Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation

2014 ◽

Vol 493 ◽

pp. 74-79

Author(s):

Y.A. Sabtalistia ◽

S.N.N. Ekasiwi ◽

B. Iskandriawan

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Energy Consumption ◽

Thermal Comfort ◽

Energy Efficient ◽

Air Conditioning ◽

Air Conditioning System ◽

Air Supply ◽

Air Conditioning Systems ◽

Air Diffusion

Energy consumption for air conditioning systems (air conditioning system) increased along with the increasing need for fresh air and comfortable in the room especially apartments. FAC system (Floor Air Conditioning) is growing because it is more energy efficient than CAC (Ceiling Air Conditioning) system. However, the position of the AC supply is on the lower level at the FAC system causes draft discomfort becomes greater as air supply closer to the occupants so that thermal comfort can be reduced. Heat mixture of windows, exterior walls, kitchen, and occupants in the studio apartment affect thermal comfort in the room too.This study aims to determine the position of the AC supply which has the best thermal comfort of FAC system in the studio apartment. It can be done by analyzing ADPI (Air Diffusion Performance Index), the distribution of air temperature, wind speed, RH (Relative Humidity), and DR (Draft Risk) to change the position of the AC supply supported by CFD (Computational Fluid Dynamics) simulation.This result prove that AC position 2 (on wall near the kitchen) is more comfortable than AC position 1 (on the bathroom wall) because AC position 2 away from occupied areas, thereby reducing the occurrence of draught discomfort.

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Energy efficient design of high depth raceway pond using computational fluid dynamics

Renewable Energy ◽

10.1016/j.renene.2018.10.016 ◽

2019 ◽

Vol 133 ◽

pp. 528-537 ◽

Cited By ~ 5

Author(s):

S.S. Sawant ◽

S.N. Gosavi ◽

H.P. Khadamkar ◽

C.S. Mathpati ◽

Reena Pandit ◽

...

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Energy Efficient ◽

Efficient Design ◽

Raceway Pond ◽

Energy Efficient Design ◽

High Depth

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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

Heat Transfer-Asian Research ◽

10.1002/htj.21425 ◽

2019 ◽

Vol 48 (4) ◽

pp. 1151-1179 ◽

Cited By ~ 1

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

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

Indoor and Built Environment ◽

10.1177/1420326x15609772 ◽

2016 ◽

Vol 26 (3) ◽

pp. 347-354 ◽

Cited By ~ 7

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.

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

Volume 8B: Heat Transfer and Thermal Engineering ◽

10.1115/imece2018-88507 ◽

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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Characterization of Single-Phase Flow Hydrodynamics in Berty Reactor using Computational Fluid Dynamics (CFD)

Reaction Chemistry & Engineering ◽

10.1039/d1re00390a ◽

2021 ◽

Author(s):

Khunnawat Ountaksinkul ◽

Sirada Sripinun ◽

Panut Bumphenkiattikul ◽

Surapon Bubphacharoen ◽

Arthit Vongachariya ◽

...

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Single Phase ◽

Three Dimensional ◽

Flow Characteristics ◽

Kinetic Studies ◽

Stirred Tank Reactors ◽

Computational Fluid Dynamics Cfd ◽

Continuous Stirred Tank

This work studies the flow characteristics in the Berty reactor, a gradientless reactor for kinetic studies, using three-dimensional (3D) computational fluid dynamics (CFD), and the non-ideal continuous stirred tank reactors...

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