Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics-artificial neural network hybrid method

2019 ◽  
Vol 48 (4) ◽  
pp. 1151-1179 ◽  
Author(s):  
Mojtaba Sepehrnia ◽  
Ghanbarali Sheikhzadeh ◽  
Golnoush Abaei ◽  
Mahdi Motamedian
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Natural Convection ◽  
Flow Field ◽  
Entropy Generation ◽  
Hybrid Method ◽  
Turbulent Natural Convection

Computational fluid dynamics and experimental study of turbulent natural convection with surface thermal radiation in a cubic enclosure

2020 ◽  
Vol 31 (05) ◽  
pp. 2050065
Author(s):  
J. M. A. Navarro ◽  
J. F. Hinojosa ◽  
I. Hernández-López
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Experimental Data ◽  
Experimental Study ◽  
Computational Fluid Dynamics ◽  
Natural Convection ◽  
Thermal Radiation ◽  
Turbulence Models ◽  
Numerical Results ◽  
Turbulent Natural Convection

This paper reports a computational fluid dynamics and experimental study to analyze the effect of surface thermal radiation on the turbulent natural convection in a closed cubic cavity. Experimental and numerical results are compared for low and high wall emissivities. Experimental temperature profiles were obtained at six different depths and heights consisting of 14 thermocouples each. Several turbulence models were evaluated against experimental data. It was found that renormalized [Formula: see text]-[Formula: see text] and standard [Formula: see text]-[Formula: see text] turbulence models present the best agreement with the experimental data for emissivities of walls of 0.98 and 0.03, respectively. Thus, the numerical results of temperature fields and flow patterns were obtained with these models. From the results, it was found that the effect of thermal radiation on experimental heat transfer coefficients is significantly, increased between 48.7% ([Formula: see text]) and 50.16% ([Formula: see text]), when the emissivity of the walls increases from 0.03 to 0.98. Therefore, the radiative exchange should not be neglected in heat transfer calculations in cubic enclosures, even if the temperature difference between heated wall and cold wall is relatively small (between 15 and 30[Formula: see text]K).

Entropy Generation in Laminar and Turbulent Natural Convection Heat Transfer From Vertical Cylinder With Annular Fins

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Jnana Ranjan Senapati ◽  
Sukanta Kumar Dash ◽  
Subhransu Roy
Keyword(s):  
Heat Transfer ◽  
Natural Convection ◽  
Entropy Generation ◽  
Vertical Cylinder ◽  
Turbulent Regime ◽  
Maximum Heat ◽  
Turbulent Natural Convection ◽  
Maximum Heat Transfer ◽  
Wide Range ◽  
Annular Fins

Entropy generation due to natural convection has been calculated for a wide range of Rayleigh number (Ra) in both laminar (104 ≤ Ra ≤ 108) and turbulent (1010 ≤ Ra ≤ 1012) flow regimes, for diameter ratio of 2 ≤ D/d ≤ 5, for an isothermal vertical cylinder fitted with annular fins. In the laminar regime, the entropy generation was predominantly caused by heat transfer (conduction and convection) and the viscous contribution was negligible with respect to heat transfer. But in the turbulent regime, entropy generation due to fluid friction is significant enough although heat transfer entropy generation is still dominant. The results demonstrate that the degree of irreversibility is higher in case of finned configuration when compared with unfinned one. With the deployment of a merit function combining the first and second laws of thermodynamics, we have tried to delineate the thermodynamic performance of finned cylinder with natural convection. So, we have defined the ratio (I/Q)finned/(I/Q)unfinned. The ratio (I/Q)finned/(I/Q)unfinned gets its minimum value at optimum fin spacing where maximum heat transfer occurs in turbulent flow, whereas in laminar flow the ratio (I/Q)finned/(I/Q)unfinned decreases continuously with the increase in number of fins.

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.

Sign in / Sign up

Export Citation Format

Share Document