scholarly journals Turbulent Kinetic Energy Distribution of Nutrient Solution Flow in NFT Hydroponic Systems Using Computational Fluid Dynamics

2019 ◽  
Vol 1 (2) ◽  
pp. 283-290
Author(s):  
Cesar H. Guzmán-Valdivia ◽  
Jorge Talavera-Otero ◽  
Omar Désiga-Orenday
Keyword(s):  
Fluid Dynamics ◽  
Kinetic Energy ◽  
Energy Distribution ◽  
Turbulent Kinetic Energy ◽  
Nutrient Solution ◽  
Kinetic Energy Distribution ◽  
Solution Flow ◽  
Flow Rates ◽  
Hydroponic System ◽  
Hydroponic Systems

Hydroponics is crucial for providing feasible and economical alternatives when soils are not available for conventional farming. Scholars have raised questions regarding the ideal nutrient solution flow rate to increase the weight and height of hydroponic crops. This paper presents the turbulent kinetic energy distribution of the nutrient solution flow in a nutrient film technique (NFT) hydroponic system using the computational fluid dynamics (CFD) method. Its main objective is to determine the dynamics of nutrient solution flow. To conduct this study, a virtual NFT hydroponic system was modeled. To determine the turbulent kinetic energy distribution in the virtual NFT hydroponic system, we conducted a CFD analysis with different pipe diameters (3.5, 9.5, and 15.5 mm) and flow rates (0.75, 1.5, 3, and 6 L min−1). The simulation results indicate that different pipe diameters and flow rates in NFT hydroponic systems vary the turbulent kinetic energy distribution of nutrient solution flow around plastic mesh pots.

scholarly journals Head lettuce production and nutrition in relation to nutrient solution flow

2020 ◽  
Vol 38 (1) ◽  
pp. 21-26
Author(s):  
Cleiton Dalastra ◽  
Marcelo CM Teixeira Filho ◽  
Marcelo R da Silva ◽  
Thiago AR Nogueira ◽  
Guilherme Carlos Fernandes
Keyword(s):  
Nutrient Solution ◽  
Flow Rate ◽  
Nutrient Accumulation ◽  
Solution Flow ◽  
Flow Rates ◽  
Hydroponic System ◽  
Optimum Flow Rate ◽  
Technical Performance ◽  
Use Efficiency ◽  
Optimum Flow

ABSTRACT The optimum flow rate of nutrient solution in hydroponic system can better nourish the crops, allowing healthy and faster growth of lettuce. However, flow also interferes with electric power consumption, so further researches are necessary, mainly on the effect of flow rate, nutrient accumulation and lettuce production. In this context, the aim of this study was to evaluate nutrition and production of head lettuce in relation to the nutrient solution flow in NFT hydroponic system. The treatments consisted of nutrient solution application at the flow rates 0.5; 1; 2, and 4 liters per minute in each cultivation channel. Five replicates per treatment consisted of 15 plants each. The flow in hydroponic systems to produce head lettuce alters the technical performance of the crop. Due to the greater nutrient accumulation in shoot and use efficiency of these elements, the highest production (g/plant) of head lettuce was obtained with a flow rate of 1 L/min of the nutrient solution.

Numerical Analysis of the Influence of Different-Shaped Square Cylinders on Water Flow

2012 ◽  
Vol 614-615 ◽  
pp. 604-607
Author(s):  
Jie Gu ◽  
Xiao Li Wang ◽  
Wei Chen ◽  
Xin Qin ◽  
Dan Qing Ma ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Energy Distribution ◽  
Turbulent Kinetic Energy ◽  
Water Flow ◽  
Turbulence Kinetic Energy ◽  
Kinetic Energy Distribution ◽  
Distribution Area ◽  
Square Cylinder ◽  
Square Cylinders ◽  
Contour Lines

A 3D numerical model was performed to simulate the different cases of the water flow across different-shaped square cylinders. Figures of streamlines and turbulent kinetic energy contour lines in different cases were obtained. Through the comparison of streamlines, the areas of strong turbulent kinetic energy and the strongest turbulent kinetic energy nucleus, the results indicated that,(i) two symmetrical vortexes were formed behind the regular quadrilateral square cylinder and the “⊥”-shaped square cylinder ,respectively, and the former were bigger than the latter .While the flow crossed the “±”-shaped square cylinder without forming vortex.(ii) When water flowed around different-shaped square cylinders, from the regular quadrilateral one, the “⊥”-shaped one to the “±”-shaped one, successively, the strong turbulent kinetic energy distribution area, in which turbulence kinetic energy value was above 18,gradually increased; while the strongest turbulence kinetic energy nucleus, whose value of turbulence kinetic energy was the largest among turbulence kinetic energy nucleuses in the strong turbulent kinetic energy distribution area, moved forward gradually and its area was smaller and smaller.

Numerical Simulation on the Flow Field of External Gear Pump Based on FLUENT

2014 ◽  
Vol 556-562 ◽  
pp. 1421-1425
Author(s):  
An Fu Guo ◽  
Ting Ting Jiang ◽  
Tong Wang ◽  
Yun Ping Hu ◽  
Da Jiang Zhang
Keyword(s):  
Kinetic Energy ◽  
Energy Distribution ◽  
Turbulent Kinetic Energy ◽  
Maximum Velocity ◽  
Flow Distribution ◽  
Kinetic Energy Distribution ◽  
Gear Pump ◽  
Intermediate Point ◽  
Gear Mesh ◽  
External Gear Pump

In this paper, the software FLUENT was employed and the two-dimensional flow fields of external gear pump, such as flow distribution, velocity distribution, pressure distribution, turbulent kinetic energy distribution are obtained. The results show that the pressure of the pump presents the symmetry and the maximum static pressure is 0.127 MPa at the oil absorption cavity inlet. The maximum velocity appeared in the left side of the gear pump body reached 6.97m/s and the minimum velocity reached 1.09m/s on the two gears meshing line. Turbulence kinetic energy distribution of the pump shows the symmetry and the minimum turbulent kinetic energy appeared in the two gear mesh is 0.0312m2/s2. Meanwhile, the maximum turbulent kinetic energy reached 12.2 m2/s2 at the exit of the oil cavity. The maximum exit velocity appeared at the position of the intermediate point reached 3m/s. The results have referenced significance for design and analysis of external gear pump.

Simulation on the Flow Field of Centrifugal Pump Based on Fluent

2014 ◽  
Vol 926-930 ◽  
pp. 1743-1746
Author(s):  
An Fu Guo ◽  
Tong Wang ◽  
Ting Ting Jiang ◽  
Yun Ping Hu ◽  
Da Jiang Zhang
Keyword(s):  
Kinetic Energy ◽  
Flow Field ◽  
Energy Distribution ◽  
Turbulent Kinetic Energy ◽  
Centrifugal Pump ◽  
Flow Distribution ◽  
Kinetic Energy Distribution ◽  
Two Dimensional ◽  
Two Dimensional Flow ◽  
Velocity Pressure

In this paper, the software Fluent was employed and the two-dimensional flow fields, such as flow distribution, velocity distribution, pressure distribution, turbulent kinetic energy distribution are obtained. The results show that the flow, velocity, pressure and turbulent kinetic energy distribution are significantly different and asymmetric. The results have referenced significance for design and analysis of the Centrifugal Pump.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

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