Computational fluid dynamics analysis for improving temperature distribution in a chili dryer

Escobedo, José Luís Carrera; Rivera, Arquímedes Ortiz; Valdivia, César Humberto Guzmán; Ruiz, Mario; Orenday, Omar Desiga

doi:10.2298/tsci160112255e

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…Establishing grid convergence is crucial in any numerical study [50]. The results must be guaranteed using a grid convergence study, which is essential to verify that the governing equations are being solved correctly and that the solution is not sensitive to the grid resolution.…”

Section: Resultsmentioning

confidence: 99%

Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control

Guzmán

Carrera²,

Durán³

et al. 2018

Sensors

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Virtual sensing is crucial in order to provide feasible and economical alternatives when physical measuring instruments are not available. Developing model-based virtual sensors to calculate real-time information at each targeted location is a complex endeavor in terms of sensing technology. This paper proposes a new approach for model-based virtual sensor development using computational fluid dynamics (CFD) and control. Its main objective is to develop a three-dimensional (3D) real-time simulator using virtual sensors to monitor the temperature in a greenhouse. To conduct this study, a small-scale greenhouse was designed, modeled, and fabricated. The controller was based on the convection heat transfer equation under specific assumptions and conditions. To determine the temperature distribution in the greenhouse, a CFD analysis was conducted. Only one well-calibrated and controlled physical sensor (temperature reference) was enough for the CFD analysis. After processing the result that was obtained from the real sensor output, each virtual sensor had learned the associative transfer function that estimated the output from given input data, resulting in a 3D real-time simulator. This study has demonstrated, for the first time, that CFD analysis and a control strategy can be combined to obtain system models for monitoring the temperature in greenhouses. These findings suggest that, generally, virtual sensing can be applied in large greenhouses for monitoring the temperature using a 3D real-time simulator.

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Section: Resultsmentioning

confidence: 99%

Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control

Guzmán

Carrera²,

Durán³

et al. 2018

Sensors

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“…AgriEngineering 2019, 1, 21 284 no explanation of why some flow rates improved the lettuce growth in terms of computing technology. Computational fluid dynamics (CFD) is progressively becoming a design-oriented tool in many engineering fields to solve and predict engineering problems that involve liquids and gases [7]. CFD has several attractive features: (a) easy variation of flow parameters, (b) fast changes to the geometry, and (c) detailed insight into flow behavior.…”

Section: Modeling the Virtual Nft Hydroponic Systemmentioning

confidence: 99%

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

2019

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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.

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“…The SIMPLE algorithm is used for pressure-velocity coupling, and the first order upwind scheme is used to discretize the convective terms in the momentum, energy, and turbulence equations. Regarding the initialization of the simulation, it is based on the average value of pressure, velocity (x, y, z), Turbulent Kinetic Energy, Turbulent Dissipation Ratio, and temperature in both streams: 8.7×10 5 Pa, (0, 0, -0.58) m/s, 0.00164 m 2 /s 2 , 0.0003 m 2 /s 3 , and 323.37 K, respectively. The residual values are (1×10 -6 , Authors: Román, R.N.I.…”

Section: Numerical Simulation Proceduresmentioning

confidence: 99%

“…The solution of this problem could be possible with the use of a device capable of recirculating the air inside the greenhouse. The search for this improvement can be carried out by means of a computational tool (CFD) that allows reducing the time of analysis, construction and economic expense [5,6]. The present work consists in the analysis and numerical simulation of a series of fan configurations inside a mixed greenhouse solar dryer, to increase and standardize the velocity and temperature of the air in the drying zones (location trays: 1m high and 0.5 m in front of the left and right wall on each side, table dimensions: 1 m wide, 0.35 m high and 4 m long).…”

Section: Introductionmentioning

confidence: 99%

Improvement of air distribution inside the greenhouse solar dryer using Computational Fluid Dynamics

Yudonago¹,

López‐Ortiz²,

Rodrı́guez³

et al.

Proceedings of the 22nd International Drying Symposium on Drying Technology - IDS '22

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In this work, a series of configurations of fans inside a greenhouse solar dryer (GSD) is analyzed and simulated, in order to increase the air velocity and temperature. The simulation was carried out in steady-state using CFD Ansys Fluent commercial codes 19.0. The results showed that the use of the fan inside the greenhouse can improve the distribution of air, since in the optimal design, a maximum velocity of 3.3 m/s and a temperature of 318 K were reached compared to the greenhouse without recirculation, where the maximum air velocity and temperature were 0.6 m/s and 316 K, respectively.

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Computational fluid dynamics analysis for improving temperature distribution in a chili dryer

Cited by 8 publications

References 36 publications

Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control

Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control

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

Improvement of air distribution inside the greenhouse solar dryer using Computational Fluid Dynamics

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