial Neural

In this study, artificial neural networks (ANNs) have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D), the ratio of the cold outlet diameter to the tube diameter(d/D), inlet pressure(P), and cold mass fraction (Y) on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA) has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y). The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data

2018 ◽  
Vol 233 (7) ◽  
pp. 2549-2569
Author(s):  
Farrukh Mazhar ◽  
Mohammad A Choudhry ◽  
Muhammad Shehryar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flight Test ◽  
Aerial Vehicle ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Trained Neural Network

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

Artificial Neural Network Applied to Predicting the Surface Tension of Acoustically Levitated Droplets of Supercooling Nanofluids

NANO ◽  
2021 ◽  
pp. 2150108
Author(s):  
Baohui Wu ◽  
Yudong LIU ◽  
Dengshi Wang ◽  
Nan Jiang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Surface Tension ◽  
Artificial Neural Network ◽  
Graphene Oxide ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Deionized Water ◽  
Artificial Neural

Droplet oscillation method is a noncontact experimental approach, which can be used to measure the surface tension of acoustically levitated droplet. In this paper, we obtained huge amounts of experimental data of deionized water and water-based graphene oxide nanofluids within the temperature range of [Formula: see text]8.2–[Formula: see text]C. Based on the experimental data, we analyzed the influence of droplet’s deformation and frequency shift phenomenon on the surface tension of levitated droplet. Eight parameters that strongly correlate with surface tension were found and used as input neurons of artificial neural network model to predict the surface tension of supercooling graphene oxide nanofluids. The experimental data of nonsupercooling graphene oxide nanofluids were used as training set to optimize artificial neural network model, and that of deionized water were served as validation set, which was used to verify the predictive ability of artificial neural network model. The root mean square error of the optimized artificial neural network model to validation set is only 0.2558[Formula: see text]mN/m, and the prediction values of the surface tension of supercooling deionized water were in good agreement with the theoretical values calculated by Vargaftik equation, which indicates that artificial neural network model can deal well with the complex nonlinear relationship. Afterwards, we successfully predicted the surface tension of supercooling nanofluids by means of the optimized artificial neural network model and obviously reduced the dispersion and deviation caused by droplet deformation and other problems during oscillation process.

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